Reflection essay
zhuzhuY
10.11.12Readings.docx
282 CHAPTER 9 Social Computing
Information Systems within the Organization
CHAPTER OUTLINE LEARNING OBJECTIVES
10.1 Transaction Processing Systems 10.1 Explain the purpose of transaction processing systems.
10.2 Functional Area Information Systems 10.2 Explain the types of support that information systems can provide
for each functional area of the organization.
10.3 Enterprise Resource Planning (ERP) 10.3 Identify advantages and drawbacks to businesses implementing an Systems enterprise resource planning system.
10.4 ERP Support for Business Processes 10.4 Describe the three main business processes supported by ERP systems.
Munchery
“What’s for dinner?” is a question everyone asks on a daily basis. For people who live and work in large metropolitan ar eas, the answer might become lost in the rush to and from work, family and personal commitments, and a desire to have something tasty (and perhaps healthy) at the end of the day. But, who has time to prepare the meal?
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In 2010, Tri Tran, founder of Munchery ( www.munchery.com ), noticed that his neighbor had an interesting answer to the “What’s for dinner?” question. For $700–$800 a week, a personal chef came to his neighbor’s home to prepare and refrigerate meals that would be ready to warm up on demand and enjoy. Realizing that this model was not financially viable to him or many other people, Tran decided to start a new company. His goal was to provide high-quality, chef-prepared, fresh, almost-ready-to-eat meals, delivered right to the customer’s door, for $7–$8 per entrée.
Meal delivery is not a new concept. Many companies are in the business of delivering hot, ready-to-eat meals. For example, Domi nos ( www.dominos.com ) delivers more than 1 million pizzas per day worldwide, and Sprig ( www.sprig.com ) focuses on healthy, organic
meals. However, as hot meals cool, the quality of the meal experience decreases. Hot meals have a very narrow window of time for a quality consumption experience, which puts pressure on the delivery system. In 1993, this delivery pressure and a desire for quality led Dominos to discontinue their 30-minute delivery promise in lieu of a “Total Satis faction Guarantee.”
Almost-ready-to-eat meals are commonly sold at the grocery store, but they require some preparation before they can be consumed. When Munchery entered the meal delivery market, the firm differentiated itself by preparing an entire meal, then chilling the prepared items for a cold delivery. Delivery of the almost-ready-to-eat chilled food (with proper warming instructions) decreased the time pressure on the delivery, al lowing the delivery team member to carry more meals on an optimized delivery route. This business model fueled Munchery’s rapid growth.
Munchery also leveraged its business model to reduce food costs. Whereas a typical restaurant spends one-third of its revenue on food supplies, Munchery spends much less. For example, in San Fran cisco, most restaurants pay $9–$11 per pound for salmon. In contrast, Munchery has successfully negotiated with its suppliers to pay only $6. Munchery also employs technology to create very efficient processes in the kitchen.
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Customers can use the mobile app or the web to learn more about their current menu options. They can virtually “meet” the chefs who prepare their meals, learn about the origin of the ingredients of their food, place orders, and select a delivery window. Munchery’s proprietary software analyses all customer transactions to optimize the delivery schedule to ensure that customers receive their orders within their desired time window. Customers can also track their order through the entire preparation and delivery to their door.
Munchery is disrupting the restaurant industry by using technol ogy in two ways. First, the company offers its customers a novel dining experience. Customers are able to enjoy chef-prepared meals at home, with minimal effort and planning, at a fraction of the cost of visiting a restaurant. Second, Munchery is able to provide a menu that changes daily, thus enabling it to feature fresher, healthier food.
In April 2016, Munchery changed its pricing model. New custom ers had to pay a monthly membership fee of $8.95 or an annual fee of
$85. The fee allows customers to receive discounts of up to 20 percent on each meal. Customers can try the service for the first month for free and then cancel at any time. Munchery adopted this model to stabilize its revenue stream.
Munchery last raised $85 million from venture capitalists in 2015. As a result, as of November 2016 the company was valued between
$250 million to $275 million.
Although Munchery has not reached its target price point of $7 to
$8 per entrée, the company is moving in that direction thanks to its use
of information technology in its operations and along its supply chain. When Munchery launched in 2011, most entrées were priced at $25. By the end of 2016, a combination of increased demand and greater economies of scale had brought prices down to $10 to $12 per entrée. At this price point, the next time you ask, “What’s for dinner?” your an swer just might be Munchery—the successful restaurant that delivers cold food to your door.
Sources: Compiled from E. Newcomer, “Munchery’s Struggles Show How Hard the Food Delivery Business Is,” Bloomberg.com , November 21, 2016; E. Newcomer, “Money-Losing Meal Delivery Startup Munchery Seeks New CEO,” Bloomberg.com , October 12, 2016; K. Kokalitcheva, “Munchery Cautiously Expands to Serving Lunch with This New Program,” Fortune, October 6, 2016; K. Kokalitcheva, “Munchery Joins Other Uber-Like Startups as They Search for Sane Business Models,” Fortune, April 19, 2016; L. Tryson, “How Technology Is Changing, Challenging the Food Industry,” Food Quality & Safety, November 12, 2015; J. Broughton, “Munchery’s Recipe for Winning Online Food Delivery,” Inc., June 11, 2015; S. Pishevar, “How Munchery
Is Literally Eating the World,” www.medium.com, June 8, 2015; T. Lien, “Munchery Raises $85 Million in Bid to Make Healthy Meals Accessible to All,” Los Angeles Times, May 26, 2015; D. MacMillan, “Munchery Valued at About $300 Million amid Food Fight,” Wall Street Journal, Digits, May 22, 2015; F. Elliot, “New Food Delivery App Munchery Launches Chef-Driven Meals on the Westside Today,” Los Angeles Eater, May 18, 2015; M. Kosoff, “Food Startup Munchery Has Hired a Bunch of Gourmet Chefs to Answer the Most Popular Question in Your Household,” Business Insider, April 8, 2015; A. Wilhelm, “Munchery Rebuilds Mobile Apps, Hires 3-Star Chef, and Gets into Booze,” TechCrunch, October 29, 2013; www.restaurant.org/News-Research/ Research/Operations-Report, accessed October 14, 2015; www.munchery. com; www.sprig.com; www.spoonrocket.com; www.dominos.com, accessed December 14, 2016.
Questions
1. How does Munchery’s operation benefit from the simple decision to offer chilled rather than hot meals?
2. What role do information systems play in Munchery’s business model?
Introduction
The opening case illustrates the integral part that information systems (IS) play in an organiza tion’s success. As you noted in the case, without IS Munchery could not exist. IS are everywhere, and they affect organizations in countless ways. Although IS are frequently discussed within the context of large organizational settings, the chapter opening case illustrates how IS play a critical role in small organizations.
It is important to note that “systems within organizations” do not have to be owned by the organization itself. Instead, organizations can deploy very productive IS that are owned by an external vendor. The key point here is that “systems within an organization” are intended to support internal processes, regardless of who actually owns the systems.
It is important for you to have a working knowledge of IS within your organization for a variety of reasons. First, your job will require you to access corporate data that are supplied primarily by your firm’s transaction processing systems and enterprise resource planning sys tems. Second, you will have a great deal of input into the format and content of the reports that you receive from these systems. Third, you will use the information contained in these reports to perform your job more productively.
This chapter will teach you about the various information systems that modern organi zations use. We begin by considering transaction processing systems, the most fundamental organizational information systems. We continue with the functional area management infor mation systems, and we conclude with enterprise resource planning systems.
Transaction Processing Systems
10.1
Transaction Processing Systems 285
Millions (sometimes billions) of transactions occur in large organizations every day. A transac tion is any business event that generates data worthy of being captured and stored in a data base. Examples of transactions are a product manufactured, a service sold, a person hired, and a payroll check generated. In another example, when you are checking out of Walmart, each time the cashier swipes an item across the bar code reader is one transaction.
A transaction processing system (TPS) supports the monitoring, collection, storage, and processing of data from the organization’s basic business transactions, each of which generates data. The TPS collects data continuously, typically in real time—that is, as soon as the data are generated—and it provides the input data for the corporate databases. The TPSs are critical to the success of any enterprise because they support core operations.
In the modern business world, TPSs are inputs for the functional area information systems and business intelligence systems, as well as business operations such as customer relation ship management, knowledge management, and e-commerce. TPSs have to efficiently handle both high volumes of data and large variations in those volumes (e.g., during periods of peak processing). They must also avoid errors and downtime, record results accurately and securely, and maintain privacy and security. Figure 10.1 illustrates how TPSs manage data. Consider these examples of how TPSs handle the complexities of transactional data:
· When more than one person or application program can access the database at the same time, the database has to be protected from errors resulting from overlapping updates. The most common error is losing the results of one of the updates.
· When processing a transaction involves more than one computer, the database and all users must be protected against inconsistencies arising from a failure of any component at any time. For example, an error that occurs at some point in an ATM withdrawal can enable a customer to receive cash, although the bank’s computer indicates that he or she did not. (Conversely, a customer might not receive cash, although the bank’s computer indicates that he or she did.)
· It must be possible to reverse a transaction in its entirety if it turns out to have been entered in error. It is also necessary to reverse a transaction when a customer returns a purchased item. For example, if you return a sweater that you have purchased, then the store must credit your credit card for the amount of the purchase, refund your cash, or offer you an in-store credit to purchase another item. The store must also update its inventory.
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· It is frequently important to preserve an audit trail. In fact, for certain transactions an audit trail may be legally required.
These and similar issues explain why organizations spend millions of dollars on expensive mainframe computers. In today’s business environment, firms must have the dependability, reliability, and processing capacity of these computers to handle their transaction processing loads.
Regardless of the specific data processed by a TPS, the actual process tends to be stan dard, whether it occurs in a manufacturing firm, a service firm, or a government organization. As the first step in this procedure, people or sensors collect data, which are entered into the
FIGURE 10.1 How transaction processing systems manage data.
computer through any input device. Generally speaking, organizations try to automate the TPS data entry as much as possible because of the large volume involved, a process called source data automation (discussed in Technology Guide 1).
Next, the system processes data in one of two basic ways: batch processing and online processing. In batch processing, the firm collects data from transactions as they occur, placing them in groups, or batches. The system then prepares and processes the batches periodically (say, every night).
In online transaction processing (OLTP), business transactions are processed online as soon as they occur. For example, when you pay for an item at a store, the system records the sale by reducing the inventory on hand by one unit, increasing sales figures for the item by one unit, and increasing the store’s cash position by the amount you paid. The system performs these tasks in real time by means of online technologies.
Before you go on. . .
1. Define TPS.
2. List the key functions of a TPS.
Functional Area Information Systems
10.2
Each department or functional area within an organization has its own collection of application programs, or information systems. Each of these functional area information systems (FAIS) supports a particular functional area in the organization by increasing each area’s internal effi ciency and effectiveness. FAISs often convey information in a variety of reports, which you will see later in this chapter. Examples of FAISs are accounting IS, finance IS, production/operations management (POM) IS, marketing IS, and human resources IS.
As illustrated in Figure 10.1, the FAIS access data from the corporate databases. The follow ing sections discuss the support that FAISs provide for these functional areas.
Information Systems for Accounting and Finance
A primary mission of the accounting and finance functional areas is to manage money flows into, within, and out of organizations. This mission is very broad because money is involved in all organizational functions. Therefore, accounting and finance information systems are very diverse and comprehensive. In this section, you focus on certain selected activities of the accounting and finance functional area.
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Financial Planning and Budgeting. Appropriate management of financial assets is a major task in financial planning and budgeting. Managers must plan for both acquiring and using resources. For example:
· Financial and economic forecasting: Knowledge about the availability and cost of money is a key ingredient for successful financial planning. Cash flow projections are particu larly important because they inform organizations what funds they need, when they need them, and how they will acquire them.
Funds for operating organizations come from multiple sources, including stock holders’ investments, bond sales, bank loans, sales of products and services, and income from investments. Decisions concerning funding for ongoing operations and for capital investment can be supported by decision support systems and business intelligence ap plications (discussed in Chapter 12), as well as expert systems (discussed in Technology
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Guide 4). Numerous software packages for conducting economic and financial forecasting are also available. Many of these packages can be downloaded from the Internet, some of them for free.
· Budgeting: An essential component of the accounting and finance function is the annual budget, which allocates the organization’s financial resources among participants and activities. The budget allows management to distribute resources in the way that best supports the organization’s mission and goals.
Several software packages are available to support budget preparation and control and to facilitate communication among participants in the budget process. These pack ages can reduce the time involved in the budget process. Furthermore, they can automat ically monitor exceptions for patterns and trends.
Managing Financial Transactions. Many accounting and finance software packages are integrated with other functional areas. For example, Peachtree by Sage ( www
.peachtree.com ) offers a sales ledger, a purchase ledger, a cash book, sales order processing, invoicing, stock control, a fixed assets register, and more.
Companies involved in electronic commerce need to access customers’ financial data (e.g., credit line), inventory levels, and manufacturing databases (to determine available ca pacity and place orders). For example, Microsoft Dynamics GP (formerly Great Plains Software) offers 50 modules that meet the most common financial, project, distribution, manufacturing, and e-business needs.
Organizations, business processes, and business activities operate with, and manage, financial transactions. Consider these examples:
· Global stock exchanges: Financial markets operate in global, 24/7/365, distributed elec tronic stock exchanges that use the Internet both to buy and sell stocks and to broadcast real-time stock prices.
· Managing multiple currencies: Global trade involves financial transactions that are carried out in different currencies. The conversion ratios of these currencies are constantly in flux. Financial and accounting systems use financial data from different countries, and they con vert the currencies from and to any other currency in seconds. Reports based on these data, which formerly required several days to generate, can now be produced in only seconds. In addition to currency conversions, these systems manage multiple languages as well.
· Virtual close: Companies traditionally closed their books (accounting records) quarterly, usually to meet regulatory requirements. Today, many companies want to be able to close their books at any time, on very short notice. Information systems make it possible to close the books quickly in what is called a virtual close. This process provides almost real-time information on the organization’s financial health.
· Expense management automation: Expense management automation (EMA) refers to sys tems that automate the data entry and processing of travel and entertainment expenses. EMA systems are web-based applications that enable companies to quickly and consistently collect expense information, enforce company policies and contracts, and reduce unplanned purchases as well as airline and hotel expenses. They also allow companies to reimburse their employees more quickly because expense approvals are not delayed by poor documentation.
Investment Management. Organizations invest large amounts of money in stocks, bonds, real estate, and other assets. Managing these investments is a complex task, for sev eral reasons. First, organizations have literally thousands of investment alternatives dispersed throughout the world to choose from. These investments are also subject to complex regula tions and tax laws, which vary from one location to another.
Investment decisions require managers to evaluate financial and economic reports pro vided by diverse institutions, including federal and state agencies, universities, research insti tutions, and financial services firms. Thousands of websites also provide financial data, many of them for free.
Control and Auditing. One major reason why organizations go out of business is their inability to forecast or secure a sufficient cash flow. Underestimating expenses, overspending, engaging in fraud, and mismanaging financial statements can lead to disaster. Consequently, it is essential that organizations effectively control their finances and financial statements. Let’s examine some of the most common forms of financial control:
· Budgetary control: After an organization has finalized its annual budget, it divides those monies into monthly allocations. Managers at various levels monitor departmental expen ditures and compare them against the budget and the operational progress of corporate plans.
· Auditing: Auditing has two basic purposes: (1) to monitor how the organization’s monies are being spent and (2) to assess the organization’s financial health. Internal audits are performed by the organization’s accounting and finance personnel. These employees also prepare for periodic external audits by outside CPA firms.
· Financial ratio analysis: Another major accounting and finance function is to monitor the company’s financial health by assessing a set of financial ratios, including liquidity ratios (the availability of cash to pay debt), activity ratios (how quickly a firm converts noncash assets to cash assets), debt ratios (measure the firm’s ability to repay long-term debt), and profitability ratios (measure the firm’s use of its assets and control of its expenses to gen erate an acceptable rate of return).
Information Systems for Marketing
It is impossible to overestimate the importance of customers to any organization. Therefore, any successful organization must understand its customers’ needs and wants and then develop its marketing and advertising strategies around them. Information systems pro vide numerous types of support to the marketing function. Customer-centric organizations are so important that we cover this topic in detail in Chapter 11.
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Information Systems for Production/Operations Management
The production/operations management (POM) function in an organization is responsible for the processes that transform inputs into useful outputs as well as for the overall operation of the busi ness. The POM function is responsible for managing the organization’s supply chain. Because supply chain management is vital to the success of modern organizations, we address this topic in detail in Chapter 11. Because of the breadth and variety of POM functions, we discuss only six here: in-house logistics and materials management, inventory management, quality control, planning production and operation, computer-integrated manufacturing (CIM), and product lifecycle management (PLM).
POM
In-House Logistics and Materials Management. Logistics management deals with ordering, purchasing, inbound logistics (receiving), and outbound logistics (shipping) activities. Related activities include inventory management and quality control.
Inventory Management. As the name suggests, inventory management determines how much inventory an organization should maintain. Both excessive inventory and insufficient inventory create problems. Overstocking can be expensive because of storage costs and the costs of spoilage and obsolescence. However, keeping insufficient inventory is also expensive because of last-minute orders and lost sales.
Operations personnel make two basic decisions: when to order and how much to order. Inventory models, such as the economic order quantity (EOQ) model, support these decisions. A large number of commercial inventory software packages are available that automate the application of these models.
Many large companies allow their suppliers to monitor their inventory levels and ship products as they are needed. This strategy, called vendor-managed inventory (VMI), eliminates the need for the company to submit purchasing orders. We discuss VMI in Chapter 11.
Quality Control. Quality control systems used by manufacturing units provide infor mation about the quality of incoming material and parts, as well as the quality of in-process semifinished and finished products. These systems record the results of all inspections and then compare these results with established metrics. They also generate periodic reports that contain information about quality—for example, the percentage of products that con tain defects or that need to be reworked. Quality control data, collected by web-based sen sors, can be interpreted in real time. Alternatively, they can be stored in a database for future analysis.
Planning Production and Operations. In many firms, POM planning is sup ported by IT. POM planning has evolved from material requirements planning (MRP) to manu facturing resource planning (MRP II), to enterprise resource planning (ERP). We briefly discuss MRP and MRP II here, and we examine ERP in detail later in this chapter.
Inventory systems that use an EOQ approach are designed for items for which demand is completely independent—for example, the number of identical personal computers a com puter manufacturer will sell. In manufacturing operations, however, the demand for some items is interdependent. Consider, for example, a company that makes three types of chairs, all of which use the same screws and bolts. In this case, the demand for screws and bolts depends on the total demand for all three types of chairs and their shipment schedules. The planning process that integrates production, purchasing, and inventory management of interdependent items is called material requirements planning (MRP).
MRP deals only with production scheduling and inventories. More complex planning also involves allocating related resources, such as money and labor. For these cases, more complex, integrated software, called manufacturing resource planning (MRP II), is available. MRP II inte grates a firm’s production, inventory management, purchasing, financing, and labor activities. Thus, MRP II adds functions to a regular MRP system. In fact, MRP II has evolved into enterprise resource planning (ERP).
Computer-Integrated Manufacturing. Computer-integrated manufacturing (CIM) (also called digital manufacturing) is an approach that integrates various automated factory systems. CIM has three basic goals: (1) to simplify all manufacturing technologies and techniques, (2) to automate as many of the manufacturing processes as possible, and (3) to in tegrate and coordinate all aspects of design, manufacturing, and related functions
through computer systems.
Product Life Cycle Management. Even within a single organiza tion, designing and developing new products can be expensive and time con suming. When multiple organizations are involved, the process can become very complex. Product life cycle management (PLM) is a business strategy that en ables manufacturers to share product-related data that support product design and development and supply chain operations. PLM applies web-based collab orative technologies to product development. By integrating formerly disparate functions, such as a manufacturing process and the logistics that support it, PLM enables these functions to collaborate, essentially forming a single team that manages the product from its inception through its completion, as shown in Figure 10.2.
FIGURE 10.2 Product life cycle.
Information Systems for Human Resource Management
HRM
Initial human resource information system (HRIS) applications dealt primarily with transaction processing systems such as managing benefits and keeping records of vacation days. As organi zational systems have moved to intranets and the web, so have HRIS applications.
Many HRIS applications are delivered through an HR portal. (See our discussion of LinkedIn in Chapter 9.) For example, numerous organizations use their web portals to advertise job openings and to conduct online hiring and training. In this section, you consider how or ganizations are using IT to perform some key HR functions: recruitment, HR maintenance and development, and HR planning and management.
Recruitment. Recruitment involves finding potential employees, evaluating them, and deciding which ones to hire. Some companies are flooded with viable applicants; others have difficulty finding the right people. IT can be helpful in both cases. IT can also assist in related activities such as testing and screening job applicants.
With millions of resumes available online (in particular, LinkedIn), it is not surprising that companies are trying to find appropriate candidates on the web, usually with the help of spe cialized search engines. Companies also advertise hundreds of thousands of jobs on the web. Online recruiting can reach more candidates, which may bring in better applicants. The costs of online recruitment are also usually lower than traditional recruiting methods such as adver tising in newspapers or in trade journals.
Human Resources Development. After employees are recruited, they become part of the corporate human resources pool, which means they must be evaluated and devel oped. IT provides support for these activities.
Most employees are periodically evaluated by their immediate supervisors. In some or ganizations, peers or subordinates also evaluate other employees. Evaluations are typically digitized, and they are used to support many decisions, ranging from rewards to transfers to layoffs.
IT also plays an important role in training and retraining. Some of the most innovative developments are taking place in the areas of intelligent computer-aided instruction and the application of multimedia support for instructional activities. For example, companies conduct much of their corporate training over their intranet or on the web.
Human Resources Planning and Management. Managing human resources in large organizations requires extensive planning and detailed strategy. IT support is particu larly valuable in the following three areas:
1. Payroll and employees’ records: The HR department is responsible for payroll preparation. This process is typically automated, meaning that paychecks are printed or money is trans ferred electronically into employees’ bank accounts.
2. Benefits administration: In return for their work contributions to their organizations, em ployees receive wages, bonuses, and various benefits. These benefits include healthcare and dental care, pension contributions (in a decreasing number of organizations), 401(k) contributions, wellness centers, and child care centers.
Managing benefits is a complex task because multiple options are available and or ganizations typically allow employees to choose and trade off their benefits. In many organizations, employees can access the company portal to self-register for specific benefits.
3. Employee relationship management: In their efforts to better manage employees, compa nies are developing employee relationship management (ERM) applications, for example, a call center for employees’ to discuss problems.
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TABLE 10.1 |
Activities Supported by Functional Area Information Systems |
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Accounting and Finance Financial planning and cost of money Budgeting—allocates financial resources among participants and activities Capital budgeting—financing of asset acquisitions Managing financial transactions Handling multiple currencies Virtual close—the ability to close the books at any time on short notice Investment management—managing organizational investments in stocks, bonds, real estate, and other investment vehicles Budgetary control—monitoring expenditures and comparing them against the budget Auditing—ensuring the accuracy of the organization’s financial transactions and assessing the condition of the organization’s financial health Payroll |
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Marketing and Sales Customer relations—knowing who customers are and treating them appropriately Customer profiles and preferences Salesforce automation—using software to automate the business tasks of sales, thereby improving the productivity of salespeople |
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Production/Operations and Logistics Inventory management—when to order new inventory, how much inventory to order, and how much inventory to keep in stock Quality control—controlling for defects in incoming materials and goods produced Materials requirements planning—planning process that integrates production, purchasing, and inventory management of interdependent items (MRP) Manufacturing resource planning—planning process that integrates an enterprise’s production, inventory management, purchasing, financing, and labor activities (MRP II) Just-in-time systems—a principle of production and inventory control in which materials and parts arrive precisely when and where needed for production (JIT) Computer-integrated manufacturing—a manufacturing approach that integrates several computerized systems, such as computer-assisted design (CAD), computer-assisted manufacturing (CAM), MRP, and JIT Product life cycle management—business strategy that enables manufacturers to collaborate on product design and development efforts, using the web |
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Human Resource Management Recruitment—finding employees, testing them, and deciding which ones to hire Performance evaluation—periodic evaluation by superiors Training Employee records Benefits administration—retirement, disability, unemployment, and so on |
Table 10.1 provides an overview of the activities that the FAIS support. Figure 10.3 shows many of the information systems that support these five functional areas.
Reports
All information systems produce reports: transaction processing systems, functional area in formation systems, ERP systems, customer relationship management systems, business intel ligence systems, and so on. We discuss reports here because they are so closely associated
FIGURE 10.3 Examples of information systems supporting the functional areas.
with FAIS and ERP systems. These reports generally fall into three categories: routine, ad hoc (on-demand), and exception.
Routine reports are produced at scheduled intervals. They range from hourly quality control reports to daily reports on absenteeism rates. Although routine reports are extremely valuable to an organization, managers frequently need special information that is not in cluded in these reports. At other times, they need the information that is normally included in routine reports, but at different times (“I need the report today, for the last three days, not for one week”).
Such out-of-the-routine reports are called ad hoc (on-demand) reports. Ad hoc reports can also include requests for the following types of information:
· Drill-down reports display a greater level of detail. For example, a manager might exam ine sales by region and decide to “drill down” by focusing specifically on sales by store and then by salesperson.
· Key indicator reports summarize the performance of critical activities. For example, a chief financial officer might want to monitor cash flow and cash on hand.
· Comparative reports compare, for example, the performances of different business units or of a single unit during different times.
Some managers prefer exception reports. Exception reports include only informa tion that falls outside certain threshold standards. To implement management by exception, management first establishes performance standards. The company then creates systems to monitor performance (through the incoming data about business transactions such as expen ditures), to compare actual performance to the standards, and to identify exceptions to the standards. The system alerts managers to the exceptions through exception reports.
Let’s use sales as an example. First, management establishes sales quotas. The company then implements a FAIS that collects and analyzes all of the sales data. An exception report would identify only those cases in which sales fell outside an established threshold—for exam ple, more than 20 percent short of the quota. It would not report expenditures that fell within the accepted range of standards. By leaving out all “acceptable” performances, exception re ports save managers time, thus helping them focus on problem areas.
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Before you go on. . .
1. Define a functional area information system and list its major characteristics.
2. How do information systems benefit the finance and accounting functional area?
3. Explain how POM personnel use information systems to perform their jobs more effectively and efficiently.
4. What are the most important HRIS applications?
5. Compare and contrast the three basic types of reports.
Enterprise Resource Planning Systems
10.3
Historically, the functional area information systems were developed independent of one an other, resulting in information silos. These silos did not communicate well with one another, and this lack of communication and integration made organizations less efficient. This ineffi ciency was particularly evident in business processes that involved more than one functional area, such as procurement and fulfillment.
Enterprise resource planning (ERP) systems are designed to correct a lack of commu nication among the functional area IS. ERP systems resolve this problem by tightly integrating the functional area IS through a common database. For this reason, experts credit ERP systems with greatly increasing organizational productivity. ERP systems adopt a business process view of the overall organization to integrate the planning, management, and use of all of an organi zation’s resources, employing a common software platform and database.
The major objectives of ERP systems are to tightly integrate the functional areas of the or ganization and to enable information to flow seamlessly across them. Tight integration means that changes in one functional area are immediately reflected in all other pertinent functional areas. In essence, ERP systems provide the information necessary to control the business pro cesses of the organization.
It is important to understand that ERP systems are an evolution of FAIS. That is, ERP systems have much the same functionality as FAIS, and they produce the same reports. As you see in IT’s About Business 10.1, ERP systems simply integrate the functions of the indi vidual FAIS.
Although some companies have developed their own ERP systems, most organizations use commercially available ERP software. The leading ERP software vendor is SAP ( www
.sap.com ). Other major vendors include Oracle ( www.oracle.com ) and PeopleSoft ( www
.peoplesoft.com ), now an Oracle company. (With more than 700 customers, PeopleSoft is the market leader in higher education). For up-to-date information on ERP software, visit http:// erp.ittoolbox.com .
ERP II Systems
ERP systems were originally deployed to facilitate business processes associated with manu facturing, such as raw materials management, inventory control, order entry, and distribution. However, these early ERP systems did not extend to other functional areas, such as sales and marketing. They also did not include any customer relationship management (CRM) capabili ties that enable organizations to capture customer-specific information. Finally, they did not provide web-enabled customer service or order fulfillment.
Over time, ERP systems evolved to include administrative, sales, marketing, and human resources processes. Companies now employ an enterprisewide approach to ERP that uses the web and connects all facets of the value chain. (You might want to review our discussion of value chains in Chapter 2.) These systems are called ERP II.
IT’s About Business 10.1
The Army Transitions from Legacy Systems to Enterprise Resource Planning Tools
MIS ACCT
Almost every organization that is more than 20 years old has some type of legacy information system—an outdated IT system that was usually developed with a specific focus and designed without con sidering future needs. In essence, legacy systems were developed as silos. (Silos refer to functional area information systems that do not communicate with other FAIS or organizational information systems.) Furthermore, each legacy system was typically created with its own data in a specific format. Unfortunately, these formats are often incompatible within the same organization. In these sce narios, one legacy system cannot use the data from other systems. The U.S. Army ( www.army.mil ) is one of the oldest national organizations in our country. Information technology plays a criti cal role in all of the Army’s processes. It is no surprise that the Army has numerous legacy information systems that, while successfully supporting various operations over the years, nonetheless do not
share data efficiently or effectively.
Today, however, the Army must pass audits as part of a fed erally mandated Department of Defense ( www.dod.gov ; DOD) fi nancial audit scheduled for 2017. The Army’s legacy systems, while proficient within their specific areas, could not produce the docu mentation required by the DOD.
Many organizations, when they retire their separate legacy systems, merge the relevant functions into one enterprise resource planning (ERP) system. The migration to an ERP is very difficult, however, often taking several years and costing large amounts of money. Large organizations such as the Army cannot simply turn off one or more legacy systems until they have assurance that the new ERP system will work and that employees are trained on how to use the new system.
Other branches of the military have transitioned to ERP sys tems. Some of the implementations failed, and some succeeded. In 2012, for example, the Air Force canceled an ERP system implemen tation (the Expeditionary Combat Support System) after spending
more than $1 billion on the project. In that same year, a Navy ERP system was deemed a “qualified” success because it did work, but it was one-third over budget and more than two years late.
In 2005, the Army began working with Accenture ( www
.accenture.com ) to develop a SAP-based ( www.sap.com ) ERP sys tem called the General Fund Enterprise Business System (GFEBS). By 2012 (seven years later!), the Army had begun phasing in the new system. As of late 2016, the Army was still phasing out its legacy systems, some of which had been in use for more than four decades. The ERP rollout is scheduled to be completed in 2018.
The GFEBS is designed to create paper trails so that the Ar my’s business operations are accountable and auditable. It is a web-based tool, meaning it is accessible anywhere with Internet access. The GFEBS standardizes, streamlines, and shares critical data across all Army divisions. This will help decision makers ana lyze business processes, cost structures, and inventories. It will also help implement the DOD’s greater transparency initiative. As of late 2016, the Army had been subjected to several audits by indepen dent auditing firms, which found no major causes for concern. This is a sign that the implementation is moving the right direction.
Sources: Compiled from M. Hoffman, “Army Taps 10 Small Businesses for
$846M ERP Services Contract,” Govcon Wire, January 8, 2016; H. Kenyon, “Army ERP System Enables Financial Transparency,” InformationWeek, April 15, 2014; H. Kenyon, “DOD Pushes Toward Joint Information Environment,” InformationWeek, March 26, 2014; D. Perera, “ERP Implementation Con tinues to Challenge the Military,” fiercegovermentit.com , March 31, 2013;
D. Perera, “Air Force Cancels ECSS,” fiercegovernmentit.com , November 29, 2012; D. Perera, “Navy ERP a ‘Qualified Success,’” fiercegovernmentit. com, September 9, 2012; T. Weiss, “Accenture wins $537M Army Financial
Services Contract,” Computerworld, June 28, 2005; www.army.mil, accessed November 21, 2016.
Questions
1. Why do you think the U.S. Army’s legacy systems were not as useful today as they were when they were developed?
2. How does this military example parallel large businesses like Sears and Walmart that have had to maintain their own legacy systems?
ERP II systems are interorganizational ERP systems that provide web-enabled links among a company’s key business systems—such as inventory and production—and its cus tomers, suppliers, distributors, and other relevant parties. These links integrate internal-facing ERP applications with the external-focused applications of supply chain management and cus tomer relationship management. Figure 10.4 illustrates the organization and functions of an ERP II system.
