BI Systems _ch09

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Business Intelligence

SystemsDavid Kroenke

Using MIS 3e

Chapter 9


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Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall 9-2

This chapter surveys the most common businessintelligence and knowledge-management applications,discusses the need and purpose for data warehouses,and explains how business intelligence applications are

delivered to users as business intelligence systems.

Along the way, youll learn tools and techniques that

MRV can use to identify the guides that contribute the

most (and least) to its competitive strategy.Well wrap up by discussing some of the potential

benefits and risks of mining credit card data.

Chapter Preview


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Study Questions

Q1 Why do organizations need business

intelligence?

Q2 What business intelligence systems are available?

Q3 What are typical reporting applications?

Q4 What are typical data-mining applications?Q5 What is the purpose of data warehouses and data marts?

Q6 What are typical knowledge-management applications?

Q7 How are business intelligence applications delivered?

Q8 2020?


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Why Do Organizations Need

Business Intelligence?

Information systems generate enormous amountsof operational data that contain patterns,relationships, clusters, and other information thatcan facilitate management, especially planning and

forecasting. Business intelligence systems producesuch information from operational data.

Data communications and data storage areessentially free, enormous amounts of data are

created and stored every day. 12,000 gigabytes per person of data, worldwide

in 2009


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How Big Is an Exabyte?

(See video)
http://en.wikipedia.org/wiki/Exabytehttp://quicktime.pearsoncmg.com/ph/bp/bp_kroenke_experiencing_1/Chap9_1_MSTR.movhttp://quicktime.pearsoncmg.com/ph/bp/bp_kroenke_experiencing_1/Chap9_1_MSTR.movhttp://en.wikipedia.org/wiki/Exabyte


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Study Questions

Q1 Why do organizations need business intelligence?

Q2 What business intelligence systems are

available?


Q4 What are typical data-mining applications?Q5 What is the purpose of data warehouses and data marts?



Q8 2020?


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Business Intelligence (BI) Tools

BI systems provide valuable information for decision making.(BI video)

Three primary BI systems:

1. Reporting Tools

Integrate data from multiple systems

Sorting, grouping, summing, averaging, comparing data

2. Data-mining Tools

Use sophisticated statistical techniques, regression analysis,and decision tree analysis

Used to discover hidden patterns and relationships

Market-basket analysis
http://www.webopedia.com/TERM/B/Business_Intelligence.htmlhttp://www.webopedia.com/TERM/B/Business_Intelligence.htmlhttp://media.pearsoncmg.com/ph/bp/bp_akamai/mymislab/DMK2_9-1.htmlhttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/Data_mininghttp://en.wikipedia.org/wiki/Data_mininghttp://media.pearsoncmg.com/ph/bp/bp_akamai/mymislab/DMK2_9-1.htmlhttp://www.webopedia.com/TERM/B/Business_Intelligence.htmlhttp://www.webopedia.com/TERM/B/Business_Intelligence.html


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Business Intelligence Tools

3. Knowledge-management tool Create value by collecting and sharing human

knowledge about products, product uses,best practices, other critical knowledge

Used by employees, managers, customers,suppliers, others who need access tocompany knowledge
http://www.webopedia.com/TERM/B/Business_Intelligence.htmlhttp://en.wikipedia.org/wiki/Knowledge_management_systemhttp://en.wikipedia.org/wiki/Knowledge_management_systemhttp://en.wikipedia.org/wiki/Knowledge_management_systemhttp://en.wikipedia.org/wiki/Knowledge_management_systemhttp://www.webopedia.com/TERM/B/Business_Intelligence.html


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Tools vs. Applications

vs. Systems

BI tool is one or more computer programs. BI toolsimplement the logic of a particular procedure orprocess.

BI application is the use of a tool on a particular

type of data for a particular purpose. BI system is an information system having all five

components that delivers results of a BI applicationto users who need those results.


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Study Questions




Q4 What are typical data-mining applications?

Q5 What is the purpose of data warehouses and data marts?Q6 What are typical knowledge-management applications?


Q8 2020?


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Basic Reporting Operations

Reporting tools produce information fromdata using five basic operations:

Sorting

Grouping Calculating

Filtering

Formatting


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List of Sales Data


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Data Sorted by Customer Name


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Sales Data,

Sorted by

Customer Name

and Groupedby Orders and

Purchase

Amount


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Sales Data Filtered to Show

Repeat Customers and Formatted

for Easier Understanding


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RFM Analysis

RFM analysis allows you to analyze and rankcustomers according to purchasing patterns as thisfigure shows.

