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Cengage_EBA_2e_Chapter0111.pptxIntroduction
Chapter 1
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Introduction
Three developments spurred recent explosive growth in the use of analytical methods in business applications:
First development:
Technological advances—scanner technology, data collection through e-commerce, Internet social networks, and data generated from personal electronic devices—produce incredible amounts of data for businesses
Businesses want to use these data to improve the efficiency and profitability of their operations, better understand their customers, price their products more effectively, and gain a competitive advantage
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Technological advances such as improved point-of-sale scanner technology and the collection of data through e-commerce.
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Introduction
Three developments spurred recent explosive growth in the use of analytical methods in business applications: (contd.)
Second development:
Ongoing research has resulted in numerous methodological developments, including:
Advances in computational approaches to effectively handle and explore massive amounts of data
Faster algorithms for optimization and simulation
More effective approaches for visualizing data
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Introduction
Three developments spurred recent explosive growth in the use of analytical methods in business applications: (contd.)
Third development:
The methodological developments were paired with an explosion in computing power and storage capability
Better computing hardware, parallel computing, and cloud computing have enabled businesses to solve big problems faster and more accurately than ever before
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Cloud computing, the more recent development, is the remote use of hardware and software over the Internet.
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Figure 1.1: Analytics Job Trend According to Indeed.com
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Decision Making
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Decision Making
Managers’ responsibility:
To make strategic, tactical, or operational decisions
Strategic decisions:
Involve higher-level issues concerned with the overall direction of the organization
Define the organization’s overall goals and aspirations for the future
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Decision Making
Tactical decisions:
Concern how the organization should achieve the goals and objectives set by its strategy
Are usually the responsibility of midlevel management
Operational decisions:
Affect how the firm is run from day to day
Are the domain of operations managers, who are the closest to the customer
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Decision Making
Decision making can be defined as the following process:
Identify and define the problem
Determine the criteria that will be used to evaluate alternative solutions
Determine the set of alternative solutions
Evaluate the alternatives
Choose an alternative
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Consider the case of the Thoroughbred Running Company (TRC). Historically, TRC had been a catalog-based retail seller of running shoes and apparel. TRC sales revenue grew quickly as it changed its emphasis from catalog-based sales to Internet-based sales.
Recently, TRC decided that it should also establish retail stores in the malls and downtown areas of major cities. This is a strategic decision that will take the firm in a new direction that it hopes will complement its Internet-based strategy.
TRC middle managers will therefore have to make a variety of tactical decisions in support of this strategic decision, including how many new stores to open this year, where to open these new stores, how many distribution centers will be needed to support the new stores, and where to locate these distribution centers.
Operations managers in the stores will need to make day-to-day decisions regarding, for instance, how many pairs of each model and size of shoes to order
from the distribution centers and how to schedule their sales personnel.
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Decision Making
Common approaches to making decisions
Tradition
Intuition
Rules of thumb
Using the relevant data available
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Business Analytics Defined
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Business Analytics Defined
Business analytics:
Scientific process of transforming data into insight for making better decisions
Used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making
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Business Analytics Defined
Tools of business analytics can aid decision making by:
Creating insights from data
Improving our ability to more accurately forecast for planning
Helping us quantify risk
Yielding better alternatives through analysis and optimization
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A Categorization on Analytical Methods and Models
Descriptive Analytics
Predictive Analytics
Prescriptive Analytics
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A Categorization of Analytical Methods and Models
Descriptive analytics: Encompasses the set of techniques that describes what has happened in the past; examples:
Data queries
Reports
Descriptive statistics
Data visualization (including data dashboards)
Data-mining techniques
Basic what-if spreadsheet models
Data query: A request for information with certain characteristics from a database
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A Categorization of Analytical Methods and Models
Data dashboards: Collections of tables, charts, maps, and summary statistics that are updated as new data become available
Uses of dashboards
To help management monitor specific aspects of the company’s performance related to their decision-making responsibilities
For corporate-level managers, daily data dashboards might summarize sales by region, current inventory levels, and other company-wide metrics
Front-line managers may view dashboards that contain metrics related to staffing levels, local inventory levels, and short-term sales forecasts
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A Categorization of Analytical Methods and Models
Predictive analytics: Consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another
Survey data and past purchase behavior may be used to help predict the market share of a new product
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A Categorization of Analytical Methods and Models
Techniques used in Predictive Analytics:
Linear regression
Time series analysis
Data mining is used to find patterns or relationships among elements of the data in a large database; often used in predictive analytics
Simulation involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision
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Example for Data Mining:
A large grocery store chain might be interested in developing a new targeted marketing campaign that offers a discount coupon on potato chips.