The various functions of ERP II systems are now delivered as e-business suites. The ma jor ERP vendors have developed modular, web-enabled software suites that integrate ERP, customer relationship management, supply chain management, procurement, decision sup port, enterprise portals, and other business applications and functions. Examples are Oracle’s e-Business Suite and SAP’s mySAP. The goal of these systems is to enable companies to execute most of their business processes using a single web-enabled system of integrated software rather than a variety of separate e-business applications.
ERP II systems include a variety of modules that are divided into core ERP modules— financial management, operations management, and human resource management—and extended ERP modules—customer relationship management, supply chain management,
FIGURE 10.4 ERP II system.
business intelligence, and e-business. If a system does not have the core ERP modules, then it is not a legitimate ERP system. The extended ERP modules, in contrast, are optional. Table 10.2 describes each of these modules.
Benefits and Limitation of ERP Systems
ERP systems can generate significant business benefits for an organization. The major benefits fall into the following three categories:
1. Organizational flexibility and agility: As you have seen, ERP systems break down many for mer departmental and functional silos of business processes, information systems, and information resources. In this way, they make organizations more flexible, agile, and adap tive. The organizations can therefore respond quickly to changing business conditions and capitalize on new business opportunities.
2. Decision support: ERP systems provide essential information on business performance across functional areas. This information significantly improves managers’ ability to make better, more timely decisions.
3. Quality and efficiency: ERP systems integrate and improve an organization’s business pro cesses, generating significant improvements in the quality of production, distribution, and customer service.
Despite all of their benefits, however, ERP systems do have drawbacks. The major limita tions of ERP implementations include the following:
· The business processes in ERP software are often predefined by the best practices that the ERP vendor has developed. Best practices are the most successful solutions or prob lem-solving methods for achieving a business objective. As a result, companies may need to change their existing business processes to fit the predefined business processes in corporated into the ERP software. For companies with well-established procedures, this
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TABLE 10.2 |
ERP Modules |
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Core ERP Modules Financial Management. These modules support accounting, financial reporting, performance man agement, and corporate governance. They manage accounting data and financial processes such as general ledger, accounts payable, accounts receivable, fixed assets, cash management and forecasting, product-cost accounting, cost-center accounting, asset accounting, tax accounting, credit manage ment, budgeting, and asset management. Operations Management. These modules manage the various aspects of production planning and execution such as demand forecasting, procurement, inventory management, materials purchasing, shipping, production planning, production scheduling, materials requirements planning, quality con trol, distribution, transportation, and plant and equipment maintenance. Human Resource Management. These modules support personnel administration (including work force planning, employee recruitment, assignment tracking, personnel planning and development, and performance management and reviews), time accounting, payroll, compensation, benefits ac counting, and regulatory requirements. |
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Extended ERP Modules Customer Relationship Management. (Discussed in detail in Chapter 11.) These modules support all aspects of a customer’s relationship with the organization. They help the organization to increase customer loyalty and retention, and thus improve its profitability. They also provide an integrated view of customer data and interactions, helping organizations to be more responsive to customer needs. Supply Chain Management. (Discussed in detail in Chapter 11.) These modules manage the infor mation flows between and among stages in a supply chain to maximize supply chain efficiency and effectiveness. They help organizations plan, schedule, control, and optimize the supply chain from the acquisition of raw materials to the receipt of finished goods by customers. Business Intelligence. (Discussed in detail in Chapter 12.) These modules collect information used throughout the organization, organize it, and apply analytical tools to assist managers with decision making. E-Business. (Discussed in detail in Chapter 7.) Customers and suppliers demand access to ERP infor mation, including order status, inventory levels, and invoice reconciliation. Furthermore, they want this information in a simplified format that can be accessed on the web. As a result, these modules provide two channels of access into ERP system information—one channel for customers (B2C) and one for suppliers and partners (B2B). |
requirement can create serious problems, especially if employees do not want to abandon their old ways of working and therefore resist the changes.
· At the same time, however, an ERP implementation can provide an opportunity to improve and in some cases completely redesign inefficient, ineffective, or outdated procedures. In fact, many companies benefit from implementing best practices for their accounting, finance, and human resource processes, as well as other support activities that companies do not consider a source of competitive advantage.
Recall from Chapter 2, however, that different companies organize their value chains in different configurations to transform inputs into valuable outputs and achieve competi tive advantages. Therefore, although the vendor’s best practices, by definition, are appro priate for most organizations, they might not be the “best” one for your company if they change those processes that give you a competitive advantage.
· ERP systems can be extremely complex, expensive, and time consuming to implement. (We discuss the implementation of ERP systems in detail in the next section.) In fact, the costs and risks of failure in implementing a new ERP system are substantial. Quite a few companies have experienced costly ERP implementation failures. Specifically, they have suffered losses in revenue, profits, and market share when core business processes and information systems failed or did not work properly. In many cases, orders and shipments were lost, inventory changes were not recorded correctly, and unreliable inventory levels caused major stock outs. Companies such as Hershey Foods, Nike, A-DEC, and Connecticut General sustained losses in amounts up to hundreds of millions of dollars. In the case of FoxMeyer Drugs, a $5 billion pharmaceutical wholesaler, the ERP implementation was so poorly executed that the company had to file for bankruptcy protection.
In almost every ERP implementation failure, the company’s business managers and IT professionals underestimated the complexity of the planning, development, and training that were required to prepare for a new ERP system that would fundamentally transform their busi ness processes and information systems. The following are the major causes of ERP implemen tation failure:
· Failure to involve affected employees in the planning and development phases and in change management processes
· Trying to accomplish too much too fast in the conversion process
· Insufficient training in the new work tasks required by the ERP system
· Failure to perform proper data conversion and testing for the new system
Implementing ERP Systems
Companies can implement ERP systems by using either on-premise software or software-as a-service (SaaS). We differentiate between these two methods in detail in Technology Guide 3.
On-Premise ERP Implementation. Depending on the types of value chain pro cesses managed by the ERP system and a company’s specific value chain, there are three stra tegic approaches to implementing an on-premise ERP system:
1. The vanilla approach: In this approach, a company implements a standard ERP package, using the package’s built-in configuration options. When the system is implemented in this way, it will deviate only minimally from the package’s standardized settings. The vanilla approach can enable the company to perform the implementation more quickly. However, the extent to which the software is adapted to the organization’s specific processes is lim ited. Fortunately, a vanilla implementation provides general functions that can support the firm’s common business processes with relative ease, even if they are not a perfect fit for those processes.
2. The custom approach: In this approach, a company implements a more customized ERP system by developing new ERP functions designed specifically for that firm. Decisions con cerning the ERP’s degree of customization are specific to each organization. To use the custom approach, the organization must carefully analyze its existing business processes to develop a system that conforms to the organization’s particular characteristics and pro cesses. Customization is also expensive and risky because computer code must be written and updated every time a new version of the ERP software is released. Going further, if the customization does not perfectly match the organization’s needs, then the system can be very difficult to use.
3. The best-of-breed approach: This approach combines the benefits of the vanilla and cus tomized systems while avoiding the extensive costs and risks associated with complete customization. Companies that adopt this approach mix and match core ERP modules as well as other extended ERP modules from different software providers to best fit their unique internal processes and value chains. Thus, a company may choose several core ERP modules from an established vendor to take advantage of industry best practices—for ex ample, for financial management and human resource management. At the same time, it may also choose specialized software to support its unique business processes—for exam ple, for manufacturing, warehousing, and distribution. Sometimes companies arrive at the best of breed approach the hard way. For example, Dell wasted millions of dollars trying to customize an integrated ERP system from a major vendor to match its unique processes before it realized that a smaller, more flexible system that integrated well with other cor porate applications was the answer.
Software-as-a-Service ERP Implementation. Companies can acquire ERP systems without having to buy a complete software solution (i.e., on-premise ERP
implementation). Many organizations are using software-as-a-service (SaaS) (discussed in Chapter 13 and Technology Guide 3) to acquire cloud-based ERP systems. (We discuss cloud computing in Technology Guide 3).
In this business model, the company rents the software from an ERP vendor who offers its products over the Internet using the SaaS model. The ERP cloud vendor manages software updates and is responsible for the system’s security and availability.
Cloud-based ERP systems can be a perfect fit for some companies. For example, companies that cannot afford to make large investments in IT, yet already have relatively structured business processes that need to be tightly integrated, might benefit from cloud computing.
The relationship between the company and the cloud vendor is regulated by contracts and by service level agreements (SLAs). The SLAs define the characteristics and quality of service; for example, a guaranteed uptime, or the percentage of time that the system is available. Cloud vendors that fail to meet these conditions can face penalties.
The decision about whether to use on-premise ERP or SaaS ERP is specific to each organi zation, and it depends on how the organization evaluates a series of advantages and disadvan tages. The following are the three major advantages of using a cloud-based ERP system:
1. The system can be used from any location that has Internet access. Consequently, users can work from any location using online shared and centralized resources (data and data bases). Users access the ERP system through a secure virtual private network (VPN) con nection (discussed in Chapter 4) with the provider.
2. Companies using cloud-based ERP avoid the initial hardware and software expenses that are typical of on-premise implementations. For example, to run SAP on-premise, a com pany must purchase SAP software as well as a license to use SAP. The magnitude of this investment can hinder small- to medium-sized enterprises (SMEs) from adopting ERP.
3. Cloud-based ERP solutions are scalable, meaning it is possible to extend ERP support to new business processes and new business partners (e.g., suppliers) by purchasing new ERP modules.
There are also disadvantages to adopting cloud-based ERP systems that a company must carefully evaluate. The following are the three major disadvantages of using a cloud-based ERP system:
1. It is not clear whether cloud-based ERP systems are more secure than on-premise systems. In fact, a survey conducted by North Bridge Venture Partners indicated that security was the primary reason why organizations did not adopt cloud-based ERP.
2. Companies that adopt cloud-based ERP systems sacrifice their control over a strategic IT resource. For this reason, some companies prefer to implement an on-premise ERP sys tem, using a strong in-house IT department that can directly manage the system.
3. A direct consequence of the lack of control over IT resources occurs when the ERP system experiences problems, for example, some ERP functions are temporarily slow or are not available. In such cases, having an internal IT department that can solve problems im mediately rather than dealing with the cloud vendor’s system support can speed up the system recovery process.
This situation is particularly important for technology-intensive companies. In such companies, IT is crucial to conduct any kind of business with customers. Exam ples are e-commerce companies, banks, and government organizations that manage emergencies or situations that might involve individual and national security (e.g., healthcare organizations, police, homeland security department, antiterrorism units, and others).
Finally, slow or unavailable software from a cloud-based ERP vendor creates business con tinuity problems for the client. (We discuss business continuity in Chapter 4.) That is, a sudden system problem or failure makes it impossible for the firm to operate. Companies lose money when they lose business continuity because customers cannot be serviced and employees
cannot do their jobs. A loss of business continuity also damages the company’s reputation be cause customers lose trust in the firm.
Enterprise Application Integration
For some organizations, integrated ERP systems are not appropriate. This situation is partic ularly true for companies that find the process of converting from their existing system too difficult or time consuming.
Such companies, however, may still have isolated information systems that need to be connected with one another. To accomplish this task, these companies can use enterprise ap plication integration. An enterprise application integration (EAI) system integrates existing systems by providing software, called middleware, that connects multiple applications. In es sence, the EAI system allows existing applications to communicate and share data, thereby enabling organizations to use existing applications while eliminating many of the problems caused by isolated information systems. EAI systems also support implementation of best-of breed ERP solutions by connecting software modules from different vendors.
Before you go on. . .
1. Define ERP and describe its functions.
2. What are ERP II systems?
3. Differentiate between core ERP modules and extended ERP modules.
4. List some drawbacks of ERP software.
5. Highlight the differences between ERP configuration, customization, and best-of-breed implementa tion strategies.
ERP Support for Business Processes
10.4
ERP systems effectively support a number of standard business processes. In particular, ERP systems manage end-to-end, cross-departmental processes. A cross-departmental process is one that (1) originates in one department and ends in a different department or (2) originates and ends in the same department but involves other departments.
The Procurement, Fulfillment, and Production Processes
The following are the three prominent examples of cross-departmental processes:
1. The procurement process, which originates in the warehouse department (need to buy) and ends in the accounting department (send payment)
2. The fulfillment process, which originates in the sales department (customer request to buy) and ends in the accounting department (receive payment)
3. The production process, which originates and ends in the warehouse department (need to produce and reception of finished goods) but involves the production department as well
These three processes are examined in more detail in the following sections, focusing on the steps that are specific to each one.
The Procurement Process. The procurement process originates when a company needs to acquire goods or services from external sources, and it concludes
POM
ACCT
300 CHAPTER 10 Information Systems within the Organization
ERP Support for Business Processes 299
FIGURE 10.5 Departments and documents flow in the procurement process.
when the company receives and pays for them. Let’s consider a procurement process in which the company needs to acquire physical goods (see Figure 10.5). This process involves three main departments—Warehouse, Purchasing, and Accounting—and it consists of the following steps:
1. The process originates in the Warehouse department, which generates a purchase requisi tion to buy the needed products.
2. The Warehouse forwards the requisition to the Purchasing department, which creates a purchase order (PO) and forwards it to a vendor. Generally, companies can choose from a number of vendors, and they select the one that best meets their requirements in regard to convenience, speed, reliability, and other characteristics.
3. After the company places the order, it receives the goods in its Warehouse department, where someone physically checks the delivery to make certain that it corresponds to what the company ordered. He or she performs this task by comparing a packing list attached to the shipment against the PO.
4. If the shipment matches the order, then the Warehouse issues a goods receipt document.
5. At the same time or shortly thereafter, the Accounting department receives an invoice from the vendor. Accounting then checks that the PO, the goods receipt document, and the in voice match. This process is called the three-way-match.
6. After Accounting verifies the match, it processes the payment and sends it to the vendor.
The Order Fulfillment Process. In contrast to procurement, in which the company purchases goods from a vendor, in the order fulfillment process, also known as the order-to cash process, the company sells goods to a customer. Fulfillment originates when the company receives a customer order, and it concludes when the company receives a payment from the customer. (See the closing case in Chapter 11 for a look at Amazon’s fulfillment process.)
The fulfillment process can follow two basic strategies: sell-from-stock and configure-to- order. Sell-from-stock involves fulfilling customer orders directly using goods that are in the warehouse (stock). These goods are standard, meaning that the company does not customize them for buyers. In contrast, in configure-to-order, the company customizes the product in re sponse to a customer request.
MKT POM ACCT
A fulfillment process involves three main departments: Sales, Ware house, and Accounting. This process includes the following steps:
1. The Sales department receives a customer inquiry, which essentially is a request for infor mation concerning the availability and price of a specific good. (We restrict our discussion here to fulfilling a customer order for physical goods rather than services.)
2. After Sales receives the inquiry, it issues a quotation that indicates availability and price.
3. If the customer agrees to the price and terms, then Sales creates a customer purchase order (PO) and a sales order.
4. Sales forwards the sales order to the Warehouse. The sales order is an interdepartmental document that helps the company keep track of the internal processes that are involved in fulfilling a specific customer order. It also provides details of the quantity, price, and other characteristics of the product.
5. The Warehouse prepares the shipment and produces two other internal documents: the picking document, which it uses to remove goods from the Warehouse, and the packing list, which accompanies the shipment and provides details about the delivery.
6. At the same time, Accounting issues an invoice for the customer.
7. The process concludes when Accounting receives a payment that is consistent with the invoice.
Figure 10.6 shows the fulfillment process. Note that it applies to both sell-from-stock and configure-to-order because the basic steps are the same for both strategies.
The Production Process. The production process does not occur in all com panies because not all companies produce physical goods. In fact, many businesses limit their activities to buying (procurement) and selling products (e.g., retailers).
POM
The production process can follow two different strategies: make-to-stock and make-to order. (See the discussion of the pull model and the push model in Chapter 11.) Make-to-stock occurs when the company produces goods to create or increase an inventory; that is, finished products that are stored in the warehouse and are available for sales. In contrast, make-to- order occurs when production is generated by a specific customer order.
Manufacturing companies that produce their own goods manage their interdepartmental production process across the Production and Warehouse departments. The production pro cess involves the following steps:
1. The Warehouse department issues a planned order when the company needs to produce a finished product, either because the Warehouse has insufficient inventory or because the customer placed a specific order for goods that are not currently in stock.
2. Once the planned order reaches Production, the production controller authorizes the or der and issues a production order, which is a written authorization to start the production of a certain amount of a specific product.
3. To assemble a finished product, Production requires a number of materials (or parts). To acquire these materials, Production generates a material withdrawal slip, which lists all of the needed parts, and forwards it to the Warehouse.
4. If the parts are available in the Warehouse, then the Warehouse delivers them to Produc tion. If the parts are not available, then the company must purchase them through the procurement process.
5. After Production has created the products, it updates the production order specifying that, as planned, a specific number of units of product now can be shipped to the Warehouse.
6. As soon as the Warehouse receives the finished goods, it issues a goods receipt document that certifies how many units of a product it received that are available for sales.
This overview of the Production process is a highly simplified one. In reality, the process is very complex, and it frequently involves additional steps. ERP systems also collect a number of other documents and pieces of information such as the bill of materials (a list of all materials needed to assemble a finished product), the list of work centers (locations where the produc tion takes place), and the product routing (production steps). All of these topics require an in- depth analysis of the production process and are therefore beyond the scope of our discussion here. Figure 10.7 illustrates the production process.
FIGURE 10.6 Departments and documents flow in the fulfillment process.
FIGURE 10.7 Departments and documents flow in the production process.
A number of events can occur that create exceptions or deviations in the procurement, fulfillment, and production processes. Deviations may include the following:
· A delay in the receipt of products
· Issues related to an unsuccessful three-way-match regarding a shipment and its associ ated invoice (procurement)
· Rejection of a quotation
· A delay in a shipment
· A mistake in preparing the shipment or in invoicing the customer (fulfillment)
· Overproduction of a product
· Reception of parts that cannot be used in the production process
· Unavailability of certain parts from a supplier
Companies use ERP systems to manage procurement, fulfillment, and production because these systems track all of the events that occur within each process. Furthermore, the system stores all of the documents created in each step of each process in a centralized database, where they are available as needed in real time. Therefore, any exceptions or mistakes made during one or more interdepartmental processes are handled right away by simply querying the ERP system and retrieving a specific document or piece of information that needs to be re vised or examined more carefully. Therefore, it is important to follow each step in each process and to register the corresponding document into the ERP system.
Figure 10.8 portrays the three cross-functional business processes we just discussed. It specifically highlights the integration of the three processes, which is made possible by ERP systems.
FIGURE 10.8 Integrated processes with ERP systems.
Interorganizational Processes: ERP with SCM and CRM
Although the procurement and the fulfillment processes involve suppliers and customers, they are considered (together with the production process) intraorganizational processes because they originate and conclude within the company. However, ERP systems can also manage processes that originate in one company and conclude in another company. These processes are called interorganizational processes, and they typically involve supply chain management (SCM) and customer relationship management (CRM) systems. You can find a more detailed description of CRM and SCM in Chapter 11. Here, we focus on the integration of these processes within a firm’s industry value chain.
SCM and CRM processes help multiple firms in an industry coordinate activities such as the production-to-sale of goods and services. Let’s consider a chain of grocery stores whose supply chain must properly manage perishable goods. On the one hand, store managers need to stock only the amount of perishable products that they are reasonably sure they will sell before the products’ expiration dates. On the other hand, they do not want to run out of stock of any prod ucts that customers need.
ERP SCM systems have the capability to place automatic requests to buy fresh perishable products from suppliers in real time. That is, as each perishable product is purchased, the sys tem captures data on that purchase, adjusts store inventory levels, and transmits these data to the grocery chain’s warehouse as well as the products’ vendors. The system executes this pro cess by connecting the point-of-sale barcode scanning system with the Warehouse and Account ing departments, as well as with the vendors’ systems. SCM systems also use historical data to predict when fresh products need to be ordered before the store’s supply becomes too low.
ERP CRM systems also benefit businesses by generating forecasting analyses of product consumption based on critical variables such as geographical area, season, day of the week, and type of customer. These analyses help grocery stores coordinate their supply chains to meet cus tomer needs for perishable products. Going further, CRM systems identify particular customer needs and then use this information to suggest specific product campaigns. These campaigns can transform a potential demand into sales opportunities, and convert sales opportunities into sales quotations and sales orders. This process is called the demand-to-order process.
Before you go on. . .
1. What are the three main intraorganizational processes that are typically supported by ERP systems?
2. Why is it important that all steps in each process generate a document that is stored in the ERP system?
3. What is the difference between intraorganizational and interorganizational processes?
4. What are the two main ES systems that support interorganizational processes?
What’s in IT for me?
For the Accounting Major
ACCT
Understanding the functions and outputs of TPSs effectively is a major concern of any accountant. It is also necessary to understand the various activities of all functional areas and how they are inter connected. Accounting information systems are a central compo nent in any ERP package. In fact, all large CPA firms actively consult with clients on ERP implementations, using thousands of specially trained accounting majors.
For the Finance Major
IT helps financial analysts and managers perform their tasks better. Of particular importance is analyzing cash flows and securing the financing required for smooth operations. Financial applications can also support such activities as risk analysis, investment man agement, and global transactions involving different currencies and fiscal regulations.
FIN
Finance activities and modeling are key components of ERP systems. Flows of funds (payments), at the core of most supply chains, must be executed efficiently and effectively. Financial ar rangements are especially important along global supply chains, where currency conventions and financial regulations must be considered.
MKT For the Marketing Major
Marketing and sales expenses are usually targets in a cost- reduction program. Also, sales force automation improves not only salespeoples’ productivity (and thus reduces costs) but also cus tomer service.
POM For the Production/Operations Management Major
Managing production tasks, materials handling, and inventories in short time intervals, at a low cost, and with high quality is critical for competitiveness. These activities can be achieved only if they are properly supported by IT. IT can also greatly enhance interac tion with other functional areas, especially sales. Collaboration in
design, manufacturing, and logistics requires knowledge of how modern information systems can be connected.
HRM For the Human Resource Management Major
Human resources managers can increase their efficiency and ef fectiveness by using IT for some of their routine functions. Human resources personnel need to understand how information flows be tween the HR department and the other functional areas. Finally, the integration of functional areas through ERP systems has a ma jor impact on skill requirements and scarcity of employees, which are related to the tasks performed by the HRM department.
MIS For the MIS Major
The MIS function is responsible for the most fundamental informa tion systems in organizations: the transaction processing systems. The TPSs provide the data for the databases. In turn, all other in formation systems use these data. MIS personnel develop appli cations that support all levels of the organization (from clerical to executive) and all functional areas. The applications also enable the firm to do business with its partners.
Summary
1. Explain the purpose of transaction processing systems.
TPSs monitor, store, collect, and process data generated from all busi ness transactions. These data provide the inputs into the organiza tion’s database.
2. Explain the types of support that information systems can provide for each functional area of the organization.
The major business functional areas are production/operations man agement, marketing, accounting/finance, and human resources man agement. Table 10.1 provides an overview of the many activities in each functional area supported by FAIS.
3. Identify advantages and drawbacks to businesses of imple menting an ERP system.
Enterprise resource planning (ERP) systems integrate the planning, management, and use of all of the organization’s resources. The major objective of ERP systems is to tightly integrate the functional areas of the organization. This integration enables information to flow seam lessly across the various functional areas.
The following are the major benefits of ERP systems:
Because ERP systems integrate organizational resources, they make organizations more flexible, agile, and adaptive. The organiza tions can therefore react quickly to changing business conditions and capitalize on new business opportunities.
· ERP systems provide essential information on business per formance across functional areas. This information significantly
improves managers’ ability to make better, more timely decisions.
· ERP systems integrate organizational resources, resulting in sig nificant improvements in the quality of customer service, produc tion, and distribution.
The following are the major drawbacks of ERP systems:
· The business processes in ERP software are often predefined by the best practices that the ERP vendor has developed. As a result, companies may need to change existing business processes to fit the predefined business processes of the software. For compa nies with well-established procedures, this requirement can be a huge problem.
· ERP systems can be extremely complex, expensive, and time consuming to implement. In fact, the costs and risks of failure in implementing a new ERP system are substantial.
4. Describe the three main business processes supported by ERP systems.
The procurement process, which originates in the warehouse department (need to buy) and ends in the accounting department (send payment).
The fulfillment process that originates in the sales department (customer request to buy) and ends in the accounting department (receive payment).
The production process that originates and ends in the warehouse department (need to produce and reception of finished goods), but involves the production department as well.
We leave the details of the steps in each of these processes up to you.
Problem-Solving Activities 305
Chapter Glossary
ad hoc (on-demand) reports Nonroutine reports that often contain special information that is not included in routine reports.
batch processing Transaction processing system (TPS) that processes data in batches at fixed periodic intervals.
comparative reports Reports that compare performances of different business units or times.
computer-integrated manufacturing (CIM) An information system that integrates various automated factory systems; also called digital manufacturing.
cross-departmental process A business pro cess that originates in one department and ends in another department, or originates and ends in the same department while involving other departments.
drill-down reports Reports that show a greater level of details than is included in rou tine reports.
enterprise application integration (EAI) sys tem A system that integrates existing systems by providing layers of software that connect applications.
enterprise resource planning (ERP) sys tems Information systems that take a busi ness process view of the overall organization to integrate the planning, management, and use of all of an organization’s resources, em ploying a common software platform and database.
ERP II systems Interorganizational ERP sys tems that provide web-enabled links among key business systems (e.g., inventory and produc tion) of a company and its customers, suppliers, distributors, and others.
exception reports Reports that include only information that exceeds certain threshold standards.
functional area information systems (FAIS) Systems that provide information to managers (usually mid-level) in the functional areas to better support managerial tasks of plan ning, organizing, and controlling operations.
key indicator reports Reports that summa rize the performance of critical activities.
online transaction processing (OLTP) Trans action processing system (TPS) that processes
data after transactions occur, frequently in real time.
order fulfillment process A cross-functional business process that originates when the com pany receives a customer order, and it concludes when it receives a payment from the customer.
procurement process A cross-functional business process that originates when a com pany needs to acquire goods or services from external sources, and it concludes when the company receives and pays for them.
production process A cross-functional busi ness process in which a company produces physical goods.
routine reports Reports produced at sched uled intervals.
transaction Any business event that gen erates data worth capturing and storing in a database.
transaction processing system (TPS) Infor mation system that supports the monitoring, collection, storage, and processing of data from the organization’s basic business transactions, each of which generates data.
Discussion Questions
1. Why is it logical to organize IT applications by functional areas?
2. Describe the role of a TPS in a service organization.
3. Describe the relationship between TPS and FAIS.
4. Discuss how IT facilitates the budgeting process.
5. How can the Internet support investment decisions?
6. Describe the benefits of integrated accounting software packages.
7. Discuss the role that IT plays in support of auditing.
8. Investigate the role of the web in human resources management.
9. What is the relationship between information silos and enterprise resource planning?
Problem-Solving Activities
1. Finding a job on the Internet is challenging, as there are almost too many places to look. Visit the following sites: www.careerbuilder
.com , www.craigslist.org , www.linkedin.com , www.jobcentral
.com , and www.monster.com . What does each of these sites provide you with as a job seeker?
2. Enter www.sas.com and access revenue optimization there. Ex plain how the software helps in optimizing prices.
3. Enter www.eleapsoftware.com and review the product that helps with online training (training systems). What are the most attractive features of this product?
4. Examine the capabilities of the following (and similar) financial software packages: Financial Analyzer (from Oracle) and CFO Vision
(from SAS Institute). Prepare a report comparing the capabilities of the software packages.
5. Surf the Net and find free accounting software. (Try www.cnet
.com , www.rkom.com , www.tucows.com , www.passtheshareware
.com , and www.freeware-guide.com .) Download the software and try it. Compare the ease of use and usefulness of each software package.
6. Examine the capabilities of the following financial software pack ages: TekPortal (from www.tekknowledge.com ), Financial Analyzer (from www.oracle.com ), and Financial Management (from www
.sas.com ). Prepare a report comparing the capabilities of the software packages.
7. Find Simply Accounting Basic from Sage Software ( http://www 9. Enter www.iemployee.com and find the support it provides to
.sage.com/us/sage-50-accounting ). Why is this product recom human resources management activities. View the demos and prepare mended for small businesses? a report on the capabilities of the products.
8. Enter www.halogensoftware.com and www.successfactors
.com. Examine their software products and compare them.
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Closing Case
Information Technology and Sustainability
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The Problem
Sustainable business practices—including “green” business, environ mental responsibility, minimal impact on global or local environment, and the use of renewable clean energy sources—have become more than just a good idea in many industries. History has taught us that nonsustainable business practices—such as early settlers killing off bi son for their hides—lead to the rapid decline of an industry.
Tectona grandis, or teak wood, is a prized—and expensive—ma terial because of its elegance and durability. Teak is a tree native to the tropics in the Southeast Asian nations of Thailand, Burma, Malay sia, and Indonesia. Historically, it has been found only in the homes of wealthy and powerful families. A tree takes about 80 years to reach maturity and be ready to harvest. Even then, only the heart of the tree contains the best wood for building furniture. Given the length of time required to replenish a forest, it is critical for the teak furniture industry to use sustainable practices.
Most teak farms are independently owned, and they operate based on traditional practices rather than current knowledge about how to efficiently manage a forest. Historically, they have not been particularly concerned with sustainable operations. As a result, they might not understand the impact of their decisions on the overall industry. This situation, however, is beginning to change. Dipantara ( http://www.tft-earth.org/who-we-work-with/projects/ ), acompany engaged in Sustainable Community Forest Enterprise Management, works with the growers to emphasize the importance of maintaining their natural resources. Dipantara is particularly interested in teak.
In addition to the teak farms, several other parties are involved in this industry. First, there are independent, nonprofit organizations such as the Forestry Stewardship Council ( http://us.fsc.org ; FSC) that promote the responsible management of the world’s forests. FSC also provides industry certifications that a farm has pursued sustainable management practices. Second, European, U.S., and Australian timber regulations have been established to prevent companies from doing harm to the forests. Finally, customers want assurance that their pur chase is not harming the global environment.
These sustainability issues create challenges from the retailer to the root (quite literally). They require a coordinated effort to develop and maintain a consistent sustainability effort.
The IT Solution
The Forestry Trust ( www.tft-earth.org ; TFT) is an NGO that focuses on the entire teak ecosystem, where all needs must be considered for the industry as a whole to operate and exist sustainably. TFT identifies transparency as the key to establishing this sustainability. To support this level of communication and transparency, the NGO developed a set of tools called SURE Technology that helps growers and companies throughout the supply chain to plan, understand, and communicate.
TFT members use the SURE Technology system to balance the interests and requirements of several external stakeholders. The only way any company can meet the demands of its external stakeholders is to obtain information about the practices of the suppliers of their raw materials. The SURE supply chain management module maps companies’ products all the way to the source to help product man ufacturers ensure that their orders are the products of legal, responsi ble, sustainable harvesting and use of the forest.
The SURE Technology allows raw material providers (such as members of the Dipantara community forests in Java) to identify their social and environmental values and track their progress on adhering to those values through dashboards. This information is available to potential buyers, who require confirmation that the materials are pro vided in a manner that is consistent with their values.
The Results
Maisons du Monde ( www.maisonsdumonde.com ; MdM) is an environ mentally focused home decoration retailer based in France. As a member of TFT, the firm works with Dipantara community farmers in Java, who supply the teak for some of their furniture. MdM has access to information on the forestry practices of teak growers through its TFT membership and the SURE Technology tools that are part of that membership. The result is that more than 50 percent of MdM products are labeled with one or more industry certifications affirming that the product is not contributing to deforestation. Furthermore, MdM sells more than 1,100 products that carry the FSC logo that confirms that the products were manufactured by responsible sources and can be traced all the way back to the tree.
How is this possible? The answer is the use of the industry supply chain system provided by TFT. The SURE Technology tools bring to gether all who grow and sell teak into a single platform to create a more transparent, environmentally friendly use of our natural resources.