R = how recently a customer purchased your

products F = how frequently a customer purchases your

products

M = how much money a customer typically

spends on your products
http://www.dbmarketing.com/articles/Art149.htmhttp://www.dbmarketing.com/articles/Art149.htm


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RFM Tools Classify Customers?

Divides customers into five groups and assigns ascore from 1 to 5

R score 1 = top 20 percent in most recent orders

R score 5 = bottom 20 percent (longest since lastorder)

F score 1 = top 20 percent in most frequent orders

F score 5 = bottom 20 percent least frequent orders

M score 1 = top 20 percent in most money spent

M score 5 = bottom 20 percent in amount of moneyspent


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Example of RFM Score Data

Figure 9-6


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Interpreting RFM Score Results

Ajax has ordered recently and orders frequently. Mscore of 3 indicates it does not order mostexpensive goods. A good and regular customer but need to attempt to up-

sell more expensive goods to Ajax

Bloominghams has not ordered in some time, butwhen it did, ordered frequently, and orders were ofhighest monetary value.

May have taken its business to another vendor. Salesteam should contact this customer immediately.


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Interpreting RFM Score Results

Caruthers has not ordered for some time;did not order frequently; did not spendmuch.

Sales team should not waste any time on thiscustomer.

Davidson in middle

Set up on automated contact system or use the

Davidson account as a training exercise


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Online Analytical Processing

(OLAP)

OLAP, a second type of reporting tool, ismore generic than RFM.

OLAP provides the ability to sum, count,

average, and perform other simplearithmetic operations on groups of data.

Remarkable characteristic of OLAP reportsis that they are dynamic. The viewer of the

report can change reports format, hencethe term online.
http://www.olapcouncil.org/http://www.olapcouncil.org/


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How Are OLAP Reports Dynamic?

OLAP reports Simple arithmetic operations on data

Sum, average, count, and so on

Dynamic User can change report structure View online

Measure Data item to be manipulatedtotal sales, average cost

Dimension Characteristic of measurepurchase date, customer

type, location, sales region
http://en.wikipedia.org/wiki/OLAPhttp://en.wikipedia.org/wiki/OLAP


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OLAP Product Family

and Store Type


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OLAP Reports

OLAP cube Presentation of measure with associated

dimensions

a.k.a. OLAP report

Users can alter format. Users can drill down into data.

Divide data into more detail

May require substantial computing power
http://en.wikipedia.org/wiki/OLAP_cubehttp://en.wikipedia.org/wiki/OLAP_cube


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OLAP Product Family and

Store Location by Store Type


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OLAP Product Family and Store

Location by Store Type, Drilled

Down to Show Stores in California


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OLAP Servers

Developed to perform OLAP analysis Server reads data from operational

database

Performs calculations Stores results in OLAP database

Third-party vendors provide software formore extensive graphical displays.

Data Warehousing Review

OLAP services
http://www.dwreview.com/OLAP/index.htmlhttp://en.wikipedia.org/wiki/OLAP_Serviceshttp://en.wikipedia.org/wiki/OLAP_Serviceshttp://www.dwreview.com/OLAP/index.html


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Role of OLAP Server

and OLAP Database


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Study Questions





Q5 What is the purpose of data warehouses and data marts?Q6 What are typical knowledge-management applications?


Q8 2020?


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Convergence of Disciplines and

Information Technology


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Unsupervised Data Mining

Analysts do not create model before runninganalysis.

Apply data-mining technique and observe results

Analysts create hypotheses afteranalysis to explain

patterns found. No prior model about the patterns and

relationships that might exist

Common statistical technique used:

Cluster analysis to find groups of similar customers fromcustomer order and demographic data


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Supervised Data Mining

Model developed before analysis Statistical techniques used to estimate

parameters

Examples: Regression analysismeasures impact

of set of variables on one another

Used for making predictions


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Regression Analysis

CellphoneWeekendMinu tes =12 + (17.5 * CustomerAge) +(23.7 * NumberMonthsOfAc cou nt)

Using this equation, analysts can predictnumber of minutes of weekend cell phone

use by summing 12, plus 17.5 times thecustomers age, plus 23.7 times the number

of months of the account.

Considerable skill is required to interpret thequality of such a model


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Neural Networks

Neural networks Popular supervised data-mining

technique used to predict values and

make classifications such as goodprospect or poor prospect customers

Complicated set of nonlinear equations

See kdnuggets.com to learn more
http://en.wikipedia.org/wiki/Neural_networkshttp://www.kdnuggets.com/software/classification-neural.htmlhttp://www.kdnuggets.com/software/classification-neural.htmlhttp://en.wikipedia.org/wiki/Neural_networks


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Market-Basket Analysis

Market-basket analysis is a data-mining techniquefor determining sales patterns.