By studying historical point-of-sale data, the store may be able to use data mining to predict which customers are the most likely to respond to an offer on discounted chips by purchasing higher-margin items such as beer or soft drinks in addition to the chips, thus increasing the store’s overall revenue.
Example for Simulation:
Banks often use simulation to model investment and default risk in order to stress test financial models.
Used in the pharmaceutical industry to assess the risk of introducing a new drug.
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A Categorization of Analytical Methods and Models
Prescriptive Analytics: Indicates a best course of action to take
Optimization models: Models that give the best decision subject to constraints of the situation
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| Model | Field | Purpose |
| Portfolio models | Finance | Use historical investment return data to determine the mix of investments that yield the highest expected return while controlling or limiting exposure to risk |
| Supply network design models | Operations | Provide the cost-minimizing plant and distribution center locations subject to meeting the customer service requirements |
| Price markdown models | Retailing | Uses historical data to yield revenue-maximizing discount levels and the timing of discount offers when goods have not sold as planned |
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A Categorization of Analytical Methods and Models
Prescriptive Analytics (contd.)
Simulation optimization: Combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain
Decision analysis
Used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events
Employs utility theory, which assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors
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Big Data
Volume
Velocity
Variety
Veracity
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Big Data
Big data: A set of data that cannot be managed, processed, or analyzed with commonly available software in a reasonable amount of time
Represents opportunities
Presents challenges in terms of data storage and processing, security, and available analytical talent
More companies are hiring data scientists who know how to process and analyze massive amounts of data
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Walmart handles over one million purchase transactions per hour.
Facebook processes more than 250 million picture uploads per day.
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Figure 1.2: The 4 Vs of Big Data
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Big Data
The four Vs have led to new technologies
Hadoop: An open-source programming environment that supports big data processing through distributed storage and processing over multiple computers
MapReduce: A programming model used within Hadoop that performs two major steps: the map step and the reduce step
Data security: The protection of stored data from destructive forces or unauthorized users
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Business Analytics in Practice
Financial Analytics
Human Resource (HR) Analytics
Marketing Analytics
Health Care Analytics
Supply-Chain Analytics
Analytics for Government and Nonprofits
Sports Analytics
Web Analytics
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Figure 1.3: The Spectrum of Business Analytics
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Business Analytics in Practice
Predictive and prescriptive analytics are sometimes referred to as advanced analytics
Financial analytics
Use of predictive models to:
Forecast future financial performance
Assess the risk of investment portfolios and projects
Construct financial instruments such as derivatives
Construct optimal portfolios of investments
Allocate assets
Create optimal capital budgeting plans
Simulation is also often used to assess risk in the financial sector
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Business Analytics in Practice
Human resource (HR) analytics
New area of application for analytics
The HR function is charged with ensuring that the organization:
Has the mix of skill sets necessary to meet its needs
Is hiring the highest-quality talent and providing an environment that retains it
Achieves its organizational diversity goals
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Example for Human Resource (HR) Analytics:
Sears Holding Corporation (SHC), owners of retailers Kmart and Sears, Roebuck and Company, has created an HR analytics team inside its corporate HR function.
The team uses descriptive and predictive analytics to support employee hiring and to track and influence retention.
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Business Analytics in Practice
Marketing analytics
Marketing is one of the fastest growing areas for the application of analytics
A better understanding of consumer behavior through the use of scanner data and data generated from social media has led to an increased interest in marketing analytics
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Business Analytics in Practice
Marketing analytics (contd.)
A better understanding of consumer behavior through marketing analytics leads to:
Better use of advertising budgets
More effective pricing strategies
Improved forecasting of demand
Improved product line management
Increased customer satisfaction and loyalty
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Example of high-impact marketing analytics:
Automobile manufacturer Chrysler teamed with J. D. Power and Associates to develop an innovate set of predictive models to support its pricing decisions for automobiles.
These models help Chrysler to better understand the ramifications of proposed pricing structures (a combination of manufacturer’s suggested retail price, interest rate offers, and rebates) and, as a result, to improve its pricing decisions.
The models have generated an estimated annual savings of $500 million.
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Figure 1.4: Google Trends for Marketing, Financial, and Human Resource(HR) Analytics, 2006–2015
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While interest in marketing, financial, and human resource analytics is increasing, the graph clearly shows the pronounced increase in the interest in marketing analytics.
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Business Analytics in Practice
Health care analytics
Descriptive, predictive, and prescriptive analytics are used to improve:
Patient, staff, and facility scheduling
Patient flow
Purchasing
Inventory control
Use of prescriptive analytics for diagnosis and treatment
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Example for use of prescriptive analytics for diagnosis and treatment:
Working with the Georgia Institute of Technology, Memorial Sloan-Kettering Cancer Center developed a real-time prescriptive model to determine the optimal placement of radioactive seeds for the treatment of prostate cancer.
Using the new model, 20–30 percent fewer seeds are needed, resulting in a faster and less invasive procedure.
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Business Analytics in Practice
Supply chain analytics
The core service of companies such as UPS and FedEx is the efficient delivery of goods, and analytics has long been used to achieve efficiency
The optimal sorting of goods, vehicle and staff scheduling, and vehicle routing are all key to profitability for logistics companies such as UPS, FedEx, and others like them
Companies can benefit from better inventory and processing control and more efficient supply chains
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Example for supply chain analytics:
ConAgra Foods uses predictive and prescriptive analytics to better plan capacity utilization by incorporating the inherent uncertainty in commodities pricing.
ConAgra realized a 100 percent return on their investment in analytics in under three months—an unheard of result for a major technology investment.
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Business Analytics in Practice
Analytics for government to:
Drive out inefficiencies
Increase the effectiveness and accountability of programs
Analytics for nonprofit agencies to ensure their effectiveness and accountability to their donors and clients
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Example of analytics for government agencies:
The New York State Department has worked with IBM to use prescriptive analytics in the development of a more effective approach to tax collection. The result was an increase in collections from delinquent payers of $83 million over two years.
Example of analytics for nonprofit agencies:
Catholic Relief Services (CRS) is the official international humanitarian agency of the U.S. Catholic community. The CRS mission is to provide relief for the victims of both natural and human-made disasters and to help people in need around the world through its health, educational, and agricultural programs.
CRS uses an analytical spreadsheet model to assist in the allocation of its annual budget based on the impact that its various relief efforts and programs will have in different countries.
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Business Analytics in Practice
Sports analytics
Professional sports teams use to:
Assess players for the amateur drafts
Decide how much to offer players in contract negotiations
Professional motorcycle racing teams that use sophisticated optimization for gearbox design to gain competitive advantage
Teams use to assist with on-field decisions such as which pitchers to use in various games of a MLB playoff series
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Business Analytics in Practice
Sports analytics (contd.)
The use of analytics for off-the-field business decisions is also increasing rapidly
Using prescriptive analytics, franchises across several major sports dynamically adjust ticket prices throughout the season to reflect the relative attractiveness and potential demand for each game
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Business Analytics in Practice
Web analytics
The analysis of online activity, which includes, but is not limited to, visits to web sites and social media sites such as Facebook and LinkedIn
Leading companies apply descriptive and advanced analytics to data collected in online experiments to determine the best way to:
Configure web sites
Position ads
Utilize social networks for the promotion of products and services
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Online experimentation involves exposing various subgroups to different versions of a Web site and tracking the results.
Because of the massive pool of Internet users, experiments can be conducted without risking the disruption of the overall business of the company.
Such experiments are proving to be invaluable because they enable the company to use trial-and-error in determining statistically what makes a difference in their Web site traffic and sales.
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