Sources: Compiled from A. Esch, “Sustainable Forests and Practicing Selective Logging,” The Balance, November 16, 2016; “Indonesia’s Timber Going Green— and Global,” Forest, Trees, and Agroforestry, August 7, 2016; “Dipantara Forest Project Reaches Aims,” TFT-Earth Case Study, December 22, 2015; J. Clark, “What’s So Great about Teak Wood Furniture,” howstuffworks.com , accessed December 14, 2015; “A Year in the Life of a TFT Forester,” TFT-Earth Case Study, February 17, 2015; S. Hickman, “Indonesian Teak Farmers Achieve Traceability to the Tree Stump,” The Guardian, March 18, 2014; www.maisonsdumonde.com; www.onepercentfortheplanet.org, www.tft-earth.org; www.en.dipantara.co.id; www.us.fcs.org; www.tft-earth.org/sure; www.theevergreengroup.com; accessed October 26, 2016.
Questions
1. Describe how information technology contributes to the sustain- ability of teak production.
2. Refer to Chapter 2. Is the SURE system a strategic information sys tem for Maisons du Monde? Why or why not?
3. Refer to Chapter 8. How would the Internet of Things help the For estry Trust in its mission?
Customer Relationship Management and Supply Chain Management
CHAPTER OUTLINE LEARNING OBJECTIVES
11.1 Defining Customer Relationship 11.1 Identify the primary functions of both customer relationship Management management (CRM) and collaborative CRM strategies.
11.2 Operational Customer Relationship 11.2 Describe how businesses might use applications of each of the two Management major components of operational CRM systems.
11.3 Other Types of Customer Relationship 11.3 Explain the advantages and disadvantages of mobile CRM systems,
Management Systems on-demand CRM systems, open-source CRM systems, social CRM systems, and real-time CRM systems.
11.4 Supply Chains 11.4 Describe the three components and the three flows of a supply chain.
11.5 Supply Chain Management 11.5 Identify popular strategies to solving different challenges of supply
chains.
11.6 Information Technology Support for 11.6 Explain the utility of each of the three major technologies that Supply Chain Management support supply chain management.
Opening Case
CHAPTER 11
Do Persuasive Technologies Go Too Far?
Persuasive technologies are designed to influence people’s behavior and change their attitudes. These technologies, which in clude apps, websites, video games, social media, and others, inte grate traditional methods of persuasion—the use of information and incentives, for example—with capabilities of information technology to modify user behavior. Persuasive technologies can be found in mo bile downloads and on websites on which behavior-oriented design
MKT
persuades consumers to purchase more items (e.g., one-click check out at Amazon) or to stay logged in longer (e.g., manipulating social media news feeds on Facebook).
Marketing professionals argue that persuasive technologies can benefit consumers, who want better service and more suitable offer ings. The marketers note that the best persuasive technologies are those that gently influence people to do something they wish they had been doing anyway.
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Today, many companies are using technologies that measure cus tomer behavior in order to design products that are not only persua sive but are specifically intended to create new habits. In addition to the technologies themselves, companies use data generated by these technologies to further refine persuasive and perhaps habit-forming strategies.
Many companies have included habit formation as a business model (for example, casinos, cigarette manufacturers, alcohol pro ducers, fast food establishments, and many others.) Persuasive technologies have provided that option to a broad range of com panies. Insights from psychology and behavioral economics con cerning how and why people make certain choices, combined with information technologies, have enabled designers of websites, apps, and many other products to create sophisticated and persua sive technologies.
Persuasive technologies raise many ethical issues, particularly the line between persuasion and manipulation. Manipulation implies persuasion with the intent to fool or control people into doing some thing, believing something, or buying something that either harms them or provides no benefit to them. The question is: Given the depth of information about consumers that persuasive technologies gener ate, coupled with companies’ increasingly sophisticated attempts to influence consumer behavior, what are the appropriate limits for these technologies, which can be so well designed that they are essentially invisible?
Let’s consider several examples of companies that are using per suasive technologies:
· GSN Games ( www.gsn.com ) designs mobile games such as poker and bingo. The company collects billions of data points each day from its players’ smartphones and tablets. These data reveal ev erything from the time of day that users play, to the type of game they prefer, to how they deal with failure. Insights from these data are so effective that, if two people were to download a game into the same type of smartphone simultaneously, in as little as five minutes, their games would begin to diverge, with each game au tomatically tailored to its user’s style of play.
Significantly, GSN does more than simply track customers’ preferences and customize its offerings accordingly. To induce players to play longer and to try more games, the company uses the data to watch for signs that players are tiring. By measuring how frequently and how quickly players press on their screens, GSN can predict when they are likely to lose interest. It then sug gests other games before players reach that point. The games are free, but GSN displays ads and sells virtual items that are useful to players. Therefore, the longer the company can persuade some one to play, the more money it makes.
· Rocket Fuel ( www.rocketfuel.com ) uses artificial intelligence to predict the best ad to present to a given customer who is visiting a particular web page, taking into account multiple types of information, such as: (a) data gathered from websites;
(b) the browsing, advertising, and purchase history associated with a given shopper’s Internet Protocol address; and (c) in sights into what style of ad works best on a particular website. The company claims that its targeted ads generate revenue for clients amounting to two to eight times what clients spend on the ads.
· Opower ( www.opower.com ) is a software company that pro motes home energy efficiency. Instead of showing customers the
usual power bill, Opower collects data from the home and dis plays the data in a chart that compares a homeowner’s energy use to the average energy use of his or her neighbors.
· Companies have been working for years on strategies to help people remember to take their medicines on time. GlowCaps ( www.glowcaps.com ) produces a special cap that fits on top of a standard pill bottle. The cap lights up when patients need to take their medicine. The caps are also wirelessly enabled, and they can send reports about how well patients are adhering to their medication schedule.
· In hybrid cars, including the Toyota Prius and Ford Fusion hy brid, display panels inform drivers of how efficiently they are driving at any given moment. The Prius plots this informa tion on a bar graph that indicates current miles per gallon. The Fusion displays a virtual plant growing (or dying) on the dashboard screen as a person’s driving efficiency increases or decreases.
· Approximately 1.6 million people in the African country of Mo zambique live with the human immunodeficiency virus (HIV). Un fortunately, only 74 percent of patients who begin HIV treatment are still taking their medication one year later. In November 2011, the British-based international children’s charity Ark began send ing text messages to HIV-positive people in one of Mozambique’s provinces to remind them about treatments and appointments. The messages helped urban and recently diagnosed HIV patients continue with their treatment regimens. Results at rural treat ment centers were disappointing, possibly due to transportation issues or limited cellular coverage.
Sources: Compiled from K. Eikenberry, “Persuasion or Manipulation?” Business Management Daily, October 27, 2016; E. Bernstein, “If You Want to Persuade People, Try ‘Altercasting,’” Wall Street Journal, September 5, 2016; L. Wend,
“Is Pricing Strategy a Game of Persuasion or Manipulation?” Deakin Business School, May 6, 2016; E. Tucker, “Can an App Really Persuade Children to Go to Sleep?” The Guardian, May 19, 2015; S. Wolf, “A Brief Look at Persuasive Technology,” kachwanya.com , April 30, 2015; E. Howell, “Using Tech to Persuade Us to Pay Attention, Just a Little Longer,” Herox, April 21, 2015; P. Newton, “The Persuasive Power of Technology,” IntelligentHQ, April 11, 2015;
K. Majcher, “Persuasive Texting in Mozambique,” MIT Technology Review, March 23, 2015; N. Byrnes, “Technology and Persuasion,” MIT Technology Review, March 23, 2015; J. Larson, “The Invisible, Manipulative Power of Persuasive Technology,” Pacific Standard, May 14, 2014; “Do Persuasive Technologies Persuade?” Gamification Research Network, April 8, 2014; T. Fritz, E. Huang,
G. Murphy, and T. Zimmerman, “Persuasive Technology in the Real World: A Study of Long-Term Use of Activity Sensing Devices for Fitness,” CHI Conference Proceedings, Toronto, Canada, 2014; J. Sutter, “Tech Guilt: 5 ‘Persuasive’ Technologies to Help You Be Good,” CNN News, August 13, 2010; www.gsn.com, www.rocketfuel.com, www.opower.com, www.glowcaps.com, accessed October 23, 2016.
Questions
1. Discuss the advantages and disadvantages of persuasive technol ogies to companies.
2. Discuss the advantages and disadvantages of persuasive technol ogies to consumers.
3. Debate the ethical nature of persuasive technologies. (Hint: Refer to Chapter 3 for ethical frameworks.)
4. Do persuasive technologies take customer relationship manage ment too far? Why or why not? Support your answer.
In Chapter 10, you learned about information systems that supported organizational activities within the organization. In this chapter, you study information systems that support organiza tional activities that extend outside the organization to customers and suppliers. The first half of this chapter addresses customer relationship management (CRM) systems and the second half addresses supply chain management (SCM) systems.
Organizations are emphasizing a customer-centric approach to their business practices because they realize that long-term customer relationships provide sustainable value that ex tends beyond an individual business transaction. Organizations are also integrating their strat egy and operations with supply chain partners because tight integration along the supply chain also leads to sustainable business value. Significantly, customer relationship management and supply chain management are important for all enterprises, regardless of size.
The chapter opening case points out that perhaps an organization’s customer relationship management efforts can go too far. With today’s sophisticated information technology, organi zations must be careful not to breach ethical guidelines in how they manage relationships with their customers.
At this point, you might be asking yourself: Why should I learn about CRM and SCM? The answer, as you will see in this chapter, is that customers and suppliers are supremely important to all organizations. Regardless of your job, you will have an impact, whether direct or indirect, on managing your firm’s customers and your firm’s supply chain. When you read the What’s in IT for Me? section at the end of this chapter, you will learn about opportunities to make imme diate contributions on your job. Therefore, it is essential that you acquire a working knowledge of CRM and CRM systems, as well as SCM and SCM systems.
Defining Customer Relationship Management
11.1
Before the supermarket, the mall, and the automobile, people purchased goods at their neigh borhood store. The owners and employees recognized customers by name and knew their pref erences and wants. For their part, customers remained loyal to the store and made repeated purchases. Over time, however, this personal customer relationship became impersonal as people moved from farms and small towns to cities, consumers became mobile, and supermar kets and department stores achieved economies of scale through mass marketing. Although prices were lower and products were more uniform in quality, the relationship with customers became nameless and impersonal.
The customer relationship has become even more impersonal with the rapid growth of the Internet and the World Wide Web. In today’s hypercompetitive marketplace, customers are in creasingly powerful; if they are dissatisfied with a product or a service from one organization, a competitor is often just one mouse click away. Furthermore, as more and more customers shop on the web, an enterprise does not even have the opportunity to make a good first impression in person.
Customer relationship management returns to personal marketing. That is, rather than market to a mass of people or companies, businesses market to each customer individually. By employing this approach, businesses can use information about each customer—for example, previous purchases, needs, and wants—to create highly individualized offers that customers are more likely to accept. The CRM approach is designed to achieve customer intimacy.
Customer relationship management is a customer-focused and customer-driven organi zational strategy. That is, organizations concentrate on assessing customers’ requirements for products and services and then providing a high-quality, responsive customer experience. CRM is not a process or a technology per se; rather, it is a customer-centric way of thinking and act ing. The focus of modern organizations has shifted from conducting business transactions to
managing customer relationships. In general, organizations recognize that customers are the core of a successful enterprise, and the success of the enterprise depends on effectively man aging relationships with them.
The CRM approach is enabled by information technology in the form of various systems and applications. However, CRM is not only about the software. Sometimes the problem with managing relationships is simply time and information. Old systems may contain the needed information, but this information may take too long to access and may not be usable across a variety of applications. The result is that companies have less time to spend with their customers.
In contrast, modern CRM strategies and systems build sustainable long-term customer re lationships that create value for the company as well as for the customer. That is, CRM helps companies acquire new customers and retain and expand their relationships with profitable existing customers. Retaining customers is particularly important because repeat customers are the largest generator of revenue for an enterprise. Also, organizations have long understood that winning back a customer who has switched to a competitor is vastly more expensive than keeping that customer satisfied in the first place.
Figure 11.1 depicts the CRM process. The process begins with marketing efforts, through which the organization solicits prospects from a target population of potential customers. A certain number of these prospects will make a purchase and thus become customers. A certain number of these customers will become repeat customers. The organization then segments its repeat customers into low- and high-value repeat customers. An organization’s overall goal is to maximize the lifetime value of a customer, which is that customer’s potential revenue stream over a number of years.
Over time all organizations inevitably lose a certain percentage of customers, a process called customer churn. The optimal result of the organization’s CRM efforts is to maximize the number of high-value repeat customers while minimizing customer churn.
FIGURE 11.1 The customer relationship management process.
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CRM is a fundamentally simple concept: Treat different customers differently because their needs differ and their value to the company may also differ. A successful CRM strategy not only improves customer satisfaction but also makes the company’s sales and service employees more productive, which in turn generates increased profits. Researchers at the National Quality Research Center at the University of Michigan discovered that a 1 percent increase in customer satisfaction can lead to as much as a 300 percent increase in a company’s market capitalization, defined as the number of shares of the company’s stock outstanding multiplied by the price per share of the stock. Put simply, a minor increase in customer satisfaction can generate a major increase in a company’s overall value.
Up to this point, you have been looking at an organization’s CRM strategy. It is important to distinguish between a CRM strategy and CRM systems. Basically, CRM systems are information systems designed to support an organization’s CRM strategy. For organizations to pursue ex cellent relationships with their customers, they need to employ CRM systems that provide the infrastructure needed to support those relationships. Because customer service and support are essential to a successful business, organizations must place a great deal of emphasis on both their CRM strategy and their CRM systems.
Broadly speaking, CRM systems lie along a continuum, from low-end CRM systems— designed for enterprises with many small customers—to high-end CRM systems—for enter prises with a few large customers. An example of a low-end system is Amazon, which uses its CRM system to recommend products to returning customers. An example of a high-end system is Boeing, which uses its CRM system to coordinate staff activities in a campaign to sell its new 787 aircraft to Delta Airlines. As you study the cases and examples in this chapter, consider where on the continuum a particular CRM system would fall.
Although CRM varies according to circumstances, all successful CRM policies share two basic elements: (1) The company must identify the many types of customer touch points, and
(2) it needs to consolidate data about each customer. Let’s examine these two elements in more detail.
Customer Touch Points
Organizations must recognize the numerous and diverse interactions they have with their cus tomers. These interactions are referred to as customer touch points. Traditional customer touch points include telephone contact, direct mailings, and actual physical interactions with cus tomers during their visits to a store. Organizational CRM systems, however, must manage many additional customer touch points that occur through the use of popular personal technologies. These touch points include e-mail, websites, and communications through smartphones (see Figure 11.2).
The business–customer relationship is constantly evolving. As personal technology usage changes, so too must the methods that businesses use to interface with their customers. It is now possible to physically locate customers through their smartphones. As a result, location infor mation can now provide another customer touch point, as you see in IT’s About Business 11.1.
Data Consolidation
Data consolidation is also critical to an organization’s CRM efforts. The organization’s CRM sys tems must manage customer data effectively. In the past, customer data were stored in isolated systems (or silos) located in different functional areas across the business—for example, in separate databases in the finance, sales, logistics, and marketing departments. Consequently, data for individual customers were difficult to share across the various functional areas.
As you saw in Chapter 5, modern interconnected systems built around a data warehouse now make all customer-related data available to every unit of the business. This complete data set on each customer is called a 360º view of that customer. By accessing this view, a company can enhance its relationship with its customers and ultimately make more productive and prof itable decisions.
FIGURE 11.2 Customer touch points.
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IT’s About Business 11.1
Location-Based Systems in Customer Relationship Management
MKT MIS
eMarketer ( www.emarketer.com ) predicted that there would be 2 billion smartphones around the world in 2016. It also expected that Wi-Fi networks would carry more traffic than cellular networks in 2016. This shift results in fewer restrictions on bandwidth for mobile users to download and upload content. As a result, location- based systems and services were expected to grow rapidly.
For example, let’s take a look at the Golden State Warriors ( www.nba.com/warriors ), who won the NBA championship in June 2015. Significantly, technology increased the enjoyment of fans who were on hand to enjoy the victory at Oracle Arena, the team’s current home in Oakland, California. Although the Warriors lost to the Cleveland Cavaliers in the NBA finals in 2016, Warrior fans used technology again to enjoy the Warriors’ season.
Oracle arena sends personalized messages to fans’ mobile de vices about merchandise deals at the team store. Once in the arena for a game, they can also receive messages about seat upgrades. War riors fans—called members of Dub Nation—can sign up for an app that detects their location in the arena and alerts them to which entrances are less busy. The app uses geolocation technology, including a bea con, a wireless hardware device that pinpoints the app users’ location.
The idea behind the Warriors’ apps is that location is an im portant way to add value for customers who are essentially a cap tive audience. Specifically, location-based services (LBS) enable the Warriors to improve service and to deepen fan loyalty. LBS can target fans where they are in real time, giving them offers they can take advantage of right away.
The Warriors have experienced mixed results from their location-based services. On the positive side, LBS notifications were credited with making it easier for fans to upgrade seats, gen erating approximately 15 percent of arena seat upgrades. In con trast, fans largely ignored the LBS notifications about free popcorn with the purchase of a slice of pizza.
Even if technology can do something, it doesn’t always make practical sense. For example, the Warrior app has the ability to show instant replays to fans in the arena. But the organization nixed that idea, figuring the action already moved too fast for re plays to be of value. However, the team was considering making instant replays available to fans not attending the game.
The LBS data on its own won’t give the Warriors the complete picture of their fans and enable them to provide experiences in context. The team must integrate that data with information from CRM, inventory, and workforce management systems. Fan be havior also varies whether they are in the arena, on the phone, or online.
There are other interesting examples of location-based ser- vices employed in customer relationship management. In Brazil, Nivea, the maker of skin care products, inserts a beacon in mag azine ads that parents can pull out and make into a wristband for their kids. A smartphone app can monitor how far the children are from their parents when in public. The app notifies parents when the child strays beyond a certain distance. As a result of this CRM tool, Brazilians now perceive Nivea as a company that cares about its customers’ children.
As another example, 313 Somerset, a mall in Singapore, has deployed a beacon-based mobile advertising service. The Tring313 app detects when users who opted in to the service are within a predefined range from the mall. That triggers the sending of cou pons and other offers to their smartphones. Retailers have enjoyed a sales conversion rate of 46 percent thanks to the promotions. (The sales conversion rate is the percentage of prospective custom- ers who actually make a purchase.)
LBS does have downsides. Critics feel that the technology al lows some companies to be too aggressive, sending so many mes sages that consumers either ignore them or shut off their phones. Even worse, customers may be so irritated and feel the messages are so irrelevant that they get rid of the app entirely. For LBS to work, companies must offer what their customers want, when they want it.
Sources: Compiled from “Most Smartphone Owners Use Location-Based Services,” eMarketer, April 22, 2016; M. Jones, “3 Ways Location-Based Marketing Is Shaping Retail in 2016,” Forbes, March 3, 2016; A. Strout, “Four Key Mobile and Location-Based Trends for 2016,” Marketing Land, January 15, 2016; L. Horwitz, “Location-Based Apps Hit the Streets, the Seats, and Everywhere Else,” TechTarget, October 8, 2015; L. Horwitz, “Where the Customer Leads,” Business Information, October 2015;
J. Nyland, “Five Ways to Use Location-Based Marketing Right Now,” MarketingProfs, June 29, 2015; M. Rao, “Mobile + Creativity: 8 Innovative Examples of Location-Based Marketing,” YourStory, June 4, 2015; L. Alton, “10 Ways Location-Based Marketing Will Evolve in 2015,” Huffington
Post, January 9, 2015; P. Britt, “Consumers Love Mobile Marketing, Location-Based Services,” Enterprise Apps Today, November 11, 2014;
L. Johnson, “4 Location-Based Marketing Tactics That Are Working,” AdWeek, August 4, 2014; R. Goodrich, “Location-Based Services: Definition & Examples,” Business News Daily, October 30, 2013; www.nba.com/ warriors, accessed October 27, 2016.
Questions
1. Describe the advantages of location-based services for cus- tomers and for businesses.
2. Describe the disadvantages of location-based services for customers and for businesses.
3. What are other location-based services that the Golden State Warriors could provide for their fans?
Data consolidation and the 360º view of the customer enable the organization’s func tional areas to readily share information about customers. This information sharing leads to collaborative CRM. Collaborative CRM systems provide effective and efficient interactive communication with the customer throughout the entire organization. That is, they integrate communications between the organization and its customers in all aspects of marketing, sales, and customer support. Collaborative CRM systems also enable customers to provide direct feedback to the organization. As you read in Chapter 9, social media applications such as social networks, blogs, microblogs, and Wikis are very important to companies that value customer input into their product and service offerings, as well as into new product development.
Recall that an organization’s CRM system contains two major components: operational CRM systems and analytical CRM systems. You will learn about operational CRM systems in the next section. We provide a brief overview of analytical CRM systems at the end of Section 11.2, and discuss these systems in more detail in Chapter 12.
Before you go on. . .
1. What is the definition of customer relationship management?
2. Why is CRM so important to any organization?
3. Define and provide examples of customer touch points.
Operational Customer Relationship Management Systems
11.2
Operational CRM systems support front-office business processes. Front-office processes are those that directly interact with customers; that is, sales, marketing, and service. The two major components of operational CRM systems are customer-facing applications and
customer-touching applications (discussed further on). Operational CRM systems provide the following benefits:
· Efficient, personalized marketing, sales, and service
· A 360º view of each customer
· The ability of sales and service employees to access a complete history of customer inter action with the organization, regardless of the touch point
An example of an operational CRM system involves Caterpillar, Inc. ( www.cat.com ), an in ternational manufacturer of industrial equipment. Caterpillar uses its CRM tools to accomplish the following objectives:
· Improve sales and account management by optimizing the information shared by mul tiple employees and by streamlining existing processes (e.g., taking orders using mobile devices)
· Form individualized relationships with customers, with the aim of improving customer satisfaction and maximizing profits
· Identify the most profitable customers, and provide them with the highest level of service
· Provide employees with the information and processes necessary to know their customers
· Understand and identify customer needs, and effectively build relationships among the company, its customer base, and its distribution partners
Customer-Facing Applications
In customer-facing CRM applications, an organization’s sales, field service, and customer in teraction center representatives interact directly with customers. These applications include customer service and support, salesforce automation, marketing, and campaign management.
Customer Service and Support. Customer service and support refers to systems that automate service requests, complaints, product returns, and requests for information. To day, organizations have implemented customer interaction centers (CIC), in which organiza tional representatives use multiple channels such as the web, telephone, fax, and face-to-face interactions to communicate with customers.
One of the best-known customer interaction centers is the call center, a centralized office set up to receive and transmit a large volume of requests by telephone. Call centers enable com panies to respond to a large variety of questions, including product support and complaints. IT’s About Business 11.2 provides an example of Priceline.com’s new customer call center.
A new technology, chatbots, are extending the traditional CIC’s functionality. As you see in IT’s About Business 11.3, chatbots can be used for many purposes. In regard to the CIC, chatbots can help CIC agents by handling the more routine calls and queries. By doing so, the agents are free to handle more complex questions. With regard to businesses themselves, chat- bots serve a variety of functions in interactions with customers.
Salesforce Automation. Salesforce automation (SFA) is the component of an operational CRM system that automatically records all of the components in a sales transaction process. SFA systems include a contact management system, which tracks all communications between the company and the customer, the purpose of each communication, and any nec essary follow-up. This system eliminates duplicated contacts and redundancy, which in turn reduces the risk of irritating customers. SFA also includes a sales lead tracking system, which lists potential customers or customers who have purchased related products; that is, products similar to those that the salesperson is trying to sell to the customer.
Other elements of an SFA system can include a sales forecasting system, which is a math ematical technique for estimating future sales, and a product knowledge system, which is a comprehensive source of information regarding products and services. More-developed SFA
IT’s About Business 11.2
Priceline.com’s New Customer Contact Center
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Priceline.com ( www.priceline.com ) is an American company that helps its customers obtain discounts on travel-related services such as airline tickets and hotel stays. The company is not a di rect supplier of these services; rather, it helps its suppliers provide travel services to its customers.
Immediately before travel websites exploded in popularity, Priceline was using customer management software that it de veloped in-house. The proprietary applications could not provide complete pictures of customers. Customer service agents had restricted access to transaction histories, which severely limited their knowledge of the customers with whom they were speaking. As the online travel industry blossomed and pricing became cutthroat, Priceline had to be able to move fast. It needed to keep its customers informed of fast-moving price changes, new prod ucts, and changing travel regulations, no matter which method they used to contact the company. The new contact center had to expand as fast as the firm’s customer base was growing while supporting significantly higher levels of interaction with each cus tomer. Agents needed access to a list of frequently asked questions, among other content. To accomplish these goals, Priceline moved the process of information management away from its information technology employees to the firm’s marketing and product man agement departments so they could update the customer knowl
edge database and FAQs themselves.
Priceline developed its new contact center using KANA Soft ware ( www.kana.com ), which provides on-premise and cloud computing customer relationship management products. (We dis cuss on-premise and cloud computing in Technology Guide 3.) The contact center provides customer service agents with the ability to
quickly manage high volumes of e-mails. The center also connects customer contacts and Priceline’s internal databases, including several by Oracle. This link enables Priceline agents to react rap idly and consistently by having access to a 360-degree view of each customer. The contact center has predetermined rules that opti mize the process of responding to client enquiries, routing certain types of questions to the most knowledgeable customer service representative.
Visitors to Priceline’s website can also help themselves to a knowledge base to answer the most common questions. Using the self-service option is often quicker for customers, and it reduces the number of calls to customer service agents, who can handle more complex phone inquiries.
Sources: Compiled from L. Remmers, “Five Contact Center Trends that Will Dominate the Market by 2017,” Nearshore Americas, June 14, 2016; J. Wade, “Call Centers Chase a False Holy Grail of Artificial Intelligence and Full Au tomation,” Nearshore Americas, March 23, 2016; B. Morgan, “The Economist Predicts Robots Will Replace Contact Centers,” Forbes, February 16, 2016; R. Brink, “The Top 4 Biggest Challenges for Contact Centers in 2016,” 3C Logic, January 29, 2016; Nicolina, “Whitepaper: Top 9 Call Center Trends for 2016,” fonolo, January 12, 2016; B. Stackpole, “Contact Center Strives for Strategic Role in Customer Experience Management,” TechTarget, October 14, 2015;
S. Sachs, “Contact Center Automation Takes Flight,” TechTarget March 10, 2015; L. Klie, “5 Hot Customer Service Technologies,” DestinationCRM.com , December 2014; “Priceline.com,” KANA Case Study, 2014; P. Greenberg, “The Customer Experience Challenge Met: KANA Responds,” ZDNet, January 9, 2013; www.priceline.com, www.kana.com, accessed October 27, 2016.
Questions
1. Discuss the reasons why Priceline needed a new customer contact center.
2. Describe the benefits of the KANA solution to customers and to Priceline.
Chatbots and Conversational Commerce
IT’s About Business 11.3
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You’ve heard of e-commerce, and B2B commerce, but what about conversational commerce? Conversational commerce refers to the intersection of messaging apps and shopping; that is, the trend to ward interacting with businesses through messaging and chat apps such as Facebook Messenger ( www.messenger.com ), WhatsApp ( www.whatsapp.com ), Kik ( www.kik.com ), and WeChat ( www
.wechat.com ). Customers can chat with company representatives, get customer support, ask questions, get personalized recommen dations, and click to purchase, all from within messaging apps. Customers can interact with a human representative, a chatbot, or a combination of the two.
Chatbots (also known as bots) are interactive software pro grams that can have simple conversations with customers or other bots. Chatbots provide computer-generated chatting expe riences with human-like conversational and natural language abil ities. They function within boundaries that are constantly being
expanded with artificial intelligence techniques such as machine learning. Chatbots can take the form of voice-controlled assistants (such as Apple’s Siri or Microsoft’s Cortana) as well as customer ser vice representatives on the websites of many organizations.
Let’s take a look at Facebook Messenger and WeChat. The two messaging services are among the largest in the world.
· In April 2016, Facebook added chatbot-building capability to Messenger, used by more than 50 million businesses and 900 mil lion individuals. Facebook wants to drive Messenger users to the more than 15 million businesses with an official brand Facebook page. For example, to shop for a T-shirt on Facebook Messenger, users can send a text to Messenger to start a chat with the mobile shopping app Spring. The app will ask the shopper for their bud get and then display several possible T-shirts. If they do not like any of these choices, Spring will present more options.
Facebook Messenger bots can process purchases with out directing shoppers to a third party. The credit card in formation that shoppers have already stored in Facebook
· The 700 million people who each month use WeChat, China’s massive social media site, can use bots to chat with friends about an upcoming concert, buy tickets to the event, book a restaurant, split the check, and call a taxi. In addition to the individual WeChat users, 10 million Chinese businesses have an account. In fact, for some firms, the WeChat bot completely replaces an Internet site.
Many organizations use Chatbots to interact with cus tomers. Consider these examples:
· Traditional banks with an online presence tend to have difficult processes for customers to reach customer-service represen tatives—generally through a call or live chat. In India, Digibank ( www.dbs.com/digibank/in/ ) has taken a different approach. The bank, which is available to its customers only through mo bile, communicates using chatbots that can answer thousands of questions sent from customers through chat. Clients can also enter a command such as “Pay Fred $25 for movie tickets” and it will ask for more details. For example, if transferring the funds to Fred would put their account in the red, the bot notifies the customer and asks if they would like to top up their account.
· The Royal Bank of Scotland ( www.rbs.com ) is using a bot called “Luvo” to provide basic customer service. The bank wants Luvo to relieve staff from having to deal with easily answered questions so they can spend their time on more complex and value-added issues. RBS built Luvo using IBM’s Watson Conversation tool, which can understand human lan guage. As a result, customers can describe what they are look ing for instead of just choosing from standard options.
· The Whole Foods ( www.wholefoodsmarket.com ) bot on Messenger asks users to input an ingredient and then sug gests recipes.
· Using a bot on Kik, Sephora ( www.sephora.com ) asks shop pers to input their skin type and then suggests beauty tips and products tailored to their needs.
· Apparel manufacturer Everlane ( www.everlane.com ) uses a bot so customers can track their orders.
· KLM Royal Dutch Airlines has a bot on Messenger to let travel ers check in, receive flight information, change their tickets, and chat with customer service agents.
· HealthTap ( www.healthtap.com ) uses a bot that enables cli ents to reach more than 100,000 doctors 24/7, through secure video or text chat. The bot prompts users to type queries into Messenger to view free answers from physicians and see simi lar questions posed by other users.
· Sentient Technologies ( www.sentient.ai ) has developed a chatbot for online shopping that does not just provide a list of recommendations like Amazon does, but also enables cus tomers to have a real-time dialogue with the bot. Sentient’s first retail partnership is with Shoes.com.
· Taco Bell ( www.tacobell.com ) developed the TacoBot that processes orders at its locations. The TacoBot understands orders and manages them. Furthermore, TacoBot stores
customers’ order histories and makes recommendations based on what time it is.
· California startup X2AI ( https://x2.ai/ ) has developed a chat- bot that functions as a therapeutic assistant. The bot, called Karim, provides help and support to patients, not treatment. Human therapists oversee each session with patients and can take over the interaction at any time. X2AI designed Karim to help refugees from Middle East conflicts because it is impossi ble to meet their needs in conventional ways, such as sending thousands of Arabic-speaking therapists into war-torn areas.
Many organizations use chatbots internally. Let’s take a look at Overstock ( www.overstock.com ). Overstock’s chatbot, called Mila, automates the simple but time-consuming process of re questing sick leave. Previously, on any given day, the company’s call center in Salt Lake City could experience dozens of employees who felt ill. When that happened, the messages were checked man ually and the employee’s manager was alerted, and the employee was replaced.
Now if workers are not feeling well, they notify Mila. The bot sends a message to the person’s manager. Ill employees can now be replaced faster, which saves money. Mila provides other em ployee self-serve functions, such as requesting holidays, updating their schedules, and other actions that used to require interacting with a human.
There can be negative consequences of using chatbots. For ex ample, Microsoft launched a chatbot called Tay that was supposed to seem like a teenage girl and interact with people on Twitter. In the first day of her appearance on Twitter, Tay had signed up more than 50,000 followers and generated almost 100,000 tweets. But then she began mimicking her followers. Microsoft had to quickly remove the chatbot when users taught her to say racist and other very offensive words.
Sources: Compiled from N. Romeo, “The Chatbot Will See You Now,” The New Yorker, December 25, 2016; C. Kuang, “The Talking Cure,” Fast Compa ny, November 2016; W. Knight, “Chatbots with Social Skills Will Convince You to Buy Something,” MIT Technology Review, October 26, 2016; C. Cran dell, “Chatbots Will Be Your New Best Friend,” Forbes, October 23, 2016;
C. Waxer, “Get Ready for the Bot Revolution,” Computerworld, October 17, 2016; O. Williams-Grut, “RBS Is Launching an A.I. Chatbot Called ‘Luvo’ to Help Customers,” Business Insider, September 28, 2016; J. Constine, “Face book Messenger Now Allows Payments in Its 30,000 Chatbots,” TechCrunch, September 12, 2016; B. Botelho, “AI Chatbot Apps to Infiltrate Businesses Sooner Than You Think,” TechTarget, August 31, 2016; W. Knight, “The HR Person at Your Next Job May Actually Be a Bot,” MIT Technology Review, Au gust 3, 2016; S. Brewster, “Do Your Banking with a Chatbot,” MIT Technology Review, May 17, 2016; “Chatbots to Take ‘HR Role’ at Overstock.com,” Gad gets Now Beta, May 9, 2016; D. Bass, “Moving Merchandise Via Chats and Bots,” Bloomberg BusinessWeek, May 2–8, 2016; W. Knight, “How to Prevent a Plague of Dumb Chatbots,” MIT Technology Review, April 18, 2016; T. Peter son, “Inside the Making of Taco Bell’s Artificially Intelligent, Drunk-Tolerant TacoBot,” Martech Today, April 14, 2016; J. Guynn, “‘Chatbots’ Ready to Take Aim at Big Money,” USA Today, April 13, 2016; M. Isaac, “A Shopping Buddy, Via Chat,” New York Times, April 12, 2016; N. Heath, “Satya Nadella: Software Bots Will Be as Big as Mobile Apps,” TechRepublic, April 4, 2016; H. Reese, “Why Microsoft’s ‘Tay’ AI Bot Went Wrong,” TechRepublic, March 24, 2016; R. Metz, “Why Microsoft Accidentally Unleashed a Neo-Nazi Sexbot,” MIT Technology Review, March 24, 2016.
Questions
1. Discuss how the capabilities of chatbots are expanding through the use of machine learning. (Hint: See Technology Guide 4.)
2. Describe how your university could use a chatbot in its ad missions process.
systems also have online product-building features, called configurators, that enable custom ers to model the product to meet their specific needs. For example, you can customize your own running shoe at NikeID ( http://nikeid.nike.com ). Finally, many current SFA systems enable the salesperson in the field to connect remotely with customers and the home office through web-based interfaces on their smartphones.
Marketing. Thus far, you have focused primarily on how sales and customer service per sonnel can benefit from CRM systems. However, CRM systems have many important applica tions for an organization’s marketing department as well. For example, they enable marketers to identify and target their best customers, to manage marketing campaigns, and to generate quality leads for the sales teams. CRM marketing applications can also sift through volumes of customer data—a process known as data mining (discussed in Chapter 12)—to develop a pur chasing profile; that is, a snapshot of a consumer’s buying habits that may lead to additional sales through cross-selling, upselling, and bundling.
Cross-selling is the marketing of additional related products to customers based on a pre vious purchase. This sales approach has been used very successfully by banks. For example, if you have a checking and savings account at your bank, then a bank officer will recommend other products for you, such as certificates of deposit (CDs) or other types of investments.
Upselling is a strategy in which the salesperson provides customers with the opportunity to purchase related products or services of greater value in place of, or along with, the consum er’s initial product or service selection. For example, if a customer goes into an electronics store to buy a new television, a salesperson may show him a pricey 1080i HD LED television placed next to a less expensive LCD television in the hope of selling the more expensive set (assuming that the customer is willing to pay more for a sharper picture). Other common examples of up- selling are warranties on electronics merchandise and the purchase of a carwash after buying gas at a gas station.
Finally, bundling is a form of cross-selling in which a business sells a group of products or services together at a lower price than their combined individual prices. For example, your cable company might bundle cable TV, broadband Internet access, and telephone service at a lower price than you would pay for each service separately.
Campaign Management. Campaign management applications help organizations plan campaigns that send the right messages to the right people through the right channels. Organizations manage their campaigns very carefully to avoid targeting people who have opted out of receiving marketing communications. Furthermore, companies use these applica tions to personalize individual messages for each particular customer.
Customer-Touching Applications
Corporations have used manual CRM systems for many years. In the mid-1990s, for example, or ganizations began to use the Internet, the web, and other electronic touch points (e.g., e-mail, point-of-sale terminals) to manage customer relationships. In contrast with customer-facing applications, through which customers deal with a company representative, customers who use these technologies interact directly with the applications themselves. For this reason, these applications are called customer-touching CRM applications or electronic CRM (e-CRM) applications. Customers typically can use these applications to help themselves. There are many types of e-CRM applications. Let’s examine some of the major ones.
Search and Comparison Capabilities. It is often difficult for customers to find what they want from the vast array of products and services available on the web. To assist customers, many online stores and malls offer search and comparison capabilities, as do inde pendent comparison websites (see www.mysimon.com ).
Technical and Other Information and Services. Many organizations offer per sonalized experiences to induce customers to make purchases or to remain loyal. For example,
websites often allow customers to download product manuals. One example is General Elec tric’s website ( www.ge.com ), which provides detailed technical and maintenance information and sells replacement parts to customers who need to repair outdated home appliances. An other example is Goodyear’s website ( www.goodyear.com ), which provides information about tires and their use.
Customized Products and Services. Another customer-touching service that many online vendors use is mass customization, a process through which customers can con figure their own products. For example, Dell ( www.dell.com ) allows customers to configure their own computer systems. The Gap ( www.gap.com ) enables customers to “mix and match” an en tire wardrobe. Websites such as Hitsquad ( www.hitsquad.com ) and Apple’s iTunes (www.apple
.com/itunes) allow customers to pick individual music titles from a library and customize a CD, a feature that traditional music stores do not offer.
Customers now also view account balances or check the shipping status of orders at any time from their computers or smartphones. If you order books from Amazon, for example, you can look up the anticipated arrival date. Many other companies, including FedEx and UPS, pro vide similar services (see www.fedex.com and www.ups.com ).
Personalized Web Pages. Many organizations permit their customers to create per sonalized web pages. Customers use these pages to record purchases and preferences, as well as problems and requests. For example, American Airlines generates personalized web pages for each of its registered travel-planning customers.
FAQs. Frequently asked questions (FAQs) are a simple tool for answering repetitive cus tomer queries. Customers may find the information they need by using this tool, thereby elim inating the need to communicate with an actual person.
E-mail and Automated Response. The most popular tool for customer service is e-mail. Inexpensive and fast, companies use e-mail not only to answer customer inquiries but also to disseminate information, send alerts and product information, and conduct correspon dence on any topic.
Loyalty Programs. Loyalty programs recognize customers who repeatedly use a ven dor’s products or services. Loyalty programs are appropriate when two conditions are met: a high frequency of repeat purchases, and limited product customization for each customer.
Although loyalty programs are frequently referred to as “rewards programs,” their ac tual purpose is not to reward past behavior, but, rather, to influence future behavior. Signifi cantly, the most profitable customers are not necessarily those whose behavior can be most easily influenced. As one example, most major U.S. airlines provide some “elite” benefits to anyone who flies 25,000 miles with them and their partners over the course of a year. Cus tomers who fly first class pay much more for a given flight than those who fly in economy class.
Nevertheless, these customers reach elite status only 1.5 to 2 times faster than economy- class passengers. Why is this true? The reason is that, although first-class passengers are far more profitable than discount seekers, they also are less influenced by loyalty programs. Dis count flyers respond much more enthusiastically to the benefits of frequent flyer programs. Therefore, airlines award more benefits to discount flyers than to first-class flyers (relative to their spending).
The airlines’ frequent flyer programs are probably the best-known loyalty programs. Other popular loyalty programs are casino players’ clubs, which reward frequent players, and super markets, which reward frequent shoppers. Loyalty programs use a database or data warehouse to maintain a record of the points (or miles) a customer has accrued and the rewards to which he or she is entitled. The programs then use analytical tools to mine the data and learn about customer behavior.
Analytical CRM Systems. Analytical CRM systems provide business intelligence by analyzing customer behavior and perceptions. (Note: We discuss analytics in detail in Chapter 12.) For example, analytical CRM systems typically provide information concerning customer requests and transactions, as well as customer responses to the organization’s marketing, sales, and service initiatives. These systems also create statistical models of customer behav ior and the value of customer relationships over time, as well as forecasts about acquiring, retaining, and losing customers.
Important technologies in analytical CRM systems include data warehouses, data mining, decision support, and other business intelligence technologies. After these systems have com pleted their various analyses, they supply information to the organization in the form of reports and digital dashboards.
Analytical CRM systems analyze customer data for a variety of purposes, including:
· Designing and executing targeted marketing campaigns
· Increasing customer acquisition, cross-selling, and upselling
· Providing input into decisions relating to products and services (e.g., pricing and product development)
· Providing financial forecasting and customer profitability analysis
Figure 11.3 illustrates the relationship between operational CRM systems and analytical CRM systems.
FIGURE 11.3 The relationship between operational CRM and analytical CRM.
Before you go on. . .
1. Differentiate between customer-facing applications and customer-touching applications.
2. Provide examples of cross-selling, upselling, and bundling (other than the examples presented in the text).
Other Types of Customer Relationship Management Systems
11.3
Now that you have examined operational and analytical CRM systems, let’s shift our focus to other types of CRM systems. Five exciting developments in this area are on-demand CRM sys tems, mobile CRM systems, open-source CRM systems, social CRM, and real-time CRM.
On-Demand CRM Systems
Customer relationship management systems may be implemented as either on-premise or on-demand. Traditionally, organizations used on-premise CRM systems, meaning that they pur chased the systems from a vendor and then installed them on site. This arrangement was ex pensive, time consuming, and inflexible. Some organizations, particularly smaller ones, could not justify the costs of these systems.
On-demand CRM systems became a solution for the drawbacks of on-premise CRM sys tems. An on-demand CRM system is one that is hosted by an external vendor in the vendor’s data center. This arrangement spares the organization the costs associated with purchasing the system. Because the vendor creates and maintains the system, the organization’s employees also need to know how to access it and use it. The concept of on-demand is also known as utility computing or software-as-a-service (SaaS) (see Technology Guide 3).
Salesforce ( www.salesforce.com ) is the best-known on-demand CRM vendor. The compa ny’s goal is to provide a new business model that allows companies to rent the CRM software instead of buying it. The secret to their success appears to be that CRM has common require ments applicable to many customers. Consequently, Salesforce’s product meets the demands of its customers without a great deal of customization.
One Salesforce customer is Babson College ( www.babson.edu ) in Wellesley, Massachusetts. Babson’s goal is to deliver the best applicant experience possible. To accomplish this mission, the school decided to use Salesforce to bring together all of the information on prospective students in a single location. All personnel who are involved with admissions have immediate access to candidate contact information, applications, and reports that indicate the status of each applicant within the enrollment process. This system makes it easy for administrators to deliver valuable information to applicants at the right time.
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Using the Salesforce platform, Babson built an admissions portal with a fully personalized user experience for prospective students. The portal consolidates all of the infor mation that potential students need. Furthermore, it displays different information to students at different points in the application process.
Despite their benefits, on-demand CRM systems have potential problems. First, the vendor could prove to be unreliable, in which case the client company would have no CRM function ality at all. Second, hosted software is difficult or impossible to modify, and only the vendor can upgrade it. Third, vendor-hosted CRM software may be difficult to integrate with the orga nization’s existing software. Finally, giving strategic customer data to vendors always carries security and privacy risks.
Mobile CRM Systems
A mobile CRM system is an interactive system that enables an organization to conduct commu nications related to sales, marketing, and customer service activities through a mobile medium for the purpose of building and maintaining relationships with its customers. Simply put, mobile CRM systems involve interacting directly with consumers through portable devices such as smart- phones. Many forward-thinking companies believe that mobile CRM systems have tremendous po tential to create personalized customer relationships that may be accessed anywhere and at any time. In fact, the opportunities offered by mobile marketing appear so rich that many companies have already identified mobile CRM systems as a cornerstone of their future marketing activities.
320 CHAPTER 11 Customer Relationship Management and Supply Chain Management
Open-Source CRM Systems
Other Types of Customer Relationship Management Systems 321
As explained in Technology Guide 2, the source code for open-source software is available at no cost. Open-source CRM systems, therefore, are CRM systems whose source code is available to developers and users.
Open-source CRM systems provide the same features or functions as other CRM software, and they may be implemented either on-premise or on-demand. Leading open-source CRM vendors include SugarCRM ( www.sugarcrm.com ), Concursive ( www.concursive.com ), and Vtiger ( www.vtiger.com ).
The benefits of open-source CRM systems include favorable pricing and a wide variety of applications. These systems are also easy to customize. This is an attractive feature for organi zations that need CRM software that is designed for their specific needs. Finally, updates and bug (software error) fixes for open-source CRM systems are rapidly distributed, and extensive support information is available free of charge.
Like all software, however, open-source CRM systems have certain risks. The most serious risk involves quality control. Because open-source CRM systems are created by a large commu nity of unpaid developers, there is sometimes no central authority responsible for overseeing the quality of the product. (We discuss open-source software in Technology Guide 2). Further more, for best results, companies must have the same IT platform in place as the one on which the open-source CRM system was developed.
Social CRM
Social CRM is the use of social media technology and services to enable organizations to en gage their customers in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent manner. Social CRM is the company’s response to the cus tomers’ ownership of this two-way conversation. In social CRM, organizations monitor services such as Facebook, Twitter, and LinkedIn (among many others) for relevant mentions of their products, services, and brand, and they respond accordingly.
Social media are also providing methods that customers are using to obtain faster, better customer service. Morton’s Steakhouse certainly put social media to good use in surprising a customer.
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A corporate manager was in meetings all day, and he had to take a later flight home that caused him to miss his dinner. So, he jokingly tweeted Morton’s Steakhouse ( www.mortons
.com ) and requested that the restaurant show up with a steak when he landed.
Morton’s saw the Tweet, discovered that the tweeter was a frequent customer (and fre quent tweeter—he had 100,000 Twitter followers), pulled data on what he typically ordered, identified the flight he was on, and then sent a delivery person to Newark Airport (New Jersey) to serve him his dinner. When he got to the reception lobby at the airport, he noticed a man in a tuxedo holding a card with his name. The man was also carrying a bag that contained a Por terhouse steak, shrimp, potatoes, bread, two napkins, and silverware.
The nearest Morton’s restaurant was 24 miles from the airport, and the manager’s flight took only two hours. This scenario says a lot about both Morton’s customer service and the speed of social media. Admittedly, the entire scenario was a publicity stunt that went explo sively viral over the Internet. This is not the point, however. The questions that businesses should be asking themselves are: Would your company even consider doing something like this? If not, why not?
Real-Time CRM
Organizations are implementing real-time customer relationship management to provide a superior level of customer satisfaction for today’s always-on, always-connected, more knowl edgeable, and less loyal customers. Real-time CRM means that organizations are able to re spond to customer product searches, requests, complaints, comments, ratings, reviews, and
recommendations in near real-time, 24/7/365. Southwest Airlines provides an excellent exam ple of real-time CRM.
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A passenger was in her seat on a Southwest Airlines flight about to take off, when the plane turned back to the gate. A flight attendant asked her to get off the plane. When she checked with the Southwest agent at the desk inside the terminal, he told her that her son was in a coma after suffering a head injury and to call her husband.
Even before she had disembarked, Southwest had rebooked her on the next nonstop flight to her son’s city—free of charge. The airline offered her a private waiting area, rerouted her lug gage, allowed her to board first, and packed a lunch for her. Moreover, the airline delivered her luggage to where she was going to stay and called her to ask about her son. The woman said that her son was recovering and that she could not be more grateful for the way she was treated. Southwest Airlines went above and beyond their responsibilities after they learned of the son’s accident. Details were not available about how the airline learned of the son’s accident,
but it is clear that Southwest brought customer relationship management to a new level.
Before you go on. . .
1. Describe on-demand CRM.
3. Describe open-source CRM.
4. Describe social CRM.
5. Describe real-time CRM.
Supply Chains
11.4
Modern organizations are increasingly concentrating on their core competencies and on be coming more flexible and agile. To accomplish these objectives, they rely on other companies rather than on companies they themselves own, to supply the goods and services they need. Organizations recognize that these suppliers can perform these activities more efficiently and effectively than they themselves can. This trend toward relying on an increasing number of suppliers has led to the concept of supply chains. A supply chain is the flow of materials, infor mation, money, and services from raw material suppliers, through factories and warehouses, to the end customers. A supply chain also includes the organizations and processes that create and deliver products, information, and services to the end customers.
Supply chains enhance trust and collaboration among supply chain partners, thus improv ing supply chain visibility and inventory velocity. Supply chain visibility refers to the ability of all organizations within a supply chain to access or view relevant data on purchased materials as these materials move through their suppliers’ production processes and transportation net works to their receiving docks. Organizations can also access or view relevant data on outbound goods as they are manufactured, assembled, or stored in inventory and then shipped through their transportation networks to their customers’ receiving docks. The more quickly a company can deliver products and services after receiving the materials required to make them—that is, the higher the inventory velocity—the more satisfied the company’s customers will be.
Supply chain information has historically been obtained by manual, labor-based tracking and monitoring, but is now increasingly being generated by sensors, RFID tags, meters, GPS, and other devices and systems. How does this transformation affect supply chain managers? For one thing, they now have real-time information on all products moving through their sup ply chains. Supply chains will therefore rely less on labor-based tracking and monitoring, be cause the new technology will allow shipping containers, trucks, products, and parts to report on their own status. The overall result is a vast improvement in supply chain visibility.
322 CHAPTER 11 Customer Relationship Management and Supply Chain Management
Supply Chains 323
Supply chains are a vital component of the overall strategies of many modern organiza tions. To use supply chains efficiently, a business must be tightly integrated with its suppliers, business partners, distributors, and customers. A critical component of this integration is the use of information systems to facilitate the exchange of information among the participants in the supply chain.
The Structure and Components of Supply Chains
The term supply chain comes from a picture of how the partnering organizations are linked. Figure 11.4 illustrates a typical supply chain. (Recall that Figure 1.5 also illustrated a supply chain, in a slightly different way.) Note that the supply chain involves three segments:
1. Upstream, where sourcing or procurement from external suppliers occurs.
In this segment, supply chain managers select suppliers to deliver the goods and services the company needs to produce its product or service. Furthermore, SC manag ers develop the pricing, delivery, and payment processes between a company and its suppliers. Included here are processes for managing inventory, receiving and verifying shipments, transferring goods to manufacturing facilities, and authorizing payments to suppliers.
2. Internal, where packaging, assembly, or manufacturing takes place.
SC managers schedule the activities necessary for production, testing, packaging, and preparing goods for delivery. They also monitor quality levels, production output, and worker productivity.
3. Downstream, where distribution takes place, frequently by external distributors.
In this segment, SC managers coordinate the receipt of orders from customers, develop a network of warehouses, select carriers to deliver products to customers, and implement invoic ing systems to receive payments from customers.
The flow of information and goods can be bidirectional. For example, damaged or un wanted products can be returned, a process known as reverse flows or reverse logistics. In the retail clothing industry, for example, reverse logistics involves clothing that customers return, either because the item had defects or because the customer did not like the item.
Tiers of Suppliers. Figure 11.4 shows several tiers of suppliers. As the diagram indi cates, a supplier may have one or more subsuppliers, a subsupplier may have its own sub supplier(s), and so on. For an automobile manufacturer, for example, Tier 3 suppliers produce basic products such as glass, plastic, and rubber; Tier 2 suppliers use these inputs to make windshields, tires, and plastic moldings; and Tier 1 suppliers produce integrated components such as dashboards and seat assemblies.
The Flows in the Supply Chain. There are typically three flows in the supply chain: material, information, and financial. Material flows are the physical products, raw materials,
FIGURE 11.4 Generic supply chain.
supplies, and so forth that flow along the chain. Material flows also include the reverse flows discussed earlier. A supply chain thus involves a product life cycle approach, from “dirt to dust.” Information flows consist of data related to demand, shipments, orders, returns, and schedules, as well as changes in any of these data. Finally, financial flows involve money trans fers, payments, credit card information and authorization, payment schedules, e-payments,
and credit-related data.
Significantly, different supply chains have different numbers and types of flows. For exam ple, in service industries, there may be no physical flow of materials, but there is frequently a flow of information, often in the form of documents (physical or electronic copies). For exam ple, the digitization of software, music, and other content can create a supply chain without any physical flow. Notice, however, that in such a case there are two types of information flows: one that replaces materials flow (digitized software), and another that provides the supporting information (orders, billing, and so on). To manage the supply chain, an organization must co ordinate all three flows among all of the parties involved in the chain, a topic we turn to next.
Before you go on. . .
1. What is a supply chain?
2. Describe the three segments of a supply chain.
3. Describe the flows in a supply chain.
Supply Chain Management
11.5
The function of supply chain management (SCM) is to improve the processes a company uses to acquire the raw materials it needs to produce a product or service and then deliver that product or service to its customers. That is, supply chain management is the process of plan ning, organizing, and optimizing the various activities performed along the supply chain. There are five basic components of SCM:
1. Plan: Planning is the strategic component of SCM. Organizations must have a strategy for managing all the resources that are involved in meeting customer demand for their prod uct or service. Planning involves developing a set of metrics (measurable deliverables) to monitor the organization’s supply chain to ensure that it is efficient and it delivers high quality and value to customers for the lowest cost.
2. Source: In the sourcing component, organizations choose suppliers to deliver the goods and services they need to create their product or service. Supply chain managers develop pricing, delivery, and payment processes with suppliers, and they create metrics to mon itor and improve their relationships with their suppliers. They also develop processes for managing their goods and services inventory, including receiving and verifying shipments, transferring the shipped materials to manufacturing facilities, and authorizing supplier payments.
3. Make: This is the manufacturing component. Supply chain managers schedule the ac tivities necessary for production, testing, packaging, and preparation for delivery. This component is the most metric-intensive part of the supply chain, in which organizations measure quality levels, production output, and worker productivity.
4. Deliver: This component, often referred to as logistics, is in which organizations coordi nate the receipt of customer orders, develop a network of warehouses, select carriers to transport their products to their customers, and create an invoicing system to receive payments.
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5. Return: Supply chain managers must create a responsive and flexible network for receiving defective, returned, or excess products back from their customers, as well as for support ing customers who have problems with delivered products.
Like other functional areas, SCM uses information systems. The goal of SCM systems is to reduce the problems, or friction, along the supply chain. Friction can increase time, costs, and inventories and decrease customer satisfaction. SCM systems, therefore, reduce un certainty and risks by decreasing inventory levels and cycle time while improving business processes and customer service. These benefits make the organization more profitable and competitive.
Significantly, SCM systems are a type of interorganizational information system. In an interorganizational information system (IOS), information flows among two or more or ganizations. By connecting the IS of business partners, IOSs enable the partners to perform a number of tasks:
· Reduce the costs of routine business transactions
· Improve the quality of the information flow by reducing or eliminating errors
· Compress the cycle time involved in fulfilling business transactions
· Eliminate paper processing and its associated inefficiencies and costs
· Make the transfer and processing of information easier for users
One of the most important goals of SCM systems is to give an organization visibility into its supply chain. Supply chain visibility is the ability of an organization to track products in transit from the manufacturer to their final destination. The goal of SCV is to improve the supply chain by making data readily available to all parties in the supply chain. Supply chain visibility pro motes quick responses to problems or changes along the supply chain by enabling companies to shift products to where they are needed. IT’s About Business 11.4 illustrates how Apple improved its supply chain visibility.
IT’s About Business 11.4
Apple Deals with Supply Chain Strain and Faulty efficiency. With the Watch, one of the suppliers was not up to
Parts
standards. Production of the Watch was functioning well until
the company discovered that one of the key components, pro-
POM duced by a supplier, was faulty. That component was the taptic When Apple ( www.apple.com ) introduced the Apple Watch in April engine, which imparts forces or vibrations to the wearer to con 2015, industry analysts had a hard time predicting how consumers vey information.
would react. Their estimates ranged from 5 million to 40 million The taptic engine supplier was China-based AAC Tech- Watches sold in 2015. This huge range in a market forecast creates nologies (AAC; www.aactechnologies.com ), maker of several havoc in planning for a supply chain. In fact, industry experts call it common components in mobile devices. AAC was unable to meet supply chain strain. either Apple’s demand or its quality standards. The discovery So, how did Apple’s supply chain gear up for such a high de- of the faulty parts caused delays and other impacts on Apple’s
gree of sales uncertainty? First, the firm allowed a certain number other suppliers as well as its customers. Apple reportedly threw of Apple Watch preorders. Based on those preorders, Apple could out all of the defective Watches to avoid having to recall them. estimate the potential market demand and alter production ac- Apple then handed over production of the taptic engine to cordingly. Second, even after releasing the Watch, Apple accepted another partner, Nidec Corporation ( www.nidec.com ) of Japan. online orders for only a few months. By controlling the ordering Nidec manufactures parts for various industries including auto- and shipping itself, Apple had accurate data regarding demand. As motive, manufacturing, and consumer electronics. It took some a result, the company did not have to manage demand by stocking time for Nidec to begin making the taptic engines. While Nidec inventory on store shelves. was increasing production, Apple came under considerable
The plan went off without a hitch, until it stumbled. Ap- fire over the delayed Watch and extremely long-range shipping ple has multiple partners in its supply chain, and it holds all of times.
them to extremely high standards. For Apple’s supply chain to Once supply caught up with demand, Apple’s supply chain meet the demand for Watches, not to mention consumer ex- functioned as planned. Apple then lifted its web-only sales policy pectations of quality, all of its partners had to operate at peak and allowed the Watch to be sold in Apple Stores and other retail
Overall, Apple did an excellent job of creating a new product with some completely new components, developing a sales and marketing plan that would go hand in hand with its supply chain, recovering from a faulty component from a supply chain partner, and still getting their device to market with great consumer fanfare. Although not everyone will find a use for an Apple Watch, the one thing you do not see is complaints about its quality. That, too, is proof of Apple’s ability to maintain ultra-high-quality standards in the face of unknown demand and supply chain strain.
Interestingly, reports in May 2016 indicate that Apple has commissioned approximately 75 million iPhone 7s, setting a pro duction record. Foxconn ( www.foxconn.com ) is still the main manufacturer for the iPhone 7, with Pegatron ( www.pegatroncorp
.com ) assisting.
Sources: Compiled from “Apple, GE Suppliers Linked to Rebel-Held Myanmar Tin Mine,” Fortune, November 29, 2016; M. Wuerthele, “Supply Chain Reports Claim Apple Will Gain Smartphone Market Share in 2016, 2017,” Apple Insider, October 12, 2016; T. Bajarin, “Apple’s Little-Known (and Somewhat Unsexy) Secret to Success,” Recode, August 30, 2016; B.
Mayo, “Report: Apple Orders Supply Chain to Produce 72–78 Million iPhone 7 Units This Year, Significantly Above Analyst Estimates,” 9to5Mac, May 23, 2016; R. Castellano, “Apple’s Problem Is a Supply Chain Issue, But Who’s to Blame?” Seeking Alpha, April 27, 2016; G. Meyers, “Apple—A Supply Chain Model of Excellence,” Supply & Demand Chain Executive, June 4, 2015; “Apple Watch Shows Value of Strong Supply Chains, and Opportunity in Disruption,” supplychain247.com , May 28, 2015; “Has Supply Chain Strain Become the New Norm?” supplychain247.com , May 1, 2015; A. Cunningham, “Taptic Engine Component Responsible for Limited Apple Watch Supplies,” Ars Technica, April 29, 2015; D. Wakabayashi and L. Luk, “Apple Watch: Faulty Taptic Engine Slows Rollout,” Wall Street Journal, April 29, 2015;
R. Bowman, “Can Apple’s Supply Chain Handle the Apple Watch?” Supply Chain Brain, April 20, 2015; www.apple.com; http://www.aactechnologies
.com; http://www.nidec.com, accessed October 2, 2016.
Questions
1. Discuss the importance of forecasting demand for supply chain planning.
2. Explain how a defective component can disrupt the flow of materials through a supply chain and the impact that dis- ruption will have on various entities upstream and down stream along the supply chain.
The Push Model Versus the Pull Model
Many SCM systems employ the push model. In this model, also known as make-to-stock, the production process begins with a forecast, which is simply an educated guess as to customer demand. The forecast must predict which products customers will want and in what quantities. The company then produces the amount of products in the forecast, typically by using mass production, and sells, or “pushes,” those products to consumers.
Unfortunately, these forecasts are often incorrect. Consider, for example, an automobile manufacturer that wants to produce a new car. Marketing managers conduct extensive re search, including customer surveys and analyses of competitors’ cars, and then provide the results to forecasters. If the forecasters’ predictions are too high—that is, if they predict that customers will purchase a certain number of these new cars but actual demand falls below this amount—then the automaker has excess cars in inventory and will incur large carrying costs (the costs of storing unsold inventory). Furthermore, the company will probably have to sell the excess cars at a discount.
From the opposite perspective, if the forecasters’ predictions are too low—that is, actual customer demand exceeds expectations—then the automaker probably will have to run ex tra shifts to meet the demand, thereby incurring substantial overtime costs. Furthermore, the company risks losing business to its competitors if the car that customers want is not available. Thus, using the push model in supply chain management can cause problems, as you will see in the next section.
To avoid the uncertainties associated with the push model, many companies now employ the pull model of supply chain management, using web-enabled information flows. In the pull model, also known as make-to-order, the production process begins with a customer order. Therefore, companies make only what customers want, a process closely aligned with mass customization (discussed in Chapter 1).
A prominent example of a company that uses the pull model is Dell Computer. Dell’s pro duction process begins with a customer order. This order not only specifies the type of com puter the customer wants but also alerts each Dell supplier as to the parts of the order for which that supplier is responsible. That way, Dell’s suppliers ship only the parts that Dell needs to produce the computer.
Not all companies can use the pull model. Automobiles, for example, are far more compli cated and more expensive to manufacture than computers, so automobile companies require longer lead times to produce new models. Automobile companies do use the pull model, but
only for specific automobiles that some customers order (e.g., Rolls-Royce, Bentley, and other extremely expensive cars).
Problems Along the Supply Chain
As you saw earlier, friction can develop within a supply chain. One major consequence of fric tion is poor customer service. In some cases, supply chains do not deliver products or services when and where customers—either individuals or businesses—need them. In other cases, the supply chain provides poor quality products. Other problems associated with supply chain fric tion are high inventory costs and revenue loss.
The problems along the supply chain arise primarily from two sources: (1) uncertainties, and (2) the need to coordinate multiple activities, internal units, and business partners. A ma jor source of supply chain uncertainties is the demand forecast. Demand for a product can be influenced by numerous factors such as competition, price, weather conditions, technological developments, overall economic conditions, and customers’ general confidence. Another un certainty is delivery times, which can be affected by numerous factors ranging from production machine failures to road construction and traffic jams. Quality problems in materials and parts can also create production delays, which also generate supply chain problems.
One major challenge that managers face in setting accurate inventory levels throughout the supply chain is known as the bullwhip effect. The bullwhip effect refers to erratic shifts in orders up and down the supply chain (see Figure 11.5). Basically, the variables that affect customer demand can become magnified when they are viewed through the eyes of managers at each link in the supply chain. If each distinct entity that makes ordering and inventory de cisions places its interests above those of the chain, then stockpiling can occur at as many as seven or eight locations along the chain. Research has shown that in some cases such hoarding has led to as much as a 100-day supply of inventory that is waiting “just in case,” versus the 10- to 20-day supply manufacturers normally keep at hand.
Solutions to Supply Chain Problems
Supply chain problems can be very costly. Therefore, organizations are motivated to find inno vative solutions. During the oil crises of the 1970s, for example, Ryder Systems, a large trucking company, purchased a refinery to control the upstream part of the supply chain and to ensure it had sufficient gasoline for its trucks. Ryder’s decision to purchase a refinery is an example of vertical integration. Vertical integration is a business strategy in which a company purchases its upstream suppliers to ensure that its essential supplies are available as soon as the com pany needs them. Ryder later sold the refinery because it could not manage a business it did not understand and because oil became more plentiful.
Ryder’s decision to vertically integrate was not the best method for managing its supply chain. In the remainder of this section, you will look at some other possible solutions to supply chain problems, many of which are supported by IT.
FIGURE 11.5 The bullwhip effect.
One widely used strategy to minimize inventories is the just-in-time (JIT) inventory sys tem. Essentially, JIT systems deliver the precise number of parts, called work-in-process inven tory, to be assembled into a finished product at precisely the right time.
Although JIT offers many benefits, it has certain drawbacks as well. To begin with, suppli ers are expected to respond instantaneously to requests. As a result, they have to carry more inventory than they otherwise would. In this sense, JIT does not eliminate excess inventory; rather, it simply shifts it from the customer to the supplier. This process can still reduce the overall inventory size if the supplier can spread the increased inventory over several customers. However, that is not always possible.
JIT also replaces a few large supply shipments with a large number of smaller ones. In terms of transportation, then, the process is less efficient.
Information Sharing. Another common approach to solving supply chain problems, and especially to improving demand forecasts, is sharing information along the supply chain. Information sharing can be facilitated by electronic data interchange and extranets, topics you will learn about in the next section.
One notable example of information sharing occurs between large manufacturers and re tailers. For example, Walmart provides Procter & Gamble with access to daily sales informa tion from every store for every item that P&G makes for Walmart. This access enables P&G to manage the inventory replenishment for Walmart’s stores. By monitoring inventory levels, P&G knows when inventories fall below the threshold for each product at any Walmart store. These data trigger an immediate shipment.
Information sharing between Walmart and P&G is executed automatically. It is part of a vendor-managed inventory strategy. Vendor-managed inventory (VMI) occurs when the supplier, rather than the retailer, manages the entire inventory process for a particular product or group of products. Significantly, P&G has similar agreements with other ma jor retailers. The benefit for P&G is accurate and timely information on consumer demand for its products. Thus, P&G can plan production more accurately, minimizing the bullwhip effect.
Before you go on. . .
1. Differentiate between the push model and the pull model.
2. Describe various problems that can occur along the supply chain.
3. Discuss possible solutions to problems along the supply chain.
Information Technology Support for Supply Chain Management
11.6
Clearly, SCM systems are essential to the successful operation of many businesses. As you have seen, these systems—and IOSs in general—rely on various forms of IT to resolve problems. Three technologies, in particular, provide support for IOSs and SCM systems: electronic data in terchange, extranets, and web services. You will learn about web services in Technology Guide
3. In this section, you examine the other two technologies.
Electronic Data Interchange (EDI)
Electronic data interchange (EDI) is a communication standard that enables business part ners to exchange routine documents, such as purchasing orders, electronically. EDI formats these documents according to agreed-upon standards (e.g., data formats). It then transmits messages over the Internet using a converter, called translator.
EDI provides many benefits that are not available with a manual delivery system. To begin with, it minimizes data entry errors, because each entry is checked by the computer. The length of the message can also be shorter, and the messages are secured. EDI also re duces cycle time, increases productivity, enhances customer service, and minimizes paper usage and storage. Figure 11.6 contrasts the process of fulfilling a purchase order with and without EDI.
FIGURE 11.6 Comparing purchase order (PO) fulfillment with and without EDI.
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FIGURE 11.7 The structure of an extranet.
EDI does have some disadvantages. Business processes must sometimes be restructured to fit EDI requirements. Also, there are many EDI standards in use today, so one company might have to use several standards to communicate with multiple business partners.
In today’s world, in which every business has a broadband connection to the Internet and where multi-megabyte design files, product photographs, and PDF sales brochures are rou tinely e-mailed, the value of reducing a structured e-commerce message from a few thousand XML bytes to a few hundred EDI bytes is negligible. As a result, EDI is being replaced by XML- based web services. (You will learn about XML in Technology Guide 3.)
Extranets
To implement IOSs and SCM systems, a company must connect the intranets of its various busi ness partners to create extranets. Extranets link business partners over the Internet by provid ing them access to certain areas of each other’s corporate intranets (see Figure 11.7).
The primary goal of extranets is to foster collaboration between and among business partners. A business provides extranet access to selected B2B suppliers, customers, and other partners. These individuals access the extranet through the Internet. Extranets enable people located outside a company to collaborate with the company’s internal employees. They also allow external business partners to enter the corporate intranet, through the Internet, to access data, place orders, check the status of those orders, communicate, and collaborate. Finally, they make it possible for partners to perform self-service activities such as checking inventory levels.
Extranets use virtual private network (VPN) technology to make communication over the Internet more secure. The major benefits of extranets are faster processes and information flow, improved order entry and customer service, lower costs (e.g., for communications, travel, and administrative overhead), and overall improved business effectiveness.
There are three major types of extranets. The type that a company chooses depends on the business partners involved and the purpose of the supply chain. We present each type next, along with its major business applications.
A Company and Its Dealers, Customers, or Suppliers. This type of ex tranet centers on a single company. An example is the FedEx extranet, which allows customers to track the status of a delivery. Customers use the Internet to access a database on the FedEx intranet. Enabling customers to monitor deliveries saves FedEx the cost of hiring human oper ators to perform that task over the phone. IT’s About Business 11.5 illustrates how Caribou Coffee works with its supplier network to provide 100 percent Rainforest Alliance Certified coffee.
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IT’s About Business 11.5
Caribou Coffee Brews A Strong Supply Chain
POM
Food products have complicated supply chains due to the perishable nature of the raw materials. Such is the case for coffee. A typical coffee supply chain includes growers, intermediaries (who buy directly from the growers and resell to other intermediaries, processors, or dealers), processors, government regulators, ex porters, brokers, roasters, distributors, and retailers. For a startup coffee-roasting company, managing the beans, seasons, regula tions, partners, suppliers, distributors, and online store can be a daunting task. Despite this complexity, many companies begin managing their supply chain with a simple spreadsheet.
Such is the case with Caribou Coffee ( www.cariboucoffee
.com ), a coffee chain based in Minneapolis, Minnesota, that has retail outlets as well as a wholesale business. In the early days, Car ibou had about 200 independent stockkeeping units (SKUs), which it could track on a spreadsheet. As business increased, that num ber quickly reached 600 SKUs. At that point Caribou realized that it needed a more sophisticated software.
MKT Part of the reason for this rapid growth was positive re
action from customers to the fact that Caribou Coffee was the first major U.S. retailer to sell only Rainforest Alliance Certified coffee and espresso. To achieve this certification, farms must follow rig orous environmental, social, and economic standards to achieve long-term sustainability. The goal is to conserve wildlife and eco systems and protect workers, their families, and local communities. While having Rainforest Alliance–certified products boosted sales for Caribou, it also meant more work in the procurement pro cess. Caribou does not simply buy beans from a distributor without knowing little to nothing of the source. Instead, Caribou brokers must certify that they are 100 percent Rainforest Alliance Certified. Caribou was experiencing growing pains with its internal demand forecasting and subsequent downstream supply chain management. Caribou turned to Logility ( www.logility.com ) to implement Voyager, a cloud-based supply chain management
system. The service provides accurate, real-time data that the company uses to improve their forecasting and to provide better customer service to their retailers.
Logility’s Voyager was the right solution for Caribou. The com pany saved time and money by using an off-the-shelf cloud-based system that meant it didn’t have to develop in-house expertise or buy expensive technology. Voyager was deployed quickly with little up-front investment. The solution also increased Caribou’s ROI.
What were the payoffs for Caribou in ditching its spreadsheet for the Voyager service? It increased the accuracy of its demand forecasts, moved to weekly planning instead of monthly, reduced the spoilage of its perishable inventory, and accelerated its inventory turnover by one-third. (Inventory turnover is the number of times that inventory is sold or used in a particular time period, usually one year. The higher the number, the better.) The implementation of this supply chain management system was a huge success for Caribou Coffee, which can focus on what they do best . . . provide a nice cup of coffee!
Sources: Compiled from J. Hardcastle, “Why Sustainable Supply Chains Matter,” Environmental Leader, September 20, 2016; A. Wieland, “The State of Sustainable Supply Chains,” Supply Chain Management Research,
August 31, 2016; K. Loria, “A Coffee Shortage Is Looming—Here’s How Soon It Could Be Extinct,” Business Insider, August 30, 2016; “New MIT Report Highlights Coffee Supply Problem,” Strategic Sourceror, May 12, 2016; “Caribou Coffee Shares Its Journey to Supply Chain Success,” Logility, June 17, 2014; “Logility Announces 2014 Leadership Award Winners,” Logility, May 1, 2014; A. Grackin and B. McBeath, “Caribou Coffee’s Supply Chain Story,” ChainLink Research, January 22, 2013; www.logility.com, accessed October 3, 2016; “Coffee—The Supply Chain,” businesscasestudies.co.uk, accessed October 3, 2016; www.rainforest-alliance.org, accessed October 3,
2016; www.cariboucoffee.com, accessed October 3, 2015.
Questions
1. Discuss the difficulties involved in the supply chain for any perishable item.
2. Describe the complex issues Caribou Coffee dealt with to ensure their supply chain was not only Rainforest Alliance Certified but also meeting their business needs.
An Industry’s Extranet. Just as a single company can set up an extranet, the major players in an industry can team up to create an extranet that will benefit all of them. For ex ample, ANXeBusiness ( www.anx.com ) enables companies to collaborate effectively through a network that provides a secure global medium for B2B information exchange. This network is used for mission-critical business transactions by leading international organizations in aero space, automotive, chemical, electronics, financial services, healthcare, logistics, manufactur ing, transportation, and related industries. It offers customers a reliable extranet as well as VPN services.
Joint Ventures and Other Business Partnerships. In this type of extranet, the partners in a joint venture use the extranet as a vehicle for communication and collabora tion. An example is Bank of America’s extranet for commercial loans. The partners involved in making these loans include a lender, a loan broker, an escrow company, and a title company. The extranet connects lenders, loan applicants, and the loan organizer, Bank of America. A sim ilar case is Lending Tree ( www.lendingtree.com ), a company that provides mortgage quotes for homeowners and also sells mortgages online. Lending Tree uses an extranet for its business partners (e.g., the lenders).
Portals and Exchanges
As you saw in Chapter 6, corporate portals offer a single point of access through a web browser to critical business information in an organization. In the context of B2B supply chain manage ment, these portals enable companies and their suppliers to collaborate very closely.
There are two basic types of corporate portals: procurement (sourcing) portals for a company’s suppliers (upstream in the supply chain), and distribution portals for a company’s customers (downstream in the supply chain). Procurement portals automate the business processes involved in purchasing or procuring products between a single buyer and multiple suppliers. For example, Boeing has deployed a procurement portal called the Boeing Supplier Portal through which it conducts business with its suppliers. Distribution portals automate the business processes involved in selling or distributing products from a single supplier to multiple buyers. For example, Dell services its business customers through its distribution por tal at http://premier.dell.com .
Before you go on. . .
1. Define EDI, and list its major benefits and limitations.
2. Define an extranet, and explain its infrastructure.
3. List and briefly define the major types of extranets.
4. Differentiate between procurement portals and distribution portals.
What’s in IT for me?
ACCT For the Accounting Major
Customer Relationship Management. CRM systems can help companies establish controls for financial reporting related to interactions with customers in order to support compliance with legislation. For example, Sarbanes–Oxley requires companies to establish and maintain an adequate set of controls for accurate financial reporting that can be audited by a third party. Other sections [302 and 401(b)] have implications for customer activ ities, including the requirements that sales figures reported for the prior year be correct. Section 409 requires companies to re port material changes to financial conditions, such as the loss of a strategic customer or significant customer claims about product quality.
CRM systems can track document flow from a sales opportu nity to a sales order, to an invoice, to an accounting document, thus enabling finance and accounting managers to monitor the entire flow. CRM systems that track sales quotes and orders can be used to incorporate process controls that identify questionable sales transactions. CRM systems can provide exception-alert capabilities to identify instances outside defined parameters that put compa nies at risk.
Supply Chain Management. The cost accountant will play an important role in developing and monitoring the financial account ing information associated with inventory and cost of goods sold. In a supply chain, much of the data for these accounting require ments will flow into the organization from various partners within
the chain. It is up to the chief accountant, the comptroller or CFO, to prepare and review these data.
Going further, accounting rules and regulations and the cross-border transfer of data are critical for global trade. IOSs can facilitate such trade. Other issues that are important for accountants are taxation and government reports. Creating in formation systems that rely on EDI also requires the attention of accountants. Finally, fraud detection in global settings (e.g., transfers of funds) can be facilitated by appropriate controls and auditing.
FIN For the Finance Major
Customer Relationship Management. CRM systems allow com panies to track marketing expenses, collecting appropriate costs for each individual marketing campaign. These costs then can be matched to corporate initiatives and financial objectives, demon strating the financial impact of the marketing campaign.
Pricing is another key area that impacts financial reporting. For example, what discounts are available? When can a price be overridden? Who approves discounts? CRM systems can put con trols into place for these issues.
Supply Chain Management. In a supply chain, the finance ma jor will be responsible for analyzing the data created and shared among supply chain partners. In many instances, the financial an alyst will recommend actions to improve supply chain efficiencies and cash flow. This may benefit all the partners in the chain. These
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recommendations will be based on financial models that incorpo rate key assumptions such as supply chain partner agreements for pricing. Through the use of extensive financial modeling, the finan cial analyst helps to manage liquidity in the supply chain.
Many finance-related issues exist in implementing IOSs. For one thing, establishing EDI and extranet relationships involves structuring payment agreements. Global supply chains may in volve complex financial arrangements, which may have legal implications.
MKT For the Marketing Major
Customer Relationship Management. CRM systems are an in tegral part of every marketing professional’s work activities. CRM systems contain the consolidated customer data that provides the foundation for making informed marketing decisions. Using these data, marketers develop well-timed and targeted sales campaigns with customized product mixes and established price points that enhance potential sales opportunities and therefore increase rev enue. CRM systems also support the development of forecasting models for future sales to existing clients through the use of histor ical data captured from previous transactions.
Supply Chain Management. A tremendous amount of use ful sales information can be derived from supply chain partners through the supporting information systems. For example, many of the customer support activities take place in the downstream portion of the supply chain. For the marketing manager, an under standing of how the downstream activities of the supply chain re late to prior chain operations is critical.
Furthermore, a tremendous amount of data is fed from the supply chain supporting information systems into the CRM sys tems that are used by marketers. The information and a complete understanding of its genesis are vital for mixed-model marketing programs.
POM For the Production/Operations Management Major
Customer Relationship Management. Production is heavily in volved in the acquisition of raw materials, conversion, and distri bution of finished goods. However, all of these activities are driven by sales. Increases or decreases in the demand for goods result in a corresponding increase or decrease in a company’s need for raw materials. Integral to a company’s demand is forecasting future sales, an important part of CRM systems. Sales forecasts are cre ated from the historical data stored in CRM systems.
This information is critically important to a production man ager who is placing orders for manufacturing processes. Without an accurate future sales forecast, production managers may face inventory problems (discussed in detail in this chapter). The use of CRM systems for production and operational support is critical to efficiently managing the resources of the company.
Supply Chain Management. The production/operations man agement major plays a major role in the supply chain development process. In many organizations, the production/operations man agement staff may even lead the supply chain integration process because of their extensive knowledge of the manufacturing com ponents of the organization. Because they are in charge of pro curement, production, materials control, and logistical handling, a
comprehensive understanding of the techniques of SCM is vital for the production/operations staff.
The downstream segment of supply chains is where market ing, distribution channels, and customer service are conducted. An understanding of how downstream activities are related to the other segments is critical. Supply chain problems can reduce customer satisfaction and negate marketing efforts. It is essential, then, that marketing professionals understand the nature of such problems and their solutions. Also, learning about CRM, its options, and its implementation is important for designing effective cus tomer services and advertising.
As competition intensifies globally, finding new global mar kets becomes critical. Use of IOSs provides an opportunity to im prove marketing and sales. Understanding the capabilities of these technologies as well as their implementation issues will enable the marketing department to excel.
HRM For the Human Resources Major
Customer Relationship Management. Companies trying to en hance their customer relationships must recognize that employees who interact with customers are critical to the success of CRM strat egies. Essentially, the success of CRM is based on the employees’ desire and ability to promote the company and its CRM initiatives. In fact, research analysts have found that customer loyalty is based largely on employees’ capabilities and their commitment to the company.
As a result, human resource managers know that a company that desires valued customer relationships needs valued relation ships with its employees. Therefore, HR managers are implement ing programs to increase employee satisfaction and are training employees to execute CRM strategies.
Supply Chain Management. Supply chains require interactions among the employees of partners in the chain. These interactions are the responsibility of the Human Resources Manager. The HR Manager must be able to address supply chain issues that relate to staffing, job descriptions, job rotations, and accountability. All of these areas are complex within a supply chain and require the HR function to understand the relationship among partners as well as the movement of resources.
Preparing and training employees to work with business part ners (frequently in foreign countries) requires knowledge about how IOSs operate. Sensitivity to cultural differences and extensive communication and collaboration can be facilitated with IT.
MIS For the MIS Major
Customer Relationship Management. The IT function in the en terprise is responsible for the corporate databases and data ware house, as well as the correctness and completeness of the data in them. That is, the IT department provides the data used in a 360º view of the customer. Furthermore, IT personnel provide the tech nologies underlying the customer interaction center.
Supply Chain Management. The MIS staff will be instrumental in the design and support of information systems—both internal or ganizational and interorganizational—that will underpin the busi ness processes that are part of the supply chain. In this capacity, the MIS staff must have a concise knowledge of the business, the systems, and the points of intersection between the two.
Summary
1. Identify the primary functions of both customer relation ship management (CRM) and collaborative CRM.
customization, rapid updates and bug (software error) fixes, and ex tensive free support information. The major drawback of open-source
CRM systems is quality control.
Customer relationship management (CRM) is an organizational strategy that is customer focused and customer driven. That is, organizations concentrate on assessing customers’ requirements for products and services and then on providing high quality, responsive services. CRM functions include acquiring new customers, retaining existing custom ers, and growing relationships with existing customers.
Collaborative CRM is an organizational CRM strategy in which data consolidation and the 360º view of the customer enable the organiza tion’s functional areas to readily share information about customers. The functions of collaborative CRM include integrating communi cations between the organization and its customers in all aspects of marketing, sales, and customer support processes, and enabling cus tomers to provide direct feedback to the organization.
2. Describe how businesses might use applications of each of the two major components of operational CRM systems.
Social CRM is the use of social media technology and services to enable organizations to engage their customers in a collaborative con versation to provide mutually beneficial value in a trusted and trans parent manner.
Real-time CRM means that organizations are able to respond to customer product searches, requests, complaints, comments, ratings, reviews, and recommendations in near real-time, 24/7/365.
4. Describe the three components and the three flows of a supply chain.
A supply chain is the flow of materials, information, money, and ser vices from raw material suppliers, through factories and warehouses, to the end customers. A supply chain involves three segments: up stream, where sourcing or procurement from external suppliers oc curs; internal, where packaging, assembly, or manufacturing takes
place; and downstream, where distribution takes place, frequently by
Operational CRM systems support the front-office business processes that interact directly with customers (i.e., sales, marketing, and ser vice). The two major components of operational CRM systems are customer-facing applications and customer-touching applications.
Customer-facing CRM applications include customer service and support, sales force automation, marketing, and campaign manage ment. Customer-touching applications include search and comparison capabilities, technical and other information and services, customized products and services, personalized web pages, FAQs, e-mail and au tomated response, and loyalty programs.
3. Explain the advantages and disadvantages of mobile CRM systems, on-demand CRM systems, open-source CRM systems, social CRM systems, and real-time CRM systems.
external distributors.
There are three flows in the supply chain: material flows, which are the physical products, raw materials, supplies, and so forth; infor mation flows, which consist of data related to demand, shipments, or ders, returns, and schedules, as well as changes in any of these data; and financial flows, which involve money transfers, payments, credit card information and authorization, payment schedules, e-payments, and credit-related data.
5. Identify popular strategies to solving different challenges of supply chains.
Two major challenges in setting accurate inventory levels through out a supply chain are the demand forecast and the bullwhip effect.
Demand for a product can be influenced by numerous factors such
On-demand CRM systems are those hosted by an external vendor in the vendor’s data center. Advantages of on-demand CRM systems include lower costs and a need for employees to know only how to access and use the software. Drawbacks include possibly unreliable vendors, dif ficulty in modifying the software, and difficulty in integrating vendor- hosted CRM software with the organization’s existing software.
Mobile CRM systems are interactive systems through which communications related to sales, marketing, and customer service activities are conducted through a mobile medium for the purpose of building and maintaining customer relationships between an or ganization and its customers. Advantages of mobile CRM systems include convenience for customers and the chance to build a truly personal relationship with customers. A drawback could be difficulty in maintaining customer expectations; that is, the company must be extremely responsive to customer needs in a mobile, near-real-time environment.
Open-source CRM systems are those whose source code is avail able to developers and users. The benefits of open-source CRM sys tems include favorable pricing, a wide variety of applications, easy
as competition, prices, weather conditions, technological develop ments, economic conditions, and customers’ general confidence. The bullwhip effect refers to erratic shifts in orders up and down the supply chain.
The most common solution to supply chain problems is building inventories as insurance against SC uncertainties. Another solution is the just-in-time (JIT) inventory system, which delivers the precise num ber of parts, called work-in-process inventory, to be assembled into a finished product at precisely the right time. The third possible solution is vendor-managed inventory (VMI), which occurs when the vendor, rather than the retailer, manages the entire inventory process for a particular product or group of products.
6. Explain the utility of each of the three major technologies that support supply chain management.
Electronic data interchange (EDI) is a communication standard that en ables the electronic transfer of routine documents, such as purchasing orders, between business partners.
Extranets are networks that link business partners over the Inter- Corporate portals offer a single point of access through a web net by providing them access to certain areas of each other’s corporate browser to critical business information in an organization. In the con intranets. The main goal of extranets is to foster collaboration among text of business-to-business supply chain management, these portals business partners. enable companies and their suppliers to collaborate very closely.
Chapter Glossary
analytical CRM system CRM system that ana lyzes customer behavior and perceptions in or der to provide actionable business intelligence.
bullwhip effect Erratic shifts in orders up and down the supply chain.
chatbots (also known as bots) Interactive software programs that can have simple conver sations with customers or other bots.
collaborative CRM system A CRM system in which communications between the organiza tion and its customers are integrated across all aspects of marketing, sales, and customer sup port processes.
customer-facing CRM applications Areas in which customers directly interact with the organization, including customer service and support, salesforce automation, marketing, and campaign management.
customer interaction center (CIC) A CRM op eration in which organizational representatives use multiple communication channels to inter act with customers in functions such as inbound teleservice and outbound telesales.
customer relationship management (CRM) A customer-focused and customer-driven organizational strategy that concentrates on ad dressing customers’ requirements for products and services, and then providing high quality, responsive services.
customer-touching CRM applications (also called electronic CRM or e-CRM) Applica tions and technologies with which customers interact and typically help themselves.
customer touch point Any interaction be tween a customer and an organization.
distribution portals Corporate portals that automate the business processes involved in selling or distributing products from a single supplier to multiple buyers.
electronic CRM (e-CRM) See customer- touching CRM applications .
electronic data interchange (EDI) A commu nication standard that enables the electronic transfer of routine documents between busi ness partners.
extranets Networks that link business part ners over the Internet by providing them ac cess to certain areas of each other’s corporate intranets.
front-office processes Those processes that directly interact with customers; that is, sales, marketing, and service.
interorganizational information system (IOS) An information system that sup ports information flow among two or more organizations.
just-in-time (JIT) An inventory system in which a supplier delivers the precise number of parts to be assembled into a finished product at precisely the right time.
loyalty program Programs that offer rewards to customers to influence future behavior.
mobile CRM system An interactive CRM sys tem in which communications related to sales, marketing, and customer service activities are conducted through a mobile medium for the purpose of building and maintaining customer relationships between an organization and its customers.
on-demand CRM system A CRM system that is hosted by an external vendor in the vendor’s data center.
open-source CRM system CRM software whose source code is available to developers and users.
operational CRM system The component of CRM that supports the front-office business pro cesses that directly interact with customers (i.e., sales, marketing, and service).
procurement portals Corporate portals that automate the business processes involved in purchasing or procuring products between a single buyer and multiple suppliers.
pull model A business model in which the production process begins with a customer order and companies make only what custom ers want, a process closely aligned with mass customization.
push model A business model in which the production process begins with a forecast,
which predicts the products that customers will want as well as the quantity of each prod uct. The company then produces the amount of products in the forecast, typically by using mass production, and sells, or “pushes,” those prod ucts to consumers.
real-time CRM system A CRM system ena bling organizations to respond to customer product searches, requests, complaints, com ments, ratings, reviews, and recommendations in near real time, 24/7/365.
salesforce automation (SFA) The component of an operational CRM system that automati cally records all the aspects in a sales transac tion process.
social CRM The use of social media technol ogy and services to enable organizations to engage their customers in a collaborative con versation in order to provide mutually beneficial value in a trusted and transparent manner.
supply chain The coordinated movement of resources from organizations through conver sion to the end consumer.
supply chain management (SCM) An activity in which the leadership of an organization pro vides extensive oversight for the partnerships and processes that compose the supply chain and leverages these relationships to provide an operational advantage.
supply chain visibility The ability of all or ganizations in a supply chain to access or view relevant data on purchased materials as these materials move through their suppliers’ produc tion processes.
vendor-managed inventory (VMI) An in ventory strategy where the supplier monitors a vendor’s inventory for a product or group of products and replenishes products when needed.
vertical integration Strategy of integrating the upstream part of the supply chain with the internal part, typically by purchasing upstream suppliers, so as to ensure timely availability of supplies.
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Discussion Questions
1. How do customer relationship management systems help organi zations achieve customer intimacy?
2. What is the relationship between data consolidation and CRM systems?
3. Discuss the relationship between CRM and customer privacy.
4. Distinguish between operational CRM systems and analytical CRM systems.
5. Differentiate between customer-facing CRM applications and customer-touching CRM applications.
6. Explain why web-based customer interaction centers are critical for successful CRM systems.
7. Why are companies so interested in e-CRM applications?
8. Discuss why it is difficult to justify CRM applications.
9. You are the CIO of a small company with a rapidly growing cus tomer base. Which CRM system would you use: an on-premise CRM
system, an on-demand CRM system, or an open-source CRM system? Remember that open-source CRM systems may be implemented either on-premise or on-demand. Discuss the pros and cons of each type of CRM system for your business.
10. List and explain the important components of a supply chain.
11. Explain how a supply chain approach may be part of a company’s overall strategy.
12. Explain the important role that information systems play in sup porting a supply chain strategy.
13. Would Rolls-Royce Motorcars ( www.rolls-roycemotorcars.com ) use a push model or a pull model in its supply chain? Support your answer.
14. Why is planning so important in supply chain management?
Problem-Solving Activities
1. Access www.ups.com and www.fedex.com . Examine some of the IT-supported customer services and tools provided by the two compa nies. Compare and contrast the customer support provided on the two companies’ websites.
2. Enter www.anntaylor.com , www.hermes.com , and www.tiffany
.com . Compare and contrast the customer service activities offered by these companies on their websites. Do you see marked similarities? Differences?
3. Access your university’s website. Investigate how your university provides for customer relationship management. (Hint: First decide who your university’s customers are.)
4. Access www.sugarcrm.com , and take the interactive tour. Prepare a report on SugarCRM’s functionality to the class.
5. Access www.ups.com and www.fedex.com . Examine some of the IT-supported customer services and tools provided by the two compa nies. Write a report on how the two companies contribute to supply chain improvements.
6. Enter www.supply-chain.org , www.cio.com , www.findarticles
.com , and www.google.com , and search for recent information on supply chain management.
7. Surf the web to find a procurement (sourcing) portal, a distribution portal, and an exchange (other than the examples presented in this chapter). List the features they have in common and those features that are unique.
Closing Case
Amazon’s Global Supply Chain
POM MKT MIS
The Problem
More than 20 years after completing its first sale, Amazon has become an online retailing powerhouse that is competing with bricks-and- mortar global retailers such as Walmart and Target. It is also compet ing in the digital marketplace (e.g., e-books, music, movies, and TV shows) with Apple’s iTunes store and Google’s Google Play.
Amazon’s goal is to provide customers with the best selection, price, and availability. Sometimes the best price is not the lowest, but
the one that provides the best shipping option. Amazon’s website and apps offer a simple, consistent, and reliable user experience. Prod uct information, prices, customer reviews, related products, recom mended products, shipping information, and more appear in the same location on the page. Amazon’s analytics systems use a customer’s or der and search history to create customized experiences for that cus tomer, and Amazon’s order fulfilment process delivers products swiftly and accurately.
Supply chain management is critical to Amazon’s success. In 1995, Amazon began with two fulfilment centers. The company has
Closing Case 337
now expanded to some 350 distribution and fulfillment centers located around the world. Amazon supports this operation with a proprietary, in-house information system that is completely integrated. When the company receives an order, the order-management, inventory- management, and warehouse-management systems locate products around the world and determine the optimal fulfillment plan. Fulfill ment is the business term that refers to the steps involved in receiving, processing, and delivering orders to the end customer. For Amazon, a global company with many products in many distribution centers, and many orders that require cooperation across centers (meaning the entire order cannot be fulfilled through one distribution center), fulfill ment requires a very high level of coordination.
To achieve this coordination, Amazon has to know where every product is located in every distribution center worldwide. For example, when suppliers send products to be “Fulfilled by Amazon” (FBA), their products are immediately scanned into Amazon’s inventory manage ment system. A “stower” then places the goods in any available bin. Items are not organized in any logical manner. However, the product and bin location are recorded by the proprietary information system.
When an order is received, a robot will go to the proper bin and select the items, guided along an optimal route by a scanner (powered by the proprietary information system). The robots bring the items to a human, who places them in boxes. Prepared boxes are sent down the “slam line,” where the packages are weighed and “slammed” with a shipping label. Finally, labeled packages are sent to appropriate load ing docks based on the shipping company that will handle the delivery. Despite the effectiveness of Amazon’s proprietary information system, several key factors, such as the weather, partner delivery com panies, and the competition, remain beyond the company’s control. For example, during the 2013 holiday season, several companies, in cluding Amazon, Kohl’s, and 1-800-flowers.com, promised last-minute delivery without taking into account the capacity of the parcel-delivery companies, FedEx ( www.fedex.com ) and UPS ( www.ups.com ), which were unable to meet the demand. In addition to the demand overload, inclement weather also strained the delivery system. Under normal conditions, parcel delivery companies can adjust delivery to account for weather. During the 2013 holiday season, however, these compa nies were already operating at full capacity. As a result, they could not
make the necessary adjustments to account for inclement weather.
After the 2013 holiday season, Amazon tried to smooth things over with its customers by offering gift cards or credit. More impor tantly, the retailer determined that it needed to implement certain structural changes to expand its control over its entire supply chain and distribution system.
The IT Solution
Amazon needed to develop a method to increase its control over the delivery of its products. One common strategy to increase efficiencies and control is to reduce the number of steps in a system. Significantly, Amazon adopted the opposite approach, adding more sorting centers to its distribution channel. Sorting centers sort pre-packaged orders by zip code. This process enables Amazon to control delivery along the entire route to the local post office for Sunday delivery (in select mar kets). In certain markets where Amazon owns a delivery system, the company can now maintain control all the way to the customer’s door. Adding a step in the distribution system required Amazon to up
date its proprietary information system. Previously, once the system had “slammed” a delivery sticker onto a package, Amazon was basi cally out of the delivery loop. The retailer might maintain tracking (if offered by the delivery company), but it was not in control of the deliv ery. After adding the new step, the fulfillment center would maintain
the package, and the system would direct the pre-packaged order to the appropriate sorting center. When packages arrived at the center, the information system would sort them by zip code. They were then delivered to the local post office or to another carrier for the “last mile” of delivery. In select cities, Amazon maintains its own delivery service. In other cities, the company has contracted with the U.S. Postal Service (USPS) to deliver on Sundays.
The Results
The additional step and the enhanced coordination enabled by Ama zon’s proprietary information system increased the company’s control over its supply chain and reduced its dependence on parcel-delivery companies. Having learned from the 2013 holiday delivery debacle, Amazon expanded its operations and achieved a strategic advantage by gaining more control over its supply chain.
Amazon’s sorting centers, updated supply chain management system, and agreement with the USPS helped the company achieve a record year in 2015. The retailer shipped to more than 185 countries, and added more than 54 million Prime Members. Amazon also re ported no major problems with the new delivery system. Finally, the company reported sales revenue of $107 billion.
Amazon is expanding its Fulfillment by Amazon service, which provides storage, packing, and shipping for independent merchants selling products on the company’ website. To do so, Amazon plans to launch a global shipping and logistics operation called Global Supply Chain by Amazon (GSCA). This operation will compete with carriers such as UPS ( www.ups.com ) and FedEx ( www.fedex.com ), as well as with Alibaba ( www.alibaba.com ), the Chinese e-commerce giant.
GSCA plans to connect two huge markets to each other, Chinese producers and American consumers. About 40 percent of Amazon’s 2016 revenue came from small businesses that buy products from China, manage the complexity of the shipping and import process, and then sell those products to U.S. customers using Fulfillment by Amazon. Amazon saves money if it can cut out those middlemen and streamline logistics enough so that more Chinese sellers use Amazon as their retail platform.
GSCA plans to attract merchants in countries such as China and then consolidate their products at regional shipping hubs. The large volume of goods means that Amazon would be able to buy cargo space at lower wholesale rates and then offer a lower price to participating Chinese merchants by passing on some of the savings. By automating the shipping paperwork, Amazon can further reduce costs and make the process more convenient for the merchants.
To handle GSCA shipping, Amazon received a license to act as a wholesaler for ocean container shipping from the Chinese Ministry of Commerce and from the U.S. Federal Maritime Commission. Having licenses from the United States and from China means that Amazon can buy space on shipping containers at wholesale rates and resell it at retail rates.
To handle air cargo, Amazon signed a deal to lease more cargo jets, in effect doubling the size of its fleet. Amazon partnered with cargo airline Atlas Air ( www.atlasair.com ) to lease 20 Boeing planes. The deal includes the use of the planes, crew, and maintenance for seven years.
The strategy puts Amazon in direct competition with Alibaba. Both companies are competing in the rapidly growing cross-border e-commerce market. By 2020, this market is expected to be a $1 trillion industry serving 900 million shoppers.
For example, Cainiao, a logistics company formed by Alibaba and three others, announced plans in 2016 for a $16 billion supply chain investment. The funds provide enhanced data quality and route
338 CHAPTER 11 Customer Relationship Management and Supply Chain Management
planning information, as well as construction of 1,800 distribution centers and 97,000 delivery stations in more than 600 cities across 31 Chinese provinces.
Amazon and Alibaba have made significant investments in their supply chain capabilities and they will soon handle more shipments than most specialist delivery postal and courier companies. For exam ple, Amazon shipped 1 billion parcels of its own goods in 2016. The company is forecast to handle a greater volume than FedEx by 2019.
Sources: Compiled from J. Webb, “Amazon and Alibaba Bet the Future on Supply Chain Management: eRetailers Invest Big in Logistics,” Forbes,
May 31, 2016; L. Rao, “Amazon Leases More Planes for Air Cargo Network,” Fortune, May 5, 2016; F. Burnson, “6 Ways Amazon Is Changing Supply Chain Management in 2016,” Software Advice, 2016; D. Gilmore, “Amazon—The Most Audacious Logistics Plan in History?” Supply Chain Digest, February 18, 2016;
E. Schuman, “What Amazon Is Doing with Its Supply Chain Could Devastate the Competition,” Computerworld, February 12, 2016; E. Weise, “Amazon’s Chinese Shipping License Reflects Global Goals,” USA Today, February 10, 2016; S. Soper, “Amazon Building Global Delivery Business to Take on Alibaba,” Bloomberg.com , February 9, 2016; J. D’Onfro, “Amazon: Here’s the Final Tally For All the Insane Shopping Everyone Did This Holiday Season,” Business Insider, December 26, 2014; T. Duryee, “Postal Workers Overwhelmed by
Flood of Amazon Sunday Deliveries,” Geek Wire, December 16, 2014; S. Soper,
“Amazon Snags Sorting From FedEx to Avert Package Pileups,” Bloomberg. com , December 9, 2014; B. Stone, “Amazon’s Grand Plan to Avoid Holiday Delivery Snafus Again,” Bloomberg.com , September 26, 2014; J. Greene, “Amazon’s New Sorting Centers Aim to Help with Controlling Deliveries,” The Seattle Times, July 28, 2014; B. Thau, “A Post-Mortem On the Holiday ’13 Retail Shipping Debacle and Remedies for ’14,” Forbes, January 28, 2014; M.
Schlangenstein, L. Patton, and A. Barinka, “UPS Shipping Delays Show Perils of Stores Overpromising,” Bloomberg.com , December 27, 2013; J. Del Ray, “This Is What It Looks Like Inside an Amazon Warehouse,” All Things Digital, December 23, 2013; B. Bacheldor, “From Scratch: Amazon Keeps Supply Chain Close to Home,” Information Week, March 5, 2004; “Amazon Global Fulfillment Center Network,” http://www.mwpvl.com/html/amazon.com.html, accessed October 21, 2016; www.amazon.com, www.ups.com, www.fedex.com, www.usps.com,
accessed October 21, 2016.
Questions
1. Describe the problems that Amazon faced during the 2013 holi day season.
2. Discuss how Amazon solved those problems through its supply chain management system.
3. Discuss how, and the reasons why, Amazon is extending Fulfill ment by Amazon into Global Supply Chain by Amazon.
Business Analytics
CHAPTER OUTLINE LEARNING OBJECTIVES
12.1 Managers and Decision 12.1 Use a decision support framework to demonstrate how technology supports Making managerial decision making at each phase of the decision-making process.
12.2 The Business Analytics 12.2 Describe each phase of the business analytics process. Process
12.3 Descriptive Analytics 12.3 Provide a definition and a use case example for descriptive analytics.
12.4 Predictive Analytics 12.4 Provide a definition and a use case example for predictive analytics.
12.5 Prescriptive Analytics 12.5 Provide a definition and a use case example for prescriptive analytics.
12.6 Presentation Tools 12.6 Describe two examples of presentation tools.
Opening Case
CHAPTER 12
Rent the Runway
On average, an American woman buys 64 new pieces of clothing per year—half of which she will wear only one time. Facebook and Insta gram are actually making matters worse. Today, women often feel that they cannot wear an outfit repeatedly because their friends have seen that outfit on social media. Moreover, fashion industry analysts note that new fashion trends in women’s apparel emerge on average every 10 weeks. Therefore, if a woman wants to feel that she is in step with fashion, she would need to change at least part of her wardrobe that frequently.
POM
A major trend in today’s economy is a transition from ownership to subscribing and sharing. At least one company has implemented this business model in the fashion industry. Located at the intersection of the sharing economy, Facebook, and Instagram, Rent the Runway (RTR; www.renttherunway.com ) buys designer dresses wholesale and rents them over the web, charging only a fraction of the price of the dress. For example, an RTR customer can wear a Calvin Klein gown that costs thousands of dollars for only $70. In 2015, RTR launched a subscription service called Unlimited that allows customers to rent and wear up to three dresses or accessories (sunglasses, bags, jackets) for as long as they want for $139 per month.
When RTR merchandisers decide whether to buy a new dress, they follow a list of 40 data points such as fabric, zippers, stitching, and shape to determine whether the dress will hold up to the rigors of multiple rentals. The longer the lifespan, the higher the return on capital. In mid-2016, RTR was averaging more than 30 turns (rentals) per dress.
Delivering a delicate designer dress is a difficult, complicated, and expensive operation. The dresses must arrive on time and in per fect condition. One mistake—a late arrival, an unsightly stain, a poor fit—creates customer relations problems. To accomplish the firm’s goals, every day RTR algorithms analyze more than 65,000 dresses and 25,000 earrings, bracelets, and necklaces as they move around the country among the firm’s 6 million members. The algorithms enable RTR to ship out 60 percent of the dresses the same day they arrive. In the RTR warehouse, employees sort returns, remove all kinds of stains, sterilize jewelry, and mend tears in clothing. The RTR process works like this:
· Customers return thousands of dresses in barcoded envelopes.
· Employees scan the dresses into the RTR analytics and logistics systems.
339
340 CHAPTER 12 Business Analytics
Managers and Decision Making 341
· Workers inspect the dresses and sort them into bins marked for regular cleaning, stain removal, or repairs.
· Employees who are part chemists and part artists remove stains from clothing, extending the life of the dresses. A database sug gests the optimal chemicals to use for each type of stain and each type of material.
· Automated cleaning machines sterilize and smooth gowns in one minute. (RTR operates the largest dry-cleaning operation in the United States.)
· Seamstresses repair tears, reattach beads, and replace sequins to get gowns ready for wear.
· Orders are assembled and checked for accuracy. The system fore casts dress demand to choose the most cost-effective shipping method for customer returns (ground or overnight air).
· Dresses are double-bagged and mailed to the next customer. To ensure a good fit, customers receive the same dress in two sizes at no extra cost.
RTR operates seven physical retail locations in the United States. The company plans to add another 10 locations over the next few years. Prices in the retail stores are identical to online prices. RTR is opening these stores to help alleviate the fears that women have when trying new brands. In fact, RTR found that shoppers spend 20 percent more in its stores than over the web. In-store customers are also more likely to explore more styles than they might discover online.
In 2016, the total value of RTR dress and accessory rentals ex ceeded $1.4 billion and the company earned over $100 million in reve nue. The number of customers, amount of repeat business, and rental volume doubled in 2014, 2015, and again in 2016. By the end of 2016, RTR was valued at some $600 million.
In November 2016, RTR and Neiman Marcus announced that the two retailers were opening an RTR store-within-a-store in certain
Neiman Marcus locations, beginning in San Francisco. Additional loca tions were scheduled to open in 2017.
RTR predicts that in 5 to 10 years, women’s closets will look very different. There will be one portion filled with clothes that she owns and another portion that will be in constant rotation, filled with rented items. Also, RTR predicts that women might go on vacation without taking a suitcase. They will instead arrive at their hotel rooms with only one small bag, and they will find their closets already filled with rented clothing from RTR.
Furthermore, the founders envision RTR as a marketplace for re tailers and brands to rent unsold inventory instead of shipping it to dis count retailers. Their overall strategy is to be the “Amazon of fashion.”
Sources: Compiled from J. Sharma, “Renting Out Fashion Could Prove a Cure for Ailing Department Stores,” Observer, December 21, 2016; C. O’Connor, “Rent the Runway to Open Neiman Marcus Stores-Within-Stores as Startup Surpasses $100M Annual Sales,” Forbes, November 16, 2016; S. Halzack, “Why Rent the Runway Is Joining Forces with Neiman Marcus,” Washington Post, November 16, 2016; C. O’Connor, “Rent the Runway to Hit $100M Revenues in 2016 Thanks to Unlimited Service,” Forbes, June 15, 2016; P. Vasan, “Rent the Runway’s Designer Closet Tops $800 Million,” CNBC, July 25, 2015; D. Silver, “A High-End Version of Rent the Runway Is Headed to the Hamptons This Summer,” Observer, June 4, 2015; E. Nagy, “Rent the Runway’s Formula for Finding and Fostering Women Leaders,” Fast Company, May 13, 2015; L. King,
“Be Authentic: Rent the Runway’s CEO on How to Win,” Forbes, March 28, 2015;
J. D’Onfro, “Why the CEO of a $400 Million E-Commerce Company Wants UPS and FedEx ‘Out of Business,’” Business Insider, March 18, 2015; J. D’Onfro, “This Startup Founder Wants You to Be Able to Go on a Vacation Without Packing
a Suitcase,” Business Insider, March 14, 2015; R. King, “Q&A: How Rent the Runway Dazzles Shoppers with Data,” ZDNet, February 20, 2015; S. Bertoni, “The Billion-Dollar Dress,” Forbes, September 8, 2014; www.renttherunway
.com, accessed August 24, 2016.
Questions
1. Describe the descriptive analytics applications of Rent the Runway’s business model.
2. Describe the predictive analytics applications of Rent the Run way’s business model.
3. Describe a possible prescriptive analytics application for Rent the Runway.
4. What companies and industries are in danger of being disrupted by Rent the Runway? (Hint: Will Rent the Runway change the way that women buy clothes?)
Introduction
The chapter opening case illustrates the importance and far-reaching nature of business ana lytics applications. Business analytics (BA) is the process of developing actionable decisions or recommendations for actions based on insights generated from historical data. Business analytics examines data with a variety of tools, formulates descriptive, predictive, and pre scriptive analytics models, and communicates these results to organizational decision mak ers. Business analytics can answer questions such as: What happened, how many, how often, where the problem is, what actions are needed, why is this happening, what will happen if these trends continue, what will happen next, what is the best (or worst) that can happen, and what actions should the organization take to achieve various successful business outcomes?
There is a great deal of confusion between the terms business analytics and business intel ligence. Business intelligence (BI) has been defined as a broad category of applications, tech nologies, and processes for gathering, storing, accessing, and analyzing data to help business
users make better decisions. Many experts argue that the terms should be used interchange ably. We agree. However, for simplicity we use the term business analytics (BA) throughout this chapter.
This chapter describes information systems (ISs) that support decision making. Essentially all organizational information systems support decision making (refer to Figure 1.4 and Chap ter 10). Fundamental organizational ISs such as transaction processing systems, functional area information systems, and enterprise resource planning systems provide a variety of re ports that help decision makers. This chapter focuses on business analytics systems, which provide critically important support to the vast majority of organizational decision makers.
The chapter begins by reviewing the manager’s job and the nature of modern managerial decisions. This discussion will help you to understand why managers need computerized sup port. The chapter then introduces the business analytics process and addresses each step in that process in turn.
It is impossible to overstate the importance of business analytics within modern organi zations. Recall from Chapter 1 that the essential goal of information systems is to provide the right information to the right person, in the right amount, at the right time, in the right format. In essence, BA achieves this goal. Business analytics systems provide actionable business re sults that decision makers can act on in a timely fashion.
It is also impossible to overstate the importance of your input into the BA process within an organization, for several reasons. First, you (the user community) will decide what data should be stored in your organization’s data warehouse. You will then work closely with the manage ment information system (MIS) department to obtain these data.
Furthermore, you will use your organization’s BA applications, probably from your first day on the job. With some BA tools such as data mining and decision support systems, you will decide how you want to analyze the data (user-driven analysis). With BA presentation applica tions such as dashboards, you will decide which data you need and in which format. Again, you will work closely with your MIS department to ensure that these applications meet your needs. A significant change is also taking place within the BA environment. In the past, organiza tions used BA only to support management. Today, however, BA applications are increasingly available to front-line personnel (e.g., call center operators), suppliers, customers, and even
regulators. These groups rely on BA to provide them with the most current information.
Much of this chapter is concerned with large-scale BA applications. You should keep in mind, however, that smaller organizations, and even individual users, can implement small- scale BA applications as well.
After you finish this chapter, you will have a basic understanding of decision making, the BA process, and the incredibly broad range of BA applications employed in modern organiza tions. This knowledge will enable you to immediately and confidently provide input into your organization’s BA processes and applications. Furthermore, this chapter will help you use your organization’s BA applications to effectively analyze data and thus make better decisions. We hope that this chapter will help you “Ask the next question.” Enjoy!
Managers and Decision Making
12.1
Management is a process by which an organization achieves its goals through the use of re sources (people, money, materials, and information). These resources are considered to be inputs. Achieving the organization’s goals is the output of the process. Managers oversee this process in an attempt to optimize it. A manager’s success is often measured by the ratio be tween the inputs and outputs for which he or she is responsible. This ratio is an indication of the organization’s productivity.
The Manager’s Job and Decision Making
To appreciate how information systems support managers, you must first understand the man ager’s job. Managers do many things, depending on their position in the organization, the type
FIGURE 12.1 The process and phases in decision making.
and size of the organization, the organization’s policies and culture, and the personalities of the managers themselves. Despite these variations, however, all managers perform three basic roles (Mintzberg, 1973): 1
1. Interpersonal roles: Figurehead, leader, liaison
2. Informational roles: Monitor, disseminator, spokesperson, analyzer
3. Decisional roles: Entrepreneur, disturbance handler, resource allocator, negotiator
Early information systems primarily supported the informational roles. In recent years, however, information systems have been developed that support all three roles. In this chapter, you will focus on the support that IT can provide for decisional roles.
A decision refers to a choice among two or more alternatives that individuals and groups make. Decisions are diverse and are made continuously. Decision making is a systematic pro cess. Economist Herbert Simon (1977) 2 described decision making as composed of three major phases: intelligence, design, and choice. Once the choice is made, the decision is implemented. Figure 12.1 illustrates this process, highlighting the tasks that are in each phase. Note that there is a continuous flow of information from intelligence to design to choice (bold lines). At any phase, however, there may be a return to a previous phase (broken lines).
This model of decision making is quite general. Undoubtedly, you have made decisions in which you did not construct a model of the situation, validate your model with test data, or conduct a sensitivity analysis. The model we present here is intended to encompass all of the conditions that might occur when making a decision. For some decisions, some steps or phases may be minimal, implicit (understood), or completely absent.
1 H. Mintzberg, The Nature of Managerial Work (New York: Harper & Row, 1973).
2 H. A. Simon, The New Science of Management Decision (Englewood Cliffs, NJ: Prentice-Hall, 1977).
The decision-making process starts with the intelligence phase, in which managers exam ine a situation and then identify and define the problem or opportunity. In the design phase, de cision makers construct a model for addressing the situation. They perform this task by making assumptions that simplify reality and by expressing the relationships among all of the relevant variables. Managers then validate the model by using test data. Finally, decision makers set criteria for evaluating all of the potential solutions that are proposed. The choice phase involves selecting a solution or course of action that seems best suited to resolve the problem. This solution (the decision) is then implemented. Implementation is successful if the proposed solu tion solves the problem or seizes the opportunity. If the solution fails, then the process returns to the previous phases. Computer-based decision support assists managers in the decision- making process.
Why Managers Need IT Support
Making good decisions is very difficult without solid information. Information is vital for each phase and activity in the decision-making process. Even when information is available, how ever, decision making is difficult because of the following trends:
· The number of alternatives is constantly increasing because of innovations in technol ogy, improved communications, the development of global markets, and the use of the Internet and e-business. A key to good decision making is to explore and compare many relevant alternatives. The greater the number of alternatives, the more a decision maker needs computer-assisted searches and comparisons.
· Most decisions must be made under time pressure. It is often not possible to manually pro cess information fast enough to be effective.
· Because of increased uncertainty in the decision environment, decisions are becoming more complex. It is usually necessary to conduct a sophisticated analysis to make a good decision.
· It often is necessary to rapidly access remote information, consult with experts, or con duct a group decision-making session, all without incurring major expenses. Decision makers, as well as the information they need to access, can be situated in different lo cations. Bringing everything together quickly and inexpensively represents a serious challenge.
These trends create major difficulties for decision makers. Fortunately, as you will see throughout this chapter, computerized decision support can be of enormous help. Next you will learn about two aspects of decision making that place our discussion of BA in context— problem structure and the nature of the decisions.
A Framework for Computerized Decision Analysis
To better understand business analytics, note that various types of decisions can be placed along two major dimensions: problem structure and the nature of the decision (Gorry and Scott Morton, 1971). 3 Figure 12.2 provides an overview of decision making along these two dimensions.
Problem Structure. The first dimension is problem structure, in which decision-making processes fall along a continuum ranging from highly structured to highly unstructured (see the left column in Figure 12.2). Structured decisions deal with routine and repetitive problems
FIGURE 12.2 Decision support framework.
for which standard solutions exist, such as inventory control. In a structured decision, the first three phases of the decision process—intelligence, design, and choice—are laid out in a partic ular sequence, and the procedures for obtaining the best (or at least a good enough) solution are known. These types of decisions are candidates for decision automation.
At the other extreme of complexity are unstructured decisions. These decisions are in tended to deal with “fuzzy,” complex problems for which there are no cut-and-dried solutions. An unstructured decision is one in which there is no standardized procedure for carrying out any of the three phases. In making such a decision, human intuition and judgment often play an important role. Typical unstructured decisions include planning new service offerings, hiring an executive, and choosing a set of research and development (R&D) projects for the coming year. Although BA cannot make unstructured decisions, it can provide information that assists decision makers.
Located between structured and unstructured decisions are semistructured decisions, in which only some of the decision-process phases are structured. Semistructured decisions re quire a combination of standard solution procedures and individual judgment. Examples of semistructured decisions are evaluating employees, setting marketing budgets for consumer products, performing capital acquisition analysis, and trading bonds.
The Nature of Decisions. The second dimension of decision support deals with the
nature of decisions. All managerial decisions fall into one of three broad categories:
1. Operational control: Executing specific tasks efficiently and effectively
2. Management control: Acquiring and using resources efficiently in accomplishing organiza tional goals
3. Strategic planning: The long-range goals and policies for growth and resource allocation These categories are displayed along the top row of Figure 12.2.
The Decision Matrix. The three primary classes of problem structure and the three broad categories of the nature of decisions can be combined in a decision-support matrix that consists of nine cells, as diagrammed in Figure 12.2. Lower-level managers usually perform the tasks in cells 1, 2, and 4. The tasks in cells 3, 5, and 7 are usually the responsibility of middle managers and professional staff. Finally, the tasks in cells 6, 8, and 9 are generally carried out by senior executives.
Today, it is difficult to state that certain organizational information systems support cer tain cells in the decision matrix. The fact is that the increasing sophistication of information systems means that essentially any information system can be useful to any decision maker, regardless of his or her level or function in the organization. As you study this chapter, you will see that business analytics is applicable across all cells of the decision matrix.
Before you go on. . .
1. Describe the decision-making process proposed by Simon.
2. You are registering for classes next semester. Apply the decision-making process to your decision about how many and which courses to take. Is your decision structured, semistructured, or unstruc tured? Support your answer.
3. Consider your decision-making process when registering for classes next semester. Explain how information technology supports (or does not support) each phase of this process.
The Business Analytics Process
12.2
As previously defined, business analytics is the process of developing actionable decisions or recommendations for actions based upon insights generated from historical data. Business analytics (BA) encompasses not only applications, but also technologies and processes. It includes both “getting data in” (to a data mart or warehouse) and “getting data out” (through BA applications).
The use of BA in organizations varies considerably. In smaller organizations, BA may be lim ited to Excel spreadsheets. In larger ones, BA is enterprisewide, and includes a wide variety of applications. The importance of BA to organizations continues to grow and BA is now a require ment for competing in the marketplace. That is, BA is a competitive necessity for organizations. Not all organizations use BA in the same way. For example, some organizations employ only one or a few applications, whereas others use enterprisewide BA. In general, there are three specific analytics targets that represent different levels of change. These targets differ in regard to their focus; scope; level of sponsorship, commitment, and required resources; techni
cal architecture; impact on personnel and business processes; and benefits.
1. The Development of One or a Few Related Analytics Applications. This target is often a point solution for a departmental need, such as campaign management in marketing. Sponsor ship, approval, funding, impacts, and benefits typically occur at the departmental level. For this target, organizations usually create a data mart to store the necessary data. Or ganizations must be careful that the data mart—an “independent” application—does not become a “data silo” that stores data that are inconsistent with, and cannot be integrated with, data used elsewhere in the organization.
2. The Development of Infrastructure to Support Enterprisewide Analytics. This target supports both current and future analytics needs. A crucial component of analytics at this level is an enterprise data warehouse. Because it is an enterprisewide initiative, senior management often provides sponsorship, approval, and funding. The impacts and benefits are also felt throughout the organization.
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An example of this target is the 3M Corporation ( www.3m.com ). Traditionally, 3M’s various divisions had operated independently, using separate decision-support plat forms. Not only was this arrangement costly, it prevented 3M from integrating the data and presenting a “single face” to its customers. For example, sales representatives did not know whether or how business customers were interacting with other 3M divisions. The solution was to develop an enterprise data warehouse that enabled 3M to operate as
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an integrated company. As an added benefit, the costs of implementing this system were offset by savings resulting from the consolidation of the various platforms.
3. Support for Organizational Transformation. With this target, a company uses business ana lytics to fundamentally transform the ways it competes in the marketplace. Business analytics supports a new business model, and it enables the business strategy. Because of the scope and importance of these changes, critical elements such as sponsorship, approval, and funding originate at the highest organizational levels. The impact on per sonnel and processes can be significant, and the benefits accrue across the organization.
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Harrah’s Entertainment (a brand of Caesars Entertainment; www.caesars
.com ) provides a good example of this analytics target. Harrah’s developed a customer loyalty program known as Total Rewards. To implement the program, Harrah’s created a data warehouse that integrated data from casino, hotel, and special event systems (e.g., wine-tasting weekends) across all the various customer touchpoints (e.g., slot machines, table games, and Internet). Harrah’s used these data to reward loyal customers and to reach out to them in personal and appealing ways, such as through promotional offers. These efforts helped the company become a leader in the gaming industry.
Regardless of the scope of BA, all organizations employ a BA process, which Figure 12.3
depicts. Let’s look at each step of Figure 12.3 in turn, from left to right.
The Business Analytics Process
The entire BA process begins with a business problem, often called pain points by practicing managers. When organizations face business problems, they often turn to business analytics, through the process shown in Figure 12.3, to help solve those problems.
FIGURE 12.3 The Business Analytics Process (drawn by Kelly Rainer, Bill Hardgrave, and Regina Halpin).
Underlying Technologies. Before we begin our discussion of the BA process, let’s emphasize the importance of the technologies that underlie the entire process (see Figure 12.3). These technologies are all improving very rapidly.
Microprocessors (or chips) are becoming increasingly powerful (see Technology Guide 1). In particular, graphics processing units (GPUs) are essential to neural networks, another under lying technology of the BA process. (We discuss neural networks in Technology Guide 4.)
MIS
Advances in digital storage capacity and access speed are driving the cost of stor age down, meaning that organizations are able to store and analyze huge amounts of data. Transmission speed (bandwidth; see Chapter 6) in computer networks, particularly the Inter net, is also rapidly increasing. As a result, decision makers are able to collaborate on difficult, time-sensitive decisions regardless of their locations. Other underlying technologies include machine learning and deep learning, which we discuss in Technology Guide 4.
Data Management.
To actually begin the BA process, an organization must
have data (and lots of it!). As you saw from our discussion of underlying technologies, organi zations are now able to analyze rapidly increasing amounts of data. As you learned in Chap ter 5, these data originate from internal sources (e.g., structured data in relational databases) and external sources (e.g., unstructured data from social media). Organizations are now able to combine and analyze structured and unstructured data from many sources in the form of Big Data. At this point, organizations integrate and “clean” these data into data marts and data warehouses (see Chapter 5) through a process called extract, transform, and load (ETL). The data in the data warehouse are now available to be analyzed by data scientists, analysts, and decision makers.
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Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics.
Organizations perform three types of analytics applications: descriptive analytics, predictive analytics, and prescriptive analytics. We discuss these analytics applications in Sections 12.3, 12.4, and 12.5, respectively.
At the end of Section 12.4, we present an example (with summarized data) that illustrates how a decision maker proceeds through the BA process. In our example, we address only de scriptive analytics and predictive analytics. We do not include prescriptive analytics because this type of analytics is not yet widespread in industry.
Presentation Tools. All three types of analytics produce results, which must be com municated to decision makers in the organization. In general, data scientists actually perform these analyses. Many organizations have employees who “translate” the results of these analy ses into business terms for the decision makers. These employees often use presentation tools in the form of dashboards to get the message across visually. We discuss dashboards and other presentation tools in Section 12.6.
Ask the Next Question. What is critically important about the analytics process is that once the results are obtained and presented, decision makers must be ready to “ask the next question.” Everyone involved in the BA process must use his or her creativity and intuition at this point. The results of the BA process will also almost always lead to new, unanswered questions.
Business Analytics Tools
A variety of BA tools are available to analyze data. They include Excel, multidimensional analy sis (also called OLAP), data mining, and decision-support systems. BA also employs numerous statistical procedures, which include descriptive statistics, affinity analysis, linear, multiple, and logistic regression, as well as many others.
Other than Excel, we discuss BA tools and statistical procedures in the type of analytics application for which they are most appropriate. We discuss Excel here because it is the most popular and common BA tool. Furthermore, Excel incorporates the functionality of many of
the other BA tools and statistical procedures. For example, analysts can use Excel to provide descriptive statistics and perform regression analyses.
BA vendors typically design their software so that it interfaces with Excel. How does this process work? Essentially, users download plug-ins that add functionality (e.g., the ability to list the top 10 percent of customers, based on purchases) to Excel. Excel then connects to the vendor’s application server—which provides additional data analysis capabilities—which in turn connects to a backend database, a data mart, or data warehouse. This arrangement gives Excel users the functionality and access to data typical of sophisticated BA products, while al lowing them to work with a familiar tool—Excel.
In the next three sections, we address descriptive analytics, predictive analytics, and pre scriptive analytics, respectively. Each section begins by defining the type of analytics, contin ues with a discussion of the BA tools and statistical procedures that are appropriate to that type of analytics, and closes with examples of that type of analytics.
Before you go on. . .
1. Describe the three business analytics targets.
2. Describe the business analytics process.
Descriptive Analytics
12.3
Organizations must analyze huge amounts of raw data to make sense out of them. This overall process is known as data reduction, which is the conversion of raw data into a smaller amount of more useful information. Descriptive, predictive, and prescriptive analytics are essentially steps in data reduction.
Descriptive analytics are the first step in data reduction. Descriptive analytics summarize what has happened in the past and allow decision makers to learn from past behaviors. De scriptive analytics are useful to produce information such as total stock in inventory, average dollars spent per customer, and year-over-year change in sales. Common examples of descrip tive analytics are reports that provide historical insights regarding an organization’s produc tion, financials, operations, sales, finance, inventory, and customers.
BA Tools in Descriptive Analytics
BA tools in descriptive analytics applications include online analytical processing, data mining, decision support systems, and a variety of statistical procedures. Examples of such statistical procedures include descriptive statistics, affinity analysis, and many others (see Figure 12.3). We take a look at these tools here.
Online Analytical Processing (OLAP). Some BA applications include online ana lytical processing, also referred to as multidimensional analysis, capabilities. OLAP involves “slicing and dicing” data stored in a dimensional format, “drilling down” in the data to greater detail, and “rolling up” the data to greater summarization (less detail).
Consider our example from Chapter 5. Recall Figure 5.6 illustrating the data cube. The product is on the x-axis, geography is on the y-axis, and time is on the z-axis. Now, suppose you want to know how many nuts the company sold in the West region in 2014. You would slice and dice the cube, using nuts as the specific measure for product, West as the measure for geogra phy, and 2014 as the measure for time. The value or values that remain in the cell(s) after our
slicing and dicing is (are) the answer to our question. As an example of drilling down, you might also want to know how many nuts were sold in January 2014. Alternatively, you might want to know how many nuts were sold from 2014 through 2016, which is an example of aggregation, also called “roll up.”
Data Mining. Data mining refers to the process of searching for valuable business infor mation in a large database, data warehouse, or data mart. Data mining can perform two basic operations: (1) identifying previously unknown patterns and (2) predicting trends and behav iors. The first operation is a descriptive analytics application and the second is a predictive analytics application.
In descriptive analytics, data mining can identify previously hidden patterns in an orga nization’s data. For example, a descriptive analytics application can analyze retail sales data to discover seemingly unrelated products that people often purchase together. The classic ex ample is beer and diapers (even though it is urban legend). Data mining found that young men tend to buy beer and diapers at the same time when shopping at convenience stores. This type of analysis is called affinity analysis or market-basket analysis.
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Affinity analysis is a data mining application that discovers co-occurrence relationships among activities performed by specific individuals or groups. In retail, affin ity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers. Retailers use this information for the purposes of cross- selling, up-selling, sales promotions, loyalty programs, store design (physical location of prod ucts), and discount offers. An example of cross-selling with market basket analysis is Amazon’s use of “customers who bought book A might also like to buy book B.”
In another example, market-basket analysis could tell a retailer that customers often pur chase shampoo and conditioner together. Therefore, putting both items on promotion at the same time would not create an increase in revenue, while a promotion involving just one of the items would likely drive sales of the other.
Decision Support Systems. Decision support systems (DSSs) combine models and data to analyze semistructured problems and some unstructured problems that involve extensive user involvement. Models are simplified representations, or abstractions, of reality. DSSs enable business managers and analysts to access data interactively, to manipulate these data, and to conduct appropriate analyses.
Decision support systems can enhance learning and contribute to all levels of decision making. DSSs also employ mathematical models. Finally, they have the related capabilities of sensitivity analysis, what–if analysis, and goal-seeking analysis, which you will learn about next. You should keep in mind that these three types of analysis are useful for any type of decision-support application. Excel, for example, supports all three.
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To learn about DSS and the three types of analysis, let’s look at an example. Blue Nile ( www.bluenile.com ) is an online retailer of certified diamonds. The firm’s website has a built-in decision support system to help customers find the diamond that best meets their needs. Blue Nile’s DSS provides an excellent example of sensitivity analysis, what–if analysis, and goal-seeking analysis.
Access the Blue Nile website and click on “Diamonds” in the upper left corner. On the drop- down box, you will see “Search for diamonds.” Keep in mind that when you experiment with the Blue Nile DSS, the number of round diamonds available will vary from what we obtained when we accessed the DSS and performed the analyses. The reason is that the Blue Nile website is updated in near-real-time as the company sells its diamonds.
There are many types of diamonds, but for this example click on “Round.” You will see:
· The number of round diamonds available for sale, again in the upper left corner. When we accessed the Blue Nile DSS, the firm offered 112,333 round diamonds for sale.
· Five slide bars labeled: Price, Carat, Cut, Color, and Clarity. Each slide bar represents a variable in Blue Nile’s DSS.
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· A list of each diamond accompanied by a value for each of the five variables. This list con stitutes the data (all round diamonds available for sale) for your analyses.
Sensitivity Analysis. Sensitivity analysis examines how sensitive an output is to any change in an input while keeping other inputs constant. Sensitivity analysis is valuable because it enables the system to adapt to changing conditions and to the varying requirements of differ ent decision-making situations. Let’s perform two sensitivity analyses on the data:
· First, adjust the slide bars for the Carat variable, so that you will see only those round diamonds between 1.00 and 1.50 carats. Keep all the other slide bars in their fully open po sition. In that way, you keep the other variables constant. Note that the number of round diamonds available decreases dramatically. When we followed this procedure, the num ber of round diamonds available for sale dropped to 14,009.
· Second, adjust the slide bars for the Color variable, so that you will see only those round diamonds of D, E, and F color. Be sure to open the slide bars for Carat and keep the other slide bars in their fully open position. When we followed this procedure, the number of round diamonds available for sale dropped to 58,993.
Comparing the results of these two sensitivity analyses, we can say that the number of round diamonds for sale is more sensitive to changes in Carat than to changes in Color, with the other variables remaining constant.
What–If Analysis. A model builder must make predictions and assumptions regarding the input data, many of which are based on the assessment of uncertain futures. The results de pend on the accuracy of these assumptions, which can be highly subjective. What–if analysis attempts to predict the impact of changes in the assumptions (input data) on the proposed solution.
Let’s perform a what–if analysis on the data. A young man’s fiancée has decided that she would like her engagement ring to be between one and two carats, at least a Very Good cut, an F color or better, and a clarity of at least VVS2 (VVS2 means “two very, very small imperfec tions”). Adjust the slide bars for all four of the variables at once. When we followed this proce dure, the number of round diamonds available for sale dropped to 3,830.
Goal-Seeking Analysis. Goal-seeking analysis represents a “backward” solution ap proach. Goal seeking attempts to calculate the value of the inputs necessary to achieve a desired level of output.
Let’s perform a goal-seeking analysis on the data. When the young man in our example looked at the list of 3,830 diamonds (using the scroll bar on the right side of the list), he noticed that the prices ranged from $6,356 to $10,117. He told his fiancée that he had only $5,000 to invest in a diamond. They consequently opened up the slide bars for the Carat, Cut, Color, and Clarity variables and adjusted the slide bar for the Price variable to be between $4,500 and
$5,000. When we followed this procedure, the number of round diamonds available for sale dropped to 3,874.
The couple now had the problem of examining the list of diamonds to decide which com bination of the four variables would be suitable. They did this by performing several what–if analyses:
· She decided that she really wanted a diamond between one and two carats. After adjust ing the Carat slide bar, the number of round diamonds available dropped to 2,035.
· She then decided that she wanted a D, E, or F color. After adjusting the Color slide bar, the number of round diamonds available dropped to 424.
· Next, she chose a Cut that was at least Very Good. After adjusting the Cut slide bar, the number of round diamonds available dropped to 266.
· The couple noticed that all 266 diamonds had a Clarity variable of either SI1 (one small imperfection) or SI2 (two small imperfections). They could either decide that this level
of clarity is acceptable or go back and perform additional what–if analyses on other variables.
Examples of Descriptive Analytics Applications
We present several examples of descriptive analytics in this section. Keep in mind that descrip tive analytics applications often immediately suggest predictive analytics applications. Let’s begin with the examples of Fandango and Darden Restaurants.
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Fandango.
Fandango ( www.fandango.com ) is the leading online ticket seller for
movie theaters. The firm sells millions of tickets to approximately 20,000 movie theaters across the United States. Fandango captures data about customers, movie theaters, ticket sales, and show times.
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Fandango wanted to analyze the movie preferences of its customers during the past year. Using a sample of movie titles, Fandango analysts investigated the total sales for different genres of movies (e.g., comedy, drama, action, and others). Using a sample of moviegoers, they calculated the average ticket sales for a week, the most popular movie, the distribution of cus tomers among the movie genres, the busiest hours of the day in the movie theater, and many other analyses. These descriptive analyses help Fandango set ticket prices, offer discounts for certain movies or show times, and assign show times of the same movie in different theaters.
Darden Restaurants. Darden Restaurants ( www.darden.com ) encompasses seven brands—Olive Garden, Longhorn Steakhouse, Bahama Breeze, Seasons 52, The Capital Grill, Eddie V’s, and Yard House. The company owns and operates more than 1,500 restaurants, em ploys more than 150,000 people, and serves more than 320 million meals every year.
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The company needed a more effective strategy to gather data about what was happening in individual restaurants than simply calling restaurant managers on the telephone at the end of every day. As a result, Darden implemented a Check-Level Analytics system to improve decision making. The system collects information about patrons from the time they arrive until they settle the bill.
The restaurant chain analyzes these types of data, among others:
· How long it takes for a guest to be seated or given a wait time.
· The length of the cook time. Darden calculates cook times for each menu item so that each restaurant can pace both the meal and the food preparation.
· The name of the server who served the guest.
· What time the customer paid the check.
· How much time the guest spent in the restaurant.
Darden links all of these data to the guest satisfaction survey, should the guest complete it. In this way, Darden can get a better picture of what its customers experience in all of its restaurants.
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Darden’s goal is to better understand its guests. For example:
· What is the optimal physical configuration of the restaurant to reduce wait times?
· Are too many two-place tables sitting empty while parties of four wait for tables?
· How much is wait time reduced, and how many more tables can be turned, by adding servers or kitchen staff during busy times?
· What special requests do guests have—for example, a seat by the window or a private corner—that the restaurant cannot accommodate?
· Who are your guests? Is this their first visit? Are they celebrating a special occasion? How did they hear about you?
Darden’s use of analytics has produced results. The restaurant company has saved at least
$20 million since implementing analytics.
For other examples of descriptive analytics, see this chapter’s opening case, closing case, IT’s About Business 12.1, and IT’s About Business 12.2. Specifically, IT’s About Business 12.1 shows how Esurance successfully employs descriptive analytics to provide personalized quotes to prospective customers.
The emergence of technology for capturing, storing, and using real-time data (e.g., the Internet of Things; see Chapter 8) has enabled real-time BA users to employ analytics to analyze data in real time. Real-time BA also helps organizations to make decisions and to interact with customers in new ways. Real-time BA is closely related to descriptive analytics because the focus of decisions is real time, rather than at some point in the future. IT’s About Business 12.2 illustrates how TaKaDu uses sensor data as inputs into real-time analytics software to monitor the pipes of water utilities.
IT’s About Business 12.1
Esurance Uses Analytics to Provide Personalized Quotes
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Esurance ( www.esurance.com ) sells automobile insurance di rectly to consumers online and over the telephone. The firm offers services to almost 90 percent of the U.S. population in 40 states. The insurer also functions as a shopping and comparison site for customers in the 10 remaining states. Esurance is a wholly owned subsidiary of Allstate ( www.allstate.com ).
Deciding on an insurance package is confusing for most peo ple. Esurance wanted to make it easier for its web customers to make choices quickly and knowledgeably. Specifically, Esurance wanted to give Internet customers a similar experience to a face-to face meeting with an insurance agent. To do this, the company de veloped an analytics software package called the Coverage Rules Engine (CRE).
The CRE integrates and analyzes data from two sources. First, Esurance employees formulate business rules by analyzing que ries against the archived information in the firm’s database and by consulting with Esurance’s agents, who assist customers on the Internet, over the phone, and in person. The insurer spent about a year and a half adding business rules to the CRE. Second, the CRE is based on the answers from a questionnaire filled out by customers. These answers produce about 55 variables that the CRE can use to alter the preliminary package offering. By analyzing the two types of data, the CRE can provide almost unlimited customization—up to 8 billion possible packages of coverage.
Consider this example of how Esurance uses its CRE. A com mon business practice is “upselling”—promoting additional products or services to customers after they’ve made their initial purchase. Esurance noted that in the insurance business, agents dealing with customers in person would often advise they add towing and roadside emergency assistance, which was a popu lar add-on. But Esurance customers on the web would rarely add towing to their packages. Consequently, Esurance conducted an experiment. The company installed a button on its site that asked customers before completing their transaction if they wanted to add towing to their policies. If not, they just wrapped up their pur chase. With this new feature, many customers did select the towing option, just like with in-person transactions.
Thanks to CRE, shopping for insurance on the web is becom ing more and more like the in-person experience with an agent. Descriptive analytics allow Esurance to instantly customize and present the policies and options that are most likely to be chosen with the customer. The company continuously monitors how well the CRE is doing by comparing the numbers of site visitors who buy a policy with those who leave the site without making a purchase.
Esurance also wants to make sure customers don’t buy too little or too much insurance. The insurer is convinced that the CRE comes up with the right combination for each customer. Not only does Esurance feel that its quotes and packages are optimal, but that they are provided in half the time (15 minutes) that a rival In ternet car insurer (GEICO) advertises. (Note: This Esurance process actually involves predictive analytics, pointing out how descriptive analytics typically leads to predictive analytics.)
Although Esurance spent $1 million to create and implement the CRE platform, the company has cut its IT costs by between
$500,000 and $1 million annually, including saving money on ana lytics software-as-a-service (SaaS).
Sources: Compiled from M. Elliott, “How Data Analytics Will Change the Risk and Insurance Industry—and Your Career,” Property Casualty 360, October 4, 2016; M. Hollmer, “Esurance Is No April Fool When It Comes to Free Publicity,” Insurance Journal, April 1, 2016; A. Woodie, “How Big Data Analytics Is Shaking Up the Insurance Business,” datanami, January 5, 2016; C. Babcock, “Esurance Puts Analytics Closer to the Customer,” InformationWeek, April 28, 2015; D. Jergler, “Google Compare May Go Beyond Auto Insurance,” Insurance Journal, March 11, 2015; G. Bartholomew, “Demystifying the Rate Madness
in Insurance Advertising,” EagleEye Analytics, October 17, 2014; S. Overby, “Progressive Insurance Uses Data Analytics to Fine-Tune Ad Strategy,” CIO, September 28, 2014; R. Clarke and A. Libarikian, “Unleashing the Value of Advance Analytics in Insurance,” McKinsey, August, 2014; D. Wing, “Evolution of Homeowners Data and Analytics,” Verisk Analytics, Quarter 2, 2014; C. Boulton, “Auto Insurers Bank on Big Data to Drive New Business,” Wall Street Journal, February 20, 2013; www.esurance.com, accessed August 28, 2016.
Questions
1. Describe how Esurance’s CRE analytics package contributes to the customer’s experience.
2. Provide an example of a predictive analytics application that Esurance could implement.
3. Provide an example of a prescriptive analytics application that Esurance could implement.
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IT’s About Business 12.2
TaKaDu’s Dashboard Helps to Conserve Water
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Israel is a desert nation that has been dealing with a drought caused by record-low rainfall since 2008. Therefore, freshwater sources in Israel and the surrounding region are precious. Israelis use water from three sources: recycled wastewater (about 85 percent); a reser voir, filled by pipelines from the Sea of Galilee 90 miles to the north (some 10 percent); and desalination (approximately 5 percent).
Regardless of the source of water, the pipes carrying this pre cious resource are all-important. About one-third of water distrib uted by utilities around the world is lost to leaks. Consequently, the ability to detect leaks instantly is the most valuable conservation technology.
In 2008, startup TaKaDu ( www.takadu.com ) designed cloud- based analytics software that utilizes raw data from smart sensors placed in the pipes of the water network. These sensors monitor the flow rates, pressure, and quality of the water, and they identify prob lems in the meters, valves, pipes, and other system equipment. The software’s algorithms then analyze these data to determine when and where water is leaking. As an added benefit, the sensors can detect weaknesses in the pipes that could lead to future leaks. Con sequently, they enable utilities to prevent leaks before they occur.
TaKaDu provides its results in the form of dashboards on employee smartphones. By scanning the dashboards, company employees monitor whether the meters are accurate, the water quality and pressure are optimal, the water flow is normal, and the pumps are operating properly. How do employees know if the wa ter flow is normal and therefore when use is abnormal? The TaKaDu software determines a baseline of regular use throughout the day. For example, it establishes that water use is highest when people are typically at home—in the mornings before they go to work and in the evenings when they return.
The software also considers local factors. At a Dutch utility, for example, the system noted regular peaks of flow one Friday afternoon. It turned out that increased water use occurred during breaks in play during a World Cup game between the Netherlands and Spain, when fans used their bathrooms.
In addition to leaks and use patterns, TaKaDu software can also help pinpoint problems that could be due to theft. Unitywater ( www.unitywater.com ), a utility in Melbourne, Australia, detected spikes of water coming from a fire hydrant. Officials discovered that a strawberry farmer was siphoning water from the hydrant. Networkwide, Unitywater saved more than 1 billion liters of water in the first 12 months of implementing the TaKaDu system, with a value of more than $2 million. The utility saves not only money, but time, because it now takes 60 percent less time to repair problems. TaKaDu software is most effective when it works with other systems. For example, it complements the sound equipment
developed by the technology company Aquarius Spectrum ( www
.aquarius-spectrum.com ). TaKaDu software identifies the loca tion of a leak within a neighborhood, and then Aquarius’s technol ogy detects the exact pipe that the leak is coming from. Another company, Curapipe ( www.curapipe.com ), has technology that plugs leaks automatically without the need for digging.
TaKaDu has helped Israel to enjoy higher agricultural yields during the current drought than the country had previously achieved in nondrought years. Recall that global utilities tend to lose 30 percent of the water they distribute. That figure is just 10 percent for Jerusalem’s utility, Hagihon, since implementing TaKaDu. In mid-2016, Israel even enjoyed a water surplus, some of which (about 150 million cubic meters per year) it pumps to Jordan and the Palestinian Authority.
By mid-2016, TaKaDu software had been adopted by some 500 utilities in nine countries around the world. TaKaDu has introduced its service for U.S. utilities that monitor water quality, an activity that is closely regulated by the U.S. Environmental Protection Agency.
Unfortunately, despite this progress, only one out of five util ities worldwide were using smart sensors in their water networks, and in the United States, it’s an even lower proportion. But TaKaDu expects the use of its technology will increase, especially among American utilities, as the cost of hardware declines, networks age, and droughts become more common.
Sources: Compiled from “Signs of a Shift in Water Utility Smart Network Thinking,” Aqua Strategy, June 2016; “3M and TaKaDu Team up to Offer Advanced Infrastructure Management to Water Utilities,” PRWeb, August 26, 2015; D. Asper, “TaKaDu’s Water-Saving Technology Saves Australia Millions of Dollars,” NoCamels, April 6, 2015; J. Foreshew, “TaKaDu Helps Keep Water Flowing for Unitywater,” The Australian, March 31, 2015; A. Little, “Anybody Call a Plumber?” Bloomberg BusinessWeek, January 12–18, 2015; J. Neeman, J. Buxton, and K. Cornish, “Water Utilities Turn to New Technologies to Increase Intelligence in Systems,” Breaking Energy, December 4, 2014; H. Moreno, “Transforming Utilities Using Data Analytics,” Forbes, September 30, 2014; S. Udasin, “Hagihon, TaKaDu Sign Contract for Jerusalem Water Management,” Jerusalem Post, September 10, 2014; J. Ollagnier, “Water Utilities: Can They Use Analytics for Smart Monitoring?” Accenture, March 28, 2014; www.takadu.com, accessed August 27, 2016.
Questions
1. How does the TaKaDu system use the Internet of Things? (Hint: See Chapter 8.)
2. Provide an example of how TaKaDu uses its system for pre dictive analytics.
3. Provide an example of how TaKaDu could use its system for prescriptive analytics.
4. Refer to Chapter 2. Is the TaKaDu system a strategic informa tion system for Jerusalem? Why or why not?
Before you go on. . .
1. Describe the purpose of descriptive analytics.
2. Discuss the BA tools that are commonly used in descriptive analytics.
Predictive Analytics
12.4
Predictive analytics examine recent and historical data to detect patterns and predict future outcomes and trends. Predictive analytics provide estimates about the likelihood of a future outcome.
The purpose of predictive analytics is not to tell decision makers what will happen in the future. Predictive analytics can only forecast what might happen in the future, based on proba bilities. For example, predictive analytics applications forecast customer behavior and purchas ing patterns, identify trends in sales activities, and forecast demand for inputs from suppliers.
BA Tools in Predictive Analytics
Organizations use a variety of BA tools and statistical procedures in performing predictive ana lytics. Tools include data mining and statistical procedures include linear regression, multiple regression, and logistic regression. There are many other tools and statistical procedures that are used in predictive analytics. For purposes of illustration, we take a look at data mining and linear regression in this section.
Data Mining. Recall that data mining can perform two basic operations: (1) identifying previously unknown patterns and (2) predicting trends and behaviors. The first operation is a descriptive analytics application and the second is a predictive analytics application.
In predictive analytics, data mining can predict trends and behaviors. For example, tar geted marketing relies on predictive information. Data mining can use data from past promo tional mailings to identify those prospects who are most likely to respond favorably to future mailings. Another business problem that uses predictive information is the forecasting of bank ruptcy and other forms of default.
Another predictive analytics application is detecting fraudulent credit card transactions. Over time, a pattern emerges of the typical ways you use your credit card and your typical shopping behaviors—the places in which you use your card, the amounts you spend, and so on. If your card is stolen and used fraudulently, then the usage often varies noticeably from your established pattern. Data-mining tools can discern this difference and bring the issue to your credit card company’s attention.
Numerous predictive data-mining applications are used in business and in other fields.
The following representative examples illustrate the variety of these applications.
· Retailing and sales: Predicting sales, preventing theft and fraud, and determining correct inventory levels and distribution schedules among outlets. For example, retailers such as AAFES (stores on military bases) use Fraud Watch from SAP ( www.sap.com ) to combat fraud by employees in their 1,400 stores.
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· Banking: Forecasting levels of bad loans and fraudulent credit card use, predicting credit card spending by new customers, and determining which kinds of customers will best respond to (and qualify for) new loan offers.
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· Manufacturing and production: Predicting machinery failures and finding key factors that help optimize manufacturing capacity.
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· Insurance: Forecasting claim amounts and medical coverage costs, classifying the most important elements that affect medical coverage, and predicting which customers will buy new insurance policies.
· Police work: Tracking crime patterns, locations, and criminal behavior in order to predict where and when future crimes might occur. Consider PredPol ( www.predpol.com ), the predictive policing company. After deploying PredPol, the Los Angeles Police Department’s Foothill Division noted a 20 percent decrease in crimes year over year.
· Healthcare: Correlating demographics of patients with critical illnesses and developing better insights on how to identify and treat symptoms and their causes. For example,
Microsoft and Stanford University announced that they had mined the search data of mil lions of users to successfully identify unreported side effects of certain medications.
· Marketing: Classifying customer demographics that can be used to predict which customers will respond to a mailing or buy a particular product.
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· Politics: In his FiveThirtyEight blog, Nate Silver famously analyzed polling and economic data to predict the results of the 2008 presidential election, calling 49 out of 50 states cor rectly. He then correctly predicted all 50 states in the 2012 presidential election. In con trast, Silver failed to correctly predict that Donald Trump would win the 2016 presidential election.
· Weather: The National Weather Service is predicting weather with increasing accuracy and precision by analyzing a myriad of variables that include past and present atmospheric conditions, location, temperature, air pressure, wind speed, and many others.
· Social good: As you will see in IT’s About Business 12.3, Simpa Networks sells solar-as a-service to poor households and small businesses. Simpa partnered with DataKind ( www.datakind.org ) whose data scientists analyzed Simpa’s historical customer data to help Simpa assess potential customers.
Examples of Predictive Analytics Applications
We present numerous examples of predictive analytics in this section. Keep in mind that de scriptive analytics applications often immediately suggest predictive analytics applications. Let’s begin with the examples of Fandango, Twiddy & Company, and Point Defiance Zoo & Aquarium.
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Fandango.
Let’s continue our example of Fandango. How does the ticket seller
know when to send e-mails to its members with discount offers for a specific movie on a spe cific day? Consider John Jones. Predictive analytics tools analyze terabytes of data to deter mine that while John likes science fiction movies, he has not seen the latest science fiction movie, which has been in theaters since the previous Friday.
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Twiddy & Company.
Travelers to the Outer Banks of North Carolina can search
more than 1,000 homes to rent on the islands through Twiddy & Company ( www.twiddy.com ). Twiddy has two business goals: to ensure that travelers enjoy their stay and that homeowners maximize their profit.
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Over the years, Twiddy had used spreadsheets to accumulate operational data. The company invested $40,000 in BA tools from SAS ( www.sas.com ) to integrate these data into an analyzable format. Before implementing SAS analytics, about the only thing that Twiddy could tell travelers was what dates particular homes were available to rent. Now, Twiddy can analyze data on market and seasonal trends to maximize rental income for the homeowners. Further more, Twiddy can analyze property locations and sizes, to suggest optimal rentals to travelers that will satisfactorily meet their vacation needs.
Twiddy bookings increased and the size of its property inventory rose by more than 10 per cent after the company began using SAS. While Twiddy revenues increased, its management costs decreased by 15 percent, thanks to a reduction in invoice processing errors and the auto mation of service schedules. The total savings? About $50,000 since using SAS analytics.
Point Defiance Zoo & Aquarium.
For Point Defiance Zoo & Aquarium
( www.pdza.org ), located in Tacoma, Washington, the biggest business problem is the weather. Specifically, unpredictable weather makes estimating zoo attendance extremely difficult. Historically, Point Defiance used standard weather forecasts to predict attendance, with mixed results.
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Attendance is critical to the success of zoos. Therefore, Point Defiance partnered with IBM ( www.ibm.com ) and analytics firm BrightStar Partners ( www.brightstarpartners.com ). The
zoo compared its attendance records for the past several years with historical weather data collected by the National Weather Service ( www.weather.gov ). Now, for any given week end, the zoo can estimate attendance with an impressive degree of accuracy. The zoo uses these predictions to flexibly schedule its staffing. How effective is this system? For one recent Memorial Day, the projected attendance was within 113 people of the actual attendance—a very accurate prediction.
The analytics software doesn’t just use weather data—it collects information on zoo guests to help target its marketing campaigns. For example, the zoo analyzed socioeconomic data associated with particular zip codes of its most regular patrons and targeted membership discounts to them. This process boosted membership by 13 percent in the first three months of the system’s deployment. Point Defiance sold $60,000 worth of memberships by spending just $4,000.
Point Defiance also analyzed data about online ticket sales and found that many people bought tickets in the evenings and mornings when they weren’t at work. As a result, the zoo im plemented time-limited deals, which helped boost its online ticket sales by 771 percent since 2013. Even in-person ticket sales got a boost, setting records during the same time period.
Additional Examples. Predictive analytics are used throughout organizations in every industry. Let’s look at some examples.
· Predictive analytics drive the coupons you receive at the grocery cash register. In the United Kingdom, grocery giant Tesco ( www.tesco.com ) predicts which discounts will be redeemed so it can better target more than 100 million personalized coupons annu ally at cash registers in 13 countries. This process increased coupon redemption rates by 360 percent over previous methods.
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· Websites predict which ads you will click in order to instantly choose which ad to show you, a process that drives millions of dollars in new revenue.
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· Leading online dating companies Match ( www.match.com ), OkCupid ( www
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.okcupid.com ), and eHarmony ( www.eharmony.com ) predict which prospect on your screen will be the most compatible with you.
· Wireless carriers predict how likely it is that you will cancel and defect to a compe titor (a process called churn), possibly before you have decided to do so. The predictions are based on factors such as dropped calls, your phone usage, your billing information, and whether your contacts have already defected.
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· Allstate Insurance tripled the accuracy of predicting bodily injury liability from car crashes based on the characteristics of the insured vehicle. This process results in approx imately $40 million in annual savings.
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· Stanford University data scientists used predictive analytics to diagnose breast cancer bet ter than human physicians by discovering an innovative method that takes into account a greater number of contributing factors in a tissue sample.
· Officials in Oregon and Pennsylvania are using predictive analytics to assess the risk that a convict will offend again.
· Financial services firms use predictive analytics to produce credit scores. They use these scores to determine the probability of customers making future credit pay ments on time.
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· Sentiment analysis is another type of predictive analysis. The input to this type of analysis is plain text (e.g., ratings, recommendations, tweets, Facebook posts) and the output of the analysis is a sentiment score. This score can be positive or negative. This score could also be a number between −1 and +1, indicating the degree of positivity or negativity.
IT’s About Business 12.3 illustrates how a startup company in India uses analytics to pre dict which of its customers will be a good credit risk and IT’s About Business 12.4 shows how the National Basketball Association uses predictive analytics in a number of ways. For other examples of predictive analytics, see this chapter’s opening case and closing case.
IT’s About Business 12.3
Simpa Networks Provide Solar Energy to India’s Poor
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Worldwide, approximately 1.6 billion people do not have electric ity, and another 1 billion have only unreliable access. Many of these people depend on kerosene lanterns and battery-powered flash lights for light. Moreover, these same people often earn less than
$10 per day, and they spend up to 30 percent of their incomes on inefficient and expensive means of accessing electricity.
The lack of electricity is especially acute in India, with 75 mil lion families not having access. Consequently, these people rely on “dirty” fuels such as kerosene, which they use in lamps. There is hope, however: There are effective alternatives to electricity, such as solar power, that can power homes and small businesses.
To address these energy needs, Simpa Networks ( www
.simpanetworks.com ), a startup technology company founded in 2010, sells solar power, not as a system but as a service, to rural homes and businesses. Its business model is to bring sustainable energy to those who currently do not have enough money to pay for their energy needs.
Simpa’s customers use the “Progressive Purchase” pricing model. Under this model, the consumer makes a series of pay ments, each of which unlocks the solar home system for a specified amount of energy consumption. When the prepaid consumption is exhausted, the solar home system is temporarily disabled until the customer makes another payment. When the consumer has fully paid the total purchase price of the product, Simpa restores full functionality. The system is permanently unlocked, and it produces energy for free.
Simpa’s problem is that, because it has limited resources, it must be selective in deciding which families and businesses to sup port. As a result, the company turned to DataKind for help. Data- Kind ( www.datakind.org ) is a nonprofit organization whose data scientists help humanitarian organizations.
DataKind’s scientists used Simpa’s information on customer energy usage and payment patterns as inputs into a credit-scoring model that helps Simpa better decide to whom to grant credit. The
objective is to provide the most appropriate services and support. That is, the model enables Simpa to become more savvy in choos ing customers. The model also enables Simpa to take risks when lending to rural farmers who cannot turn to banks for loans.
Simpa’s solar-as-a-service has created huge time savings for those doing farming and domestic tasks such as cooking and cleaning. The system also has health benefits, including better quality of light and better air quality, thanks to reduced kerosene fumes and wood smoke. Before Simpa’s system, approximately 8 in 10 energy-poor individuals experienced eye irritation from fumes. After customers deployed the system, that number fell to about 3 in 10. Furthermore, fire accidents plunged to zero. The system also helps boost sales in shops that can stay open later, thanks to cheaper energy.
At the end of 2016, almost 400,000 people and tens of thou sands of small businesses had access to Simpa’s clean, reliable en ergy. The credit-scoring model helped Simpa provide its system to the optimal mix of customers. In that way, the company was able to receive an excellent return on its investment and, in turn, help additional families and businesses.
Sources: Compiled from R. Bean, “Another Side of Big Data: Big Data for Social Good,” Forbes, September 23, 2016; M. Barlow, “Data and Social Good,” O’Reilly, August 14, 2015; “Simpa Networks: Making Solar Power Affordable in Rural India,” OPIC, 2014; J. Novet, “DataKind’s Do-Good Data-Science Projects Arrive in 5 More Cities,” Venture Beat, August 21, 2014; B. Prows, “Rural Solar Energy Lights India’s Future,” MobileBeyond, July 12, 2014; A. Satter, “Watch How Solar Power Is Transforming Rural
India,” Think Progress, July 10, 2014; “We Envision a World without Energy Poverty by 2030: Bijli Program Update,” The Climate Group, July 3, 2014; “Affordable Pay-As-You-Go Solar Power for India’s Energy-Poor Homes,” ADB Knowledge Showcases, August 2013; www.datakind.org, www
.simpanetworks.com accessed August 28, 2016.
Questions
1. Describe how Simpa Networks uses descriptive analytics to further its mission.
2. How does predictive analytics help Simpa Networks provide solar energy to India’s underserved population?
3. Describe how Simpa Networks could use prescriptive ana lytics to further its mission.
Analytics in the National Basketball Association
IT’s About Business 12.4
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Historically, the primary method of assessing player or team per formance was the “eye test.” This term refers to the intuitive feeling that people with long experience in a sport acquired from watching games and practices. Today, professional sports teams are utilizing analytics to provide data-driven judgments.
The analytics revolution in professional sports started in base ball, as highlighted in the book and the movie Moneyball. Baseball is a fairly easy sport to analyze statistically because it involves a series of one-on-one match-ups between a batter and a pitcher.
Furthermore, each play has an obvious start point and end point. In statistical terms, each event is called a “state.”
In contrast to the discrete play in baseball, basketball is one continuous state. Players move instantly from offense to defense. Moreover, regardless of a player’s position, he or she can be at any spot on the court at any time. Analysts could not statistically cal culate the odds of (i.e., predict) a particular result occurring (e.g., a player making a shot).
Consequently, modern analytics revolves around the loca tions and trajectories of the players and the ball. Essentially, ana lytics in the National Basketball Association (NBA; www.nba.com ) is a mapping and data visualization challenge. The challenge is to
graphically represent information about movement through space and time; that is, to make data observable.
The First System. Kirk Goldsberry, a longtime basketball fan with a PhD in geography, undertook the task of developing ana lytics software for professional basketball. First, he imposed a grid over the usable area of the court where players actually shoot—the area that stretches from just outside the three-point line (roughly 25 feet) to the basket. Then he searched for data in each cell of that 1,284-square-foot grid.
Obtaining the data needed for accurate analysis was difficult. Mapping 10 constantly moving players is not a simple process. On ESPN.com, Goldsberry grabbed statistics for all 700,000 shots taken in every NBA game from 2006 to 2011. The site had such char acteristics as the player name, where he took the shot from, and whether it went in the basket. Goldsberry then developed a data base mapping the coordinates for all the shots taken in that period. Goldsberry next analyzed his data to graph shot statistics for in dividual players, including where a given player shot, how often, and whether or not the shot was good. This system, called CourtVision, revealed player differences that had not been previously detected. For example, Ray Allen, one of the league’s top shooters, had several areas from which he made a high percentage of his shots from the
three-point range. However, he rarely tried any mid-range shots.
CourtVision enabled fans, and the league, to visualize their favorite players’ shot patterns. However, CourtVision did not con sider factors such as who the defender was or other events going on around the shot. Nevertheless, Goldsberry’s system provided team management with an initial player assessment tool.
Today’s System. The next opportunity to collect data came when a company called Stats ( www.stats.com ) developed a six-camera system for basketball. The camera system, which is now employed in all 29 NBA arenas, tracks each player on the court throughout ev ery game. It therefore provides a complete view of the entire game, including tracking individual players and ball possession.
Stats offered its data to Goldsberry. The data were more spe cific than what Goldsberry had obtained from ESPN.com. Once Goldsberry had the data, he could analyze them to answer any number of questions.
· Players who “draw the defense” can be quantified as ones who pass the ball effectively when two or more players are guarding them.
· “Getting good spacing” visualizes which players control which parts of the court.
· “On-ball defense” assesses how effectively a player defending the ball decreases his opponent’s chance of scoring.
Analyzing the camera data also helped Goldsberry better grasp one of the most difficult parts of analyzing abasketball game: defense. Historically, teams had used basic numbers—for example, how many steals, how many blocks—to evaluate a player’s defensive game. The new system provided a much more refined view of the game.
Goldsberry began by noticing that the space around the bas ket is the most crucial to defend because this area is where offen sive players shoot the highest percentage. Therefore, he analyzed how effectively defenders could stop opponents from making the shot within five feet of the basket. He found that NBA defenders allowed an average shooting percentage of 49.7 in that area.
Utilizing his new data, Goldsberry categorized defenders into two types. The first category blocked or altered their rivals’ shots.
In other words, they decreased “shooting efficiency.” In the 2014 NBA season, for example, Indiana Pacers center Roy Hibbert and Milwaukee Bucks center Larry Sanders led the NBA, reduced oppo nents’ shooting efficiency to 38 percent.
In the second category of defenders, Goldsberry found that they decreased the frequency of their opponents’ shots, in addition to their efficiency. He examined the average rate of shots versus the rate when certain players were defending the area. This way, he could see who was in play when the number of shots decreased. Again in the 2014 NBA season, the best player at this type of de fense was Houston Rockets center Dwight Howard, who decreased opponents’ shooting frequency by 9 percent. As opponents shot less often around the basket, they took more mid-range shots, which are the least successful shots in the league.
Because basketball has no states, Goldsberry essentially cre ated them by dividing games into slices of time. He then employed the same types of analyses that had been applied to the states in baseball. He could then enumerate—in terms of points—every player’s every move, from an entry pass into the post (the area close to the basket) to a drive to the basket. These analyses created a new method to assess everything a player and team does.
Let’s consider one example, the Houston Rockets. Utilizing the results of its analytics software, its players almost never attempt long-range two-point jump shots because the Rockets feel this type of shot is among the worst plays. The position is too far from the basket to have a high likelihood of going in, but it’s not far enough (behind the three-point line) to gain an extra point for the risk in taking an even longer shot.
Analytics are impacting not only the Rockets, but the entire NBA as well. For example, in one particular month, NBA data anal ysis revealed that players attempted more three-point shots than free throws. In fact, the three-point shot defines the shift to analyt ics in the NBA. It is not a coincidence that the Golden State Warriors, who won the 2015 championship, were the NBA’s top three-point shooting team in the regular season.
Sources: Compiled from “NBA Teams That Have Analytics Department,” NBAStuffer.com, February 18, 2017; T. Ross, “Welcome to Smarter Basket ball,” The Atlantic, June 25, 2015; K. Mehta, “Data and the NBA: A Slam Dunk Approach to Basketball,” Umbel, June 22, 2015; C. Benjamin, “The 4 Fallacies of NBA Analytics,” Men’s Journal, June 3, 2015; S. Hammer,
“Analytics Key to Modern NBA Success,” The Miscellany News, May 6, 2015;
M. Lawrence, “Big Data’s Air-Ball: Five Questions about Players that NBA Analytics Can’t Answer,” Forbes, February 27, 2015; B. Alamar, “The Inside Man: NBA Analytics,” ESPN.com , February 19, 2015; M. McClusky, “One Man’s Quest to Track Every NBA Remade Basketball,” Wired, October 28, 2014; T. Moynihan, “The NBA’s New High-Tech Control Center Is a Hoops Fan’s Dream,” Wired, October 28, 2014; R. Simmons, “Golden State Warriors at the Forefront of NBA Data Analysis,” SFGate, September 14, 2014; B. Holmes, “New Age of NBA Analytics: Advantage or Overload?” Boston Globe, March 30, 2014; D. Oliver, “How Numbers Have Changed the NBA,” ESPN.com , November 15, 2013; www.nba.com, accessed August 25, 2016.
Questions
1. Provide an example of the use of descriptive analytics for an NBA team using Goldberry’s system.
2. Provide an example of the use of predictive analytics for an NBA team using Goldberry’s system.
3. Provide an example of the use of prescriptive analytics for an NBA team using Goldberry’s system.
4. What are the advantages and disadvantages of Goldsberry’s system to NBA players? Provide specific examples to sup port your answer.
Example of Descriptive Analytics and Predictive Analytics
Let’s consider the following example to demonstrate how descriptive and predictive analytics are used to bridge the gap between data management and actionable business decisions.
Weather data can be used to make predictions relating to certain business problems. We have already mentioned how Point Defiance Zoo & Aquarium, located in Tacoma, Washington, used weather data to help accurately estimate zoo attendance and therefore boost ticket sales. Suppose you are the northeast district manager for the American Automobile Association (AAA; www.aaa.com ) and your district covers multiple offices in several states. You need to create a work schedule for your employees who receive and dispatch service calls. Your goal is to provide optimal coverage in your offices, reduce unnecessary salary expenses, and provide excellent customer service by reducing customers’ wait time. Therefore, you decide to predict the number of service calls per day received during normal business hours based on the
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daily low temperature measured in degrees.
Data Management. To investigate this business problem, you collect data on service calls for the past year from the AAA locations within your district. You first “clean” the data by adjusting for outliers and missing values, resulting in a final sample of 3,219 service calls.
You are now ready to build a regression model using temperature as the independent vari able to predict the number of customer service calls received in any given day (the dependent variable). You will use the results to predict how many employees should be available to re ceive and dispatch customer service calls.
Descriptive Analytics. For this example, the correlation between daily low tempera ture and the number of service calls was found to be −0.84 with an average of 48 service calls per day. The correlation is negative because as the temperature decreases, the number of ser vice calls increases.
The square of this correlation, R2 = .71, is the predictive power of the model. That is, the model, using low daily temperature, explains approximately 71 percent of the variation in the number of calls received each day. Therefore, 1 minus R2 means 29 percent of the variance in the dependent variable is due to extraneous or unexplained variables.
Predictive Analytics. The manager has decided to use linear regression for his predic tive analysis. To do so, reasonable assumptions must be met:
1. He must have at least 30 data points.
2. The relationship between the independent and dependent variables must be linear. The linearity assumption can best be tested with scatter plots.
3. Even though normality of the data is often assumed, he should check this assumption.
The sample size of 3,219 satisfies the first assumption. The manager then used Excel to test the second assumption, producing a scatterplot to determine if the plot of each ordered pair of data (independent variable, dependent variable) produced a linear pattern. The scatterplot for the data did exhibit a linear pattern. Therefore, linear regression is an acceptable statistical procedure for these data.
To check for normality, he used the Ryan-Joiner normality test to calculate the correlation between the data and the normal scores of the data. If the value is near 1, the sample dataset is likely to be normal. This dataset met the normality assumption.
Now that the three linear regression assumptions have been met, the next step is to define the linear regression model between these two variables (using Excel or a similar statistical package). The linear regression model is:
Number of calls received = 124.79 − 1.5 × (daily low temperature)
These results mean that for every one degree of increase in the daily low temperature, the predicted daily number of calls received will decrease by 1.5. That is, on warmer days AAA will
expect to receive fewer calls, and will expect more calls on colder days. At a temperature of 0 degrees (x = 0), the expected number of calls will be approximately 125.
Actionable Business Decision. Based on the linear regression, the district man ager is able to use the projected daily low temperature for up to 10 days in advance to predict how many service calls will be received per day. (The Weather Channel provides reasonably accurate daily temperatures on a 10-day outlook.) Therefore, he can predict how many em ployees will be needed to work to manage the expected number of service calls to ensure low wait times for the customers.
Now, ask the next question. At this point, the manager can return to the data man agement stage with new input variables. For the AAA data in this example, it is feasible to con sider another business problem with the appropriate inputs or expand by considering other variables relevant to the business question in the example. For example, the manager might want to include the actual time of day, by hour, so that he could more accurately decide on staffing levels. He also might want to examine the location of his AAA branches as a variable. Adding these variables would require new regression models.
You also want to recall that our example is for the northeast district, where temperatures are cooler than in other regions of the country. For example, a new linear regression would have to be performed using data from the southwest. The reason is that, if we used the northeast re gression model for the southwest, what would be the result for a 100-degree day? The number of calls received would be negative!
Our example proceeds from data management to descriptive analytics to predictive ana lytics (through a simple linear regression model). We address how the results of predictive ana lytics often lead to additional questions that include additional variables, which would require a multiple linear regression model.
From a statistical perspective, the question may be asked: Aren’t there many different an alytical approaches to solving the same problem? The answer is yes. But a more important question to ask is: Which one approach is the best? The answer to this question is none! The best approach depends on the kind of data you are working with, and because data come in all shapes and sizes, you cannot have one best approach for all problems. Therefore, the best model selection for data is always an important exercise in data analytics.
Before you go on. . .
1. Describe the purpose of predictive analytics.
2. Discuss the BA tools that are commonly used in predictive analytics.
Prescriptive Analytics
12.5
Prescriptive analytics go beyond descriptive and predictive models by recommending one or more courses of action and showing the likely outcome of each decision. Predictive analytics do not predict one possible future, but rather multiple future outcomes based on the decision maker’s actions. Prescriptive analytics attempt to quantify the effect of future decisions in or der to advise on possible outcomes before the decisions are actually made.
Some companies are successfully using prescriptive analytics to optimize production, scheduling, and inventory along the supply chain to make sure that they deliver the right prod ucts at the right time so they can best optimize the customer’s experience.
Prescriptive analytics require predictive analytics with two additional components: actionable data and a feedback system that tracks the outcome produced by the action taken. Because prescriptive analytics are able to predict the possible consequences based on
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different choices of action, they can also recommend the best course of action for any prespec ified outcome.
BA Tools in Prescriptive Analytics
Organizations use a variety of BA tools and statistical procedures in performing prescriptive analytics. Statistical procedures include optimization, simulation, and decision trees. A discus sion of these procedures is beyond the scope of this text.
Examples of Prescriptive Analytics Applications
We present numerous examples of prescriptive analytics in this section. Let’s begin by return ing to our example of Fandango.
Fandango. The company uses prescriptive analytics to be able to change ticket price offerings every hour. Fandango has learned when the most desirable movie times are by analyzing millions of show times instantaneously. These data are then used to set an optimal price at any given time, based on the supply of show times and the demand for movie tickets. This process maximizes profits. The data from each show provide the feedback as to the contri bution of each ticket price to profits.
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Google Driverless Car. During every trip, the car makes multiple decisions about what to do based on predictions of future outcomes. For example, when approaching an in tersection, the car must determine whether to go left, right, or straight ahead. Based on its destination, it makes a decision. Additionally, the car must anticipate what might be coming in regard to traffic, pedestrians, bicyclists, and so on. The car must also analyze the impact of a possible decision before actually making that decision.
The Oil and Gas Industry. Using prescriptive analytics enables companies to analyze a variety of structured and unstructured data (including video, image, and sound data) to optimize fracking (hydraulic fracturing) operations. Another prescriptive analytics application optimized the materials and equipment necessary to pump oil out of the ground or to optimize scheduling, production, inventory, and supply chain design to optimize delivery of the right products in the right amount to the right customers at the right time.
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For an additional example of prescriptive analytics, see this chapter’s closing case. Pay particular attention to how UPS progressed from descriptive analytics to predictive analytics and then on to prescriptive analytics.
Before you go on. . .
1. Describe the purpose of prescriptive analytics.
2. Discuss the BA tools that are commonly used in prescriptive analytics.
Presentation Tools
12.6
As you saw in Figure 12.3, organizations use presentation tools to present the results of anal yses to users in visual formats such as charts, graphs, figures, and tables. This process, known as data visualization, makes the results more attractive and understandable to users. Results may be presented after descriptive analytics, predictive analytics, and prescriptive analytics
|
TABLE 12.1 |
The Capabilities of Dashboards |
|
Capability Description |
|
|
Drill down The ability to go to details, at several levels; it can be done by a series of menus or by clicking on a drillable portion of the screen. Critical success factors (CSFs) The factors most critical for the success of a business. These can be organizational, industry, departmental, or for individual workers. Key performance indicators The specific measures of CSFs. Status access The latest data available on KPI or some other metric, often in real time. Trend analysis Short-, medium-, and long-term trend of KPIs or metrics, which are projected using forecasting methods. Exception reporting Reports highlighting deviations larger than certain thresholds. Reports may include only deviations. |
are performed. A variety of visualization methods and software packages that support decision making are available. Dashboards are the most common BA presentation tool. We also take a look at another valuable data visualization tool, geographic information systems.
Dashboards
Dashboards evolved from executive information systems, which were information systems de signed specifically for the information needs of top executives. Today, however, many employ ees, business partners, and customers use digital dashboards.
A dashboard provides easy access to timely information and direct access to management reports. It is user friendly, supported by graphics, and, most importantly, enables managers to examine exception reports and drill down into detailed data. Table 12.1 summarizes the vari ous capabilities common to many dashboards. Moreover, some of the capabilities discussed in this section have been incorporated into many BA products, as illustrated in Figure 12.4.
FIGURE 12.4 Sample performance dashboard.
Image courtesy of Dundas Data Visualization, Inc., 2014 (www.dundas.com).
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FIGURE 12.5 Bloomberg Terminal.
Carlos Osario/Zuma Press.
One outstanding example of a dashboard is offered by Bloomberg LP ( www.bloomberg
.com ), a privately held company that provides a subscription service that sells financial data, software to analyze these data, trading tools, and news (electronic, print, TV, and radio). All of this information is accessible through a color-coded Bloomberg keyboard that displays the desired information on a computer screen, either the user’s screen or one that Bloomberg pro vides. Users can also set up their own computers to access the service without a Bloomberg keyboard. The subscription service plus the keyboard is called the Bloomberg Terminal. It liter ally represents a do-it-yourself dashboard because users can customize their information feeds as well as the look and feel of those feeds (see Figure 12.5).
A unique and interesting application of dashboards to support the informational needs of executives is the Management Cockpit. Essentially, a Management Cockpit is a strategic management room containing an elaborate set of dashboards that enable top-level decision makers to pilot their businesses better. The goal is to create an environment that encour ages more efficient management meetings and boosts team performance through effec tive communication. To help achieve this goal, the dashboard graphically displays KPIs and information relating to critical success factors on the walls of a meeting room called the Management Cockpit Room (see Figure 12.6). The cockpit-like arrangement of instrument panels and displays helps managers visualize how all of the different factors in the business interrelate.
Within the room, the four walls are designated by color: Black, Red, Blue, and White. The Black Wall displays the principal success factors and financial indicators. The Red Wall mea sures market performance. The Blue Wall projects the performance of internal processes and employees. Finally, the White Wall indicates the status of strategic projects. The Flight Deck, a six-screen, high-end PC, enables executives to drill down to detailed information. External information needed for competitive analyses can easily be imported into the room.
Board members and other executives hold meetings in the Management Cockpit Room. Managers also meet there with the comptroller to discuss current business issues. The Man agement Cockpit can implement various what–if scenarios for this purpose. It also provides a common basis for information and communication. Finally, it supports efforts to translate a corporate strategy into concrete activities by identifying performance indicators.
FIGURE 12.6 Management Cockpit.
The Management Cockpit is a registered trademark of SAP, created by Professor Patrick M. Georges.
Geographic Information Systems
A geographic information system (GIS) is a computer-based system for capturing, integrat ing, manipulating, and displaying data using digitized maps. Its most distinguishing character istic is that every record or digital object has an identified geographical location. This process, called geocoding, enables users to generate information for planning, problem solving, and decision making. The graphical format also makes it easy for managers to visualize the data. There are countless applications of GISs to improve decision making in both the public and private sectors.
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The Children’s National Health System offers injury prevention advice to the com munity. Clinicians have found that using geospatial data helps them accomplish this mission. The healthcare center integrated its existing electronic health records system with GIS soft ware from ESRI ( www.esri.com ) to display health data with geospatial coordinates. One of the integrated system’s first projects focused on pediatric burn cases.
GIS mapping enabled the clinic to identify the hotspots where injuries were occurring on a map. That map allowed staff members to develop prevention programs tailored to the demo graphics of areas with high rates of injuries. For example, if the system identifies a cluster of children with burns in a Hispanic neighborhood, the staff will work with community groups to provide parents with Spanish-language information about safety.
The new system has produced results. The clinic is seeing fewer burn patients overall and fewer patients requiring high-level burn care. The Children’s National Health System is now using its system to map concentrations of other medical conditions, such as obesity and asthma.
Before you go on. . .
1. Discuss why presentation tools are so valuable in the business analytics process.
2. What is a dashboard? Why are dashboards so valuable to an organization’s decision makers?
Summary 365
What’s in IT for me?
ACCT For the Accounting Major
BA is used extensively in auditing to uncover irregularities. It is also used to uncover and prevent fraud. CPAs use BA for many of their duties, ranging from risk analysis to cost control.
FIN For the Finance Major
People have been using computers for decades to solve financial problems. Innovative BA applications have been created for activ ities such as making stock market decisions, refinancing bonds, assessing debt risks, analyzing financial conditions, predicting business failures, forecasting financial trends, and investing in global markets.
MKT For the Marketing Major
POM For the Production/Operations Management Major
BA supports complex operations and production decisions from inventory control to production planning to supply chain integration.
HRM For the Human Resources Management Major
Human resources personnel use BA for many of their activities. For example, BA applications can find resumes of applicants posted on the web and sort them to match needed skills and to support man agement succession planning.
MIS For the MIS Major
MIS provides the data infrastructure used in BA. MIS person nel are also involved in building, deploying, and supporting BA applications.
Summary
1. Use a decision support framework to demonstrate how technology supports managerial decision making at each phase of the decision-making process.
When making a decision, either organizational or personal, the deci sion maker goes through a three-step process: intelligence, design, and choice. When the choice is made, the decision is implemented. In general, it is difficult to state which information systems support spe cific decision makers in an organization. Modern information systems,
3. Provide a definition and a use case example for descriptive analytics.
Descriptive analytics summarize what has happened in the past and allow decision makers to learn from past behaviors. We leave the ex ample to you.
4. Provide a definition and a use case example for predictive analytics.
particularly business analytics systems, are available to support every
one in an organization.
2. Describe each phase of the business analytics process.
Business analytics is the process of developing actionable decisions or recommendations for actions based on insights generated from his
Predictive analytics examine recent and historical data in order to de tect patterns and predict future outcomes and trends. We leave the example to you.
5. Provide a definition and a use case example for prescriptive analytics.
torical data. The phases in the business analytics process are shown
in Figure 12.3 and include data management, descriptive analytics (with associated analytics tools and statistics procedures), predictive analytics (with associated analytics tools and statistical procedures), prescriptive analytics (with associated analytics tools and statistical procedures), and presentation tools. The results of the business ana lytics process are actionable business decisions.
Prescriptive analytics go beyond descriptive and predictive models by recommending one or more courses of action and showing the likely outcome of each decision. We leave the example to you.
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6. Describe two examples of presentation tools.
A geographic information system (GIS) is a computer-based
system for capturing, integrating, manipulating, and displaying
A dashboard provides easy access to timely information and direct access to management reports. It is user friendly, it is supported by graphics, and, most importantly, it enables managers to examine ex ception reports and drill down into detailed data.
data using digitized maps. Its most distinguishing characteristic is that every record or digital object has an identified geographical location.
Chapter Glossary
business analytics (BA) The process of de veloping actionable decisions or recommenda tions for actions based on insights generated from historical data.
business intelligence (BI) A broad category of applications, technologies, and processes for gathering, storing, accessing, and analyz ing data to help business users make more in formed decisions.
dashboard A business analytics presenta tion tool that provides rapid access to timely information and direct access to management reports.
data mining The process of searching for val uable business information in a large database, data warehouse, or data mart.
decision A choice that individuals and groups make among two or more alternatives.
decision support systems (DSSs) Business intelligence systems that combine models and data in an attempt to solve semistructured and some unstructured problems with extensive user involvement.
descriptive analytics A type of business ana lytics that summarize what has happened in the past and allow decision makers to learn from past behaviors.
geographic information system (GIS) A computer-based system for capturing, integrat ing, manipulating, and displaying data using digitized maps.
management A process by which organiza tional goals are achieved through the use of resources.
multidimensional data analysis See online analytical processing (OLAP).
online analytical processing (OLAP) (or multidimensional data analysis) A set of ca pabilities for “slicing and dicing” data using dimensions and measures associated with the data.
predictive analytics A type of business ana lytics that examines recent and historical data in order to detect patterns and predict future outcomes and trends.
prescriptive analytics A type of business an alytics that recommends one or more courses of action and shows the likely outcome of each decision.
productivity The ratio between the inputs to a process and the outputs from that process.
Discussion Questions
1. Your company is considering opening a new factory in China. List several typical activities involved in each phase of the decision (intelli gence, design, and choice).
2. Recall that a market basket analysis (a type of data mining) of con venience store purchases revealed that customers tended to buy beer and diapers at the same time when they shopped. Now that the analy sis showed that this relationship exists, provide a rationale for it. Note: You will have to decide what the next question is.
3. The American Can Company announced that it was interested in acquiring a company in the health maintenance organization (HMO) field. Two decisions were involved in this act: (1) the decision to ac quire an HMO, and (2) the decision of which HMO to acquire. How can the use of BA assist the company in this endeavor?
4. Discuss the strategic benefits of business analytics.
5. In early 2012, the New York Times reported the story of a Target data scientist who was able to determine if a customer was pregnant based on her pattern of previous purchases.
a. Describe the business analytics models that the data scientist used.
b. Refer to Chapter 3 and discuss the ethics of Target’s analytics process.
c. Research the story and note the unintended consequences of Target’s analytics process.
6. Consider the admissions process at your university. Your univer sity’s admissions process involves the analysis of many variables to decide whom to admit to each year’s freshman class. Contact your admissions office and gather information on the variables used in the admissions process. As you recall from applying at your university, typ ical variables would include high school attended, high school grade point average, standardized test scores such as ACT or SAT, and many others. (Do not be surprised if there are variables that your admissions office cannot provide.)
a. Provide an example of how your admissions office uses de scriptive analytics in the admissions process. Use the variables you have found in your example.
b. Provide an example of how your admissions office uses pre dictive analytics in the admissions process. Use the variables you have found in your example.
c. Provide an example of how your admissions office uses pre scriptive analytics in the admissions process. Use the variables you have found in your example.
Exercises
1. Consider the city of Barcelona, Spain, which has placed sen sors in all its trash dumpsters. The sensors measure how full each dumpster is.
a. Describe a descriptive analytics application using this sensor data.
b. Describe a predictive analytics application using this sensor data.
c. Describe a prescriptive analytics application using this sensor data.
2. Consider General Electric’s latest-generation LEAP aircraft engine. Sensors in this engine measure vibration and several different temper atures (depending on the location of the sensor).
a. Describe a descriptive analytics application using this sensor data.
b. Describe a predictive analytics application using this sensor data.
c. Describe a prescriptive analytics application using this sensor data.
3. You are a business analyst for a chain of grocery stores. You ana lyze retail sales data, perform a descriptive analytics application, and discover that bread and milk are the two products that are purchased together more often than any other pair of products.
a. Describe a predictive analytics application using these data.
b. Describe a prescriptive analytics application using these data.
Data-Driven Exercise
Background
Organizations exist to fulfill a purpose. This purpose often takes the form of a mission statement. Once the mission is provided to employ ees, all organizational activities should be aimed at helping the organ ization fulfill its purpose, regardless of the functional area in which specific activities occur.
It would seem that the mission statement should be all the or ganization needs to drive decisions, but several complications come into play. Today, decision makers face a dynamic environment in which decisions must be made quickly, information support is not
Closing Case
United Parcel Service Uses Three Types of Analytics
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The Problem
United Parcel Service (UPS; www.ups.com ) is a global organization, with 424,000 employees and nearly 100,000 vehicles. UPS drivers typ ically make between 120 and 175 “drops” per day. Between any two drops, drivers can take a number of possible paths. With 55,000 routes in the Unites States alone, the total number of possible routes is incon ceivably vast. Clearly, it is in the best interest of UPS and its drivers to find the most efficient routes. Therefore, any tiny amount of efficiency that can be gained in daily operations yields significant improvements
Closing Case 367
always available, and the penalty of making incorrect decisions, or not making decisions at all, can threaten the long-term viability of the organization.
Analytics are an attempt to provide support to decision makers in spite of these difficulties. All forms of analytics make use of historical data. The difference is in the type of question that is asked.
· Descriptive analytics helps decision makers answer questions about what has happened in the past (it describes).
· Predictive analytics assumes that trends and relationships from the past will remain constant and therefore answers the question of what could happen in the future (assuming the trends and relation ships remain constant).
· Prescriptive analytics (not yet well defined in the industry) allows the decision maker to model how certain actions will play out in the market. Prescriptive analytics “prescribes” the “if you do this, then this could happen” to potential actions.
Activity
Analytics play an important role across the entire organization. For this activity, you will assume the role of a Data Analytics Specialist for a large retail organization. Your employer operates in both the tradi tional and online retail spaces. However, the economy has taken a toll on overall productivity and decision makers have to be well informed to make appropriate decisions.
Deliverable
Your job is to analyze the data using descriptive and predictive analyt ics, interpret the results, and provide a written analysis to the appro priate decision makers in the following functional areas: Accounting, Finance, Human Resources, Marketing, and Operations. You will use Microsoft Excel to analyze the data and produce both descriptive and predictive analytics. Most importantly, you will write a memo that de tails your findings for the appropriate decision maker in a way that is easy to understand. That is, you will need to use data visualization to make it easy and intuitive for the decision maker to understand and act upon.
Your instructor may customize this activity to fit his or her needs, so be sure you understand the requirements before you begin.
Please visit http://www.wiley.com/go/rainer/IS7e/datadriven exercise to download the full activity and the Excel spreadsheet file.
to the company’s bottom line. Essentially, “little” things matter a great deal to UPS.
UPS must also manage a low-margin business as well as a un ionized workforce that is compensated at the high end of the industry scale. Significantly, rival FedEx ( www.fedex.com ) uses independent contractors for its ground network. Consequently, FedEx does not have the burden of expensive employee benefits packages.
Another problem for UPS is that the increase in electronic com merce has shifted an increasing number of UPS’s delivery stops from retailers to residences. In fact, UPS expects that residential deliveries will make up half of the company’s total deliveries by 2018. Histori cally, drivers would drop off multiple packages at a retailer. Today, they must make scattered stops to drop off packages at individual houses
368 CHAPTER 12 Business Analytics
in a neighborhood. This process involves more routes and is more time consuming.
The Solution
For decades UPS has been using three types of analytics to produce efficiencies:
1. Descriptive analytics asks, “Where are we today?”
2. Predictive analytics asks, “With our current trajectory, where will
we be tomorrow?”
3. Prescriptive analytics asks, “Where should we be tomorrow?”
As UPS has moved from descriptive to predictive to prescriptive analytics, its data needs have increased, the skill set of its people has increased, and the business impact of analytics has increased. We con sider these developments next.
Descriptive Analytics. UPS implemented descriptive analytics in the 1990s when the company provided its drivers with hand-held com puters, called Delivery Information Acquisition Devices (DIADs). The DIADs enabled UPS to capture detailed data that measured the com pany’s current status. For example, the company measured driving variables in hundredths of a second. Their reasoning was that if they could reduce one mile per driver per day in the United States alone, that process would add up to $50 million to the bottom line annually.
One drawback to the DIAD system was that the data were scat tered across various locations. Specifically, some of the data were with employees, some were located in corporate repositories, some were contained in Excel spreadsheets, and some were distributed through out the company. However, UPS did not have a predictive model that could help the company “tomorrow.”
Predictive Analytics. To address this problem, in 2003 UPS de ployed predictive analytics with its Package Flow Technologies sys tem. With this system, drivers started the day with a DIAD that detailed the packages they were to deliver and the order in which they were to deliver those packages. The DIAD became the drivers’ assistant. The system enabled UPS to reduce total delivery driving by 85 million miles per year. That process saved the firm 8.5 million gallons of fuel and it saved the planet from 85,000 metric tons of carbon dioxide going into the atmosphere.
However, drivers had to provide different services from the same vehicle—for example, deferred service and premium service. They had some packages that had to be delivered by 10:30 A.M., some that had to be delivered by noon, and some that had to be delivered by 2:00 P.M. Drivers therefore had to decide how they were going to service those customers. With so many variables to consider, it was practically im possible for drivers to optimize their routes.
Prescriptive Analytics. UPS realized that it needed to take analytics to the next level. So, in mid-2012, the company began deploying its On- Road Integrated Optimization and Navigation (ORION) system. ORION reorganizes the drivers’ routes based on today’s customers, today’s needs, and today’s packages, and it designs deliveries in a very spe cific, optimized order. ORION takes into account UPS business rules, maps, what time drivers need to be at specific locations, and customer preferences.
When UPS drivers are on the road, they usually travel at speeds of 20 to 25 miles per hour. Therefore, every mile reduced equates to a savings of two to three minutes. Because ORION shortens routes by seven to eight miles per day, this savings enables UPS to deliver more packages.
ORION enhances UPS customer service with more efficient rout ing, and it allows UPS to offer innovative services and customized solutions. An example of this type of service is UPS My Choice, which gives customers a one-day alert for the time a package is arriving, and allows them to control the timing and location of the delivery. By mid 2016, UPS My Choice had almost 13 million members.
At the beginning of a shift, a UPS driver checks his DIAD, which displays two possible delivery routes. One route uses ORION, and the other uses UPS’s traditional combination of work rules, procedures, and analytic tools. Drivers can choose either route, but if they decide not to use ORION, then they will be asked to explain their decisions. Driver reaction to ORION has been mixed. The experience can frustrate some drivers who might not want to surrender their autonomy or who might not follow ORION’s logic in designing their routes.
The Results
In April 2016, UPS won the prestigious Edelman Prize for excellence in analytics and operations research for its ORION project. UPS com pleted the deployment of ORION at the end of 2016.
The results from ORION have been outstanding. UPS is realizing savings of between $300 and $400 million per year in driver produc tivity and fuel economy. Furthermore, with ORION, UPS drivers have saved 100 million miles in driving, resulting in a decrease in carbon emissions of 100,000 metric tons per year. In addition to this, ORION will produce further environmental benefits and cost reductions when UPS vehicles outside the United States are equipped with the technology.
UPS continues to look into the future. Interestingly, ORION provides a natural link to driverless vehicles.
However, ORION does not yet perform certain functions. For ex ample, when drivers leave in the morning, the route they have in their DIAD does not change, meaning the system does not update routes if something goes wrong. ORION also does not take traffic or weather conditions into account. UPS plans to integrate these features into subsequent upgrades of ORION.
Sources: Compiled from “How UPS Delivers Predictive Analytics,” CIO, September 28, 2016; T. Davenport, “Prescriptive Analytics Project Delivering Big Dividends at UPS,” DataInformed, April 19, 2016; “INFORMS Awards UPS Its 2016 Edelman Prize: The Leading Award in Analytics and Operations Research,” INFORMS, April 11, 2016; C. Powers, “How UPS Augments Its Drivers’ Intuition with Predictive Analytics,” ASUG News, June 9, 2015; E. Siegel, “Predictive Analytics Driving Results, ROI at UPS,” Data Informed, June 1, 2015; E. Siegel, “Wise Practitioner—Predictive Analytics Interview Series: Jack Levis of UPS,”
Predictive Analytics World, April 28, 2015; J. Berman, “UPS Is Focused on the Future for Its ORION Technology,” Logistics Management, March 3, 2015; “UPS Moves Up Full ORION Rollout in U.S. Market to the End of 2016,” DC
Velocity, March 2, 2015; J. Gidman, “Algorithm Will Tell All UPS Trucks Where to Go,” Newser, February 17, 2015; S. Rosenbush and L. Stevens, “At UPS, the Algorithm Is the Driver,” Wall Street Journal, February 16, 2015; J. Dix, “How UPS Uses Analytics to Drive Down Costs,” Network World, December 1, 2014; K. Noyes, “The Shortest Distance Between Two Points? At UPS, It’s Complicated,” Fortune, July 25, 2014; www.ups.com, accessed August 26, 2016.
Questions
1. Explain how DIADs were a descriptive analytics solution for UPS.
2. Explain how the Package Flow Technologies system was a predic tive analytics solution for UPS.
3. Explain how the ORION system was a prescriptive analytics solution for UPS.
4. Describe another potential application for the UPS ORION sys tem. That is, what is the next question that UPS managers might ask of the ORION system?