Uses statistical methods to identify salespatterns in large volumes of data

Shows which products customers tend to buytogether

Used to estimate probability of customerpurchase

Helps identify cross-selling opportunities

"Customers who bought book X also bought book Y
http://en.wikipedia.org/wiki/Market_basket_analysishttp://en.wikipedia.org/wiki/Market_basket_analysishttp://en.wikipedia.org/wiki/Market_basket_analysishttp://en.wikipedia.org/wiki/Market_basket_analysis


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Hypothetical Sales Data of 1,000

Items at a Dive Shop


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Market-Basket Terminology

SupportProbability that two items will be bought together

Fins and masks purchased together 150 times,thus support for fins and a mask is 150/1,000, or

15 percent Support for fins and weights is 60/1,000, or 6

percent

Support for fins along with a second pair of fins is

10/1,000, or 1 percent


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LiftRatio of confidence to base probability of buying

item

Shows how much base probability increases or

decreases when other products are purchased Example:

Lift of fins and a mask is confidence of fins givena mask, divided by the base probability of fins.

Lift of fins and a mask is .5556/.28 = 1.98


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ConfidenceWhat proportion of the customers who bought a mask also

bought fins?

Conditional probability estimate

Example:

Probability of buying fins = 28%

Probability of buying swim mask = 27%

After buying fins,

Probability of buying mask = 150/270 or 55.56%

Likelihood that a customer will also buy fins almostdoubles, from 28% to 55.56%. Thus, all sales

personnel should try to sell fins to anyone buying amask.


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Decision Trees

Decision tree Hierarchical arrangement of criteria that predict a

classification or value

Unsupervised data-mining technique

Basic idea of a decision tree Select attributes most useful for classifying

something on some criteria that create disparategroups

More different or pure the groups, thebetter the classification
http://en.wikipedia.org/wiki/Decision_Treeshttp://en.wikipedia.org/wiki/Decision_Trees


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Decision Tree

Figure CE16-3

If Senior = Yes If Junior = Yes


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Decision Tree for Loan Evaluation

Common business application Classify loan applications by likelihood of default

Rules identify loans for bank approval

Identify market segment

Structure marketing campaign

Predict problems


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Decision Tree Analysis of

MIS Class Grades

Students characteristics Class (junior or senior), major, employment, age, club

affiliations, and other characteristics

Values used to create groups that were as different as possibleon the classification GPA above or below 3.0

Results

Best criterionClass

Next subdivide Seniors and Juniors into more pure groups

Seniorsbusiness and non-business majors

Juniorsrestaurant employees and non-restaurantemployees

Best classifier is whether the junior worked in a restaurant


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Create Set of If/Then Decision

Rules

If student is a junior and works in a restaurant, thenpredict grade > 3.0.

If student is a senior and is a non-business major,then predict grade < 3.0.

If student is a junior and does not work in arestaurant, then predict grade < 3.0.

If student is a senior and is a business major, then

make no prediction.


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A Decision Tree for a Loan

Evaluation

Classifying likelihood of default Examined 3,485 loans

28 percent of those defaulted

Evaluation criteria

A. Percentage of loan past due less than 50 percent =.94, no default

B. Percentage of loan past due greater than 50percent = .89, default

Subdivide groups A and B each into threeclassifications: CreditScore, MonthsPastDue, andCurrentLTV


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A Decision Tree for a Loan

Evaluation

Resulting rules If the loan is more than half paid, then accept the loan. If the loan is less than half paid and

IfCreditScore is greater than 572.6 and IfCurrentLTVis less than .94, then accept the loan.

Otherwise, reject the loan. Use this analysis to structure a marketing campaign to appeal

to a particular market segment

Decision trees are easy to understand and easy to implementusing decision rules.

Some organizations use decision trees to select variables tobe used by other types of data-mining tools.


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Credit Score Decision Tree

Figure CE14-4


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Study Questions

Q1 Why do organizations need business intelligence?Q2 What business intelligence systems are available?



Q5 What is the purpose of data warehouses anddata marts?



Q8 2020?


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Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall9-49

What Is the Purpose of Data

Warehouses and Data Marts?

Purpose: (video) To extract and clean data from various

operational systems and other sources

To store and catalog data for BIprocessing

Extract, clean, prepare data

Stored in data-warehouse DBMS
http://media.pearsoncmg.com/ph/bp/bp_akamai/mymislab/DMK2_9-2.htmlhttp://media.pearsoncmg.com/ph/bp/bp_akamai/mymislab/DMK2_9-2.html


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Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall9-50

Components of a Data Warehouse


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Data Warehouse Data Sources

Internal operations systems External data purchased from outside

sources

Data from social networking, user-generatedcontent applications

Metadata concerning data stored in data-warehouse meta database

Clickstream dataof customers clickingbehavior on a Web site
http://clickstream.com/http://clickstream.com/http://clickstream.com/http://clickstream.com/


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Example Typical of Customer

Credit Data


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Problems with Operational Data

Dirty datamistakes in spelling or punctuation,incorrect data associated with a field, incomplete oroutdated data or even data that is duplicated in thedatabase.
http://www.webopedia.com/TERM/d/dirty_data.htmlhttp://www.webopedia.com/TERM/d/dirty_data.html


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Examples of Dirty Data

A value of B for customer gender

213 for customer age

Value of 9999999999 for a U.S. phone

number Part color of gren

mail address of [email protected].


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Problems with Operational Data

Too much data causes: Curse of dimensionality

1. Problem caused by the exponential increase in volumeassociated with adding extra dimensions to a

(mathematical) space.

2. Too many rows or data points

3. With more attributes, the easier it is to build a model thatfits the sample data but that is worthless as a predictor.

Major activities in data mining concerns efficient andeffective ways of selecting attributes.
http://en.wikipedia.org/wiki/Curse_of_dimensionalityhttp://en.wikipedia.org/wiki/Curse_of_dimensionality


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Data Warehouses vs. Data Marts

Data mart is a collection of data (video) Created to address particular needs

Business function Problem Opportunity

Smaller than data warehouse

Users may not have data management expertise Need knowledgeable analysts for specific function

Data extracted from data warehouse for afunctional area
http://media.pearsoncmg.com/ph/bp/bp_akamai/mymislab/DMK2_9-3.htmlhttp://media.pearsoncmg.com/ph/bp/bp_akamai/mymislab/DMK2_9-3.html


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Components of a Data Mart


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Study Questions




Q5 What is the purpose of data warehouses and data marts?Q6 What are typical knowledge management

applications?


Q8 2020?


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Knowledge Management (KM)

The process of creating value fromintellectual capital and sharing thatknowledge with employees, managers,suppliers, customers, and others who need it.

Reporting and data mining are used to createnew information from data, knowledge-management systems concern the sharing of

knowledge that is known to exist.


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Primary Benefits of KM

1. KM fosters innovation by encouraging the free flow of ideas.2. KM improves customer service by streamlining response time.

3. KM boosts revenues by getting products and services tomarket faster.

4. KM enhances employee retention rates by recognizing the

value of employees knowledge and rewarding them for it.5. KM streamlines operations and reduces costs by eliminating

redundant or unnecessary processes.

6. KM preserves organizational memory by capturing and storingthe lessons learned and best practices of key employees.

Sharing of Document Content and


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Sharing of Document Content and

Employee Knowledge

Sharing Document Content Collaboration systems are concerned with

document creation and changemanagement, KM applications areconcerned with maximizing content use.

Two Typical Knowledge


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Two Typical Knowledge-

Management Applications

Two key technologies for sharing content in KMsystems:

1. Indexingmost important content function in KMapplications that provide easily accessible and

robust means of determining if content exists anda link to obtain the content. Used in conjunctionwith search functions.

Two Typical Knowledge


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Two Typical Knowledge-

Management Applications

RSS (Real Simple Syndication)a standard for subscribing tocontent sources on Web sites. An RSS Reader program helpsusers to:

Subscribe to content sources.

Periodically check sources for new or updated content through RSS

feeds. Place content summaries in an RSS inbox with link to the full

content.

Think of RSS as an email system for content

Data source must provide what is termed an RSS feed, which

simply means that the site posts changes according to one of theRSS standards.
http://www.whatisrss.com/http://www.whatisrss.com/


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Interface of a Typical RSS Reader

Bl P t f Sh P i t T


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Blog Posts of SharePoint Team

Member


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Expert Systems

Expert systems attempt to capture humanexpertise and put it into a format that can beused by nonexperts.

Expert systems are rule-based systems thatuse If

Then rules similar to those createdby decision-tree analysis, except they arecreated from human experts instead of data-

mining systems.
http://www.aaai.org/aitopics/pmwiki/pmwiki.php/AITopics/ExpertSystemshttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://publib.boulder.ibm.com/infocenter/dmndhelp/v6r1mx/index.jsp?topic=/com.ibm.btools.help.modeler611.doc/doc/tasks/busrulesmodeling/creatingifthenrules.htmlhttp://www.aaai.org/aitopics/pmwiki/pmwiki.php/AITopics/ExpertSystems


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Problems of Expert Systems

1. Difficult and expensive to develop. They requiremany labor hours from both experts in the domainunder study and designers of expert systems.High opportunity cost of tying up domain experts.

2. Difficult to maintain. Nature of rule-based systemscreates unexpected consequences when adding anew rule in middle of hundreds of others. A smallchange can cause very different outcomes.

3. No expert system has the same diagnostic abilityas knowledgeable, skilled, and experienceddoctors. Rules/actions change frequently.


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Expert Systems for Pharmacies

Used as a safety net to screen decisions of doctors and othermedical professionals. These systems help to achievehospitals goal of state-of-the-art, error-free care.

DoseChecker, verifies appropriate dosages on prescriptionsissued in the hospital.

PharmADE, ensures that patients are not prescribed drugsthat have harmful interactions.

Pharmacy order-entry system invokes these applications as aprescription is entered. If either system detects a problem withthe prescription, it generates an alert.
http://medexpert.msi.meduniwien.ac.at/dosechecker_info.htmlhttp://www.openclinical.org/aisp_pharmade.htmlhttp://www.openclinical.org/aisp_pharmade.htmlhttp://medexpert.msi.meduniwien.ac.at/dosechecker_info.html


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Pharmacy Alert


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Study Questions




Q5 What is the purpose of data warehouses and data marts?


Q7 How are business intelligence applications

delivered?

Q8 2020?

H A B i I t lli


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How Are Business Intelligence

Applications Delivered?

What Are the Management


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What Are the Management

Functions of a BI Server?

Maintains metadata about authorized allocation ofBI results to users

Tracks what results are available, what users areauthorized to view those results, and schedule to

provide results to authorized users. Adjustsallocations as available results change andusers come and go.

BI Servers Vary in Complexity and


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Functionality

Some BI servers are simply Web sites fromwhich users can download, orpull BIapplication results.

For example, a BI Web server might postresults of an RFM analysis for salespeopleto query to obtain RFM scores for theircustomers. Management function for such a

site would simply be to track authorizedusers and restrict access.



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Functionality

BI server could operate as a portal server.
http://www.biportal.org/Default.aspx?pageId=90410&mode=PostView&bmi=116408


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BI Portals

Portals might provide common data such as localweather, and links to company news, and to BIapplication results such as reports on daily sales,operations, new employees, and results of data-mining applications.

Authorized users are allowed to place reports,data-mining results, or other BI application resultson their customized pages.

BI application serverpushes the subscribedresults to the user.
http://www.biportal.org/Default.aspx?pageId=90410&mode=PostView&bmi=116408http://www.biportal.org/Default.aspx?pageId=90410&mode=PostView&bmi=116408


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Report Server

A special case of a BI application serverthat serves only reports

BI application servers track results, users,authorizations, page customizations,subscriptions, alerts, and data for any otherfunctionality provided.

What Are the Delivery Functions


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What Are the Delivery Functions

of a BI Server?

Track authorized users Track the schedule for providing results to users

Issue exception alerts that notify users of an exceptional event

Procedures used depends on the nature of the BI system

Procedures tend to be more flexible than those in anoperational system because users of a BI system tend to beengaged in work that is neither structured nor routine

Procedures are determined by unique requirements of users

BI results can be delivered to any device, such as computers,

PDAs, phones, other applications such as Microsoft Office, andas a SOA service


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Study Questions







Q8 2020?


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2020?

Through data mining, companies, known as data aggregators,will know more about your purchasing psyche than you, yourmother, or your analyst.

If you use your card to purchase secondhand clothing, retread

tires, bail bond services, massages, casino gambling or betting

you alert the credit card company of potential financialproblems and, as a result, it may cancel your card or reduceyour credit limit.

Absent laws to the contrary, by 2020 your credit card data willbe fully integrated with personal and family data maintained by

the data aggregators (like Acxiom and ChoicePoint). By 2020, some online retailers will know a lot more about you,

data aggregators, and most consumers purchases than well

know ourselves.

Ethics Guide: The Ethics of


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Ethics Guide: The Ethics of

Classification

Serious problems can arise when classifyingpeople.

What about classifying applicants for college wherethere are more applicants than positions?

Admissions committee uses a decision-tree data-mining program to derive statistically validmeasