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Cengage_EBA_Chapter01.pptx

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Chapter 1

Introduction

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Introduction

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Introduction

Three developments spurred recent explosive growth in the use of analytical methods in business applications:

First development:

Technological advances, 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.

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, and

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.

Figure 1.1 - Google Trends Graph of Searches on the term Analytics

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Figure 1.1 is a graph generated by Google Trends that displays the search volume for the word analytics from 2004 to 2013 (projected) on a percentage basis from the peak.

The figure clearly illustrates the recent increase in interest in analytics.

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.

These decisions 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.

They are usually the responsibility of midlevel management.

Operational decisions:

Affect how the firm is run from day to day.

They 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.

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 of Analytical Methods

and Models

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A Categorization of Analytical Methods and Models

Descriptive analytics: It encompasses the set of techniques that describes what has happened in the past.

Examples - data queries, reports, descriptive statistics, data visualization (data dashboards), data-mining techniques, and basic what-if spreadsheet models.

Data query - It is 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: It 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: contd.

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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.

Data mining

Simulation

Used to find patterns or relationships among elements of the data in a large database; often used in predictive analytics.

It involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision.

A Categorization of Analytical Methods and Models

Prescriptive Analytics: It indicates a best course of action to take

Models used in prescriptive analytics:

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Optimization models

Simulation optimization

Decision analysis

Models that give the best decision subject to constraints of the situation.

Combines the use of probability and statistics to model uncertainty with optimization techniques to find good decisions in highly complex and highly uncertain settings.

Used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events.

It also employs utility theory, which assigns values to outcomes based on the decision maker’s attitude toward risk, loss, and other factors.

A Categorization of Analytical Methods and Models

Optimization models

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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Big Data

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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.

Big data represents opportunities.

It also presents analytical challenges from a processing point of view and consequently has itself led to an increase in the use of analytics.

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.

Business Analytics in Practice

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Figure 1.2 - The Spectrum of Business Analytics

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Companies that apply analytics often follow a trajectory similar to that shown in Figure 1.2.

Organizations start with basic analytics in the lower left.

As they realize the advantages of these analytic techniques, they often progress to more sophisticated techniques in an effort to reap the derived competitive advantage.

Predictive and prescriptive analytics are sometimes therefore referred to as advanced analytics.

Business Analytics in Practice

Types of applications of analytics by application area

Financial analytics

Use of predictive models

To forecast future financial performance

To assess the risk of investment portfolios and projects

To construct financial instruments such as derivatives

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Business Analytics in Practice

Financial analytics (contd.)

Use of prescriptive models

To construct optimal portfolios of investments

To allocate assets, and

To create optimal capital budgeting plans.

Simulation is also often used to assess risk in the financial sector

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Example for use of prescriptive models:

GE Asset Management uses optimization models to decide how to invest its own cash received from insurance policies and other financial products, as well as the cash of its clients such as Genworth Financial.

The estimated benefit from the optimization models was $75 million over a five-year period.

Example for use of simulation:

Deployment by Hypo Real Estate International of simulation models to successfully manage commercial real estate risk.

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, and

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.

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:

The better use of advertising budgets

More effective pricing strategies

Improved forecasting of demand

Improved product line management, and

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.

Figure 1.3 - Google Trends for Marketing, Financial, and Human Resource Analytics, 2004–2012

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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.

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.

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.

Business Analytics in Practice

Analytics for government and nonprofits

To drive out inefficiencies

To 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.

Business Analytics in Practice

Sports analytics

Used for player evaluation and on-field strategy in professional sports.

To assess players for the amateur drafts and to decide how much to offer players in contract negotiations.

Professional motorcycle racing teams that use sophisticated optimization for gearbox design to gain competitive advantage.

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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 - It is 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, and

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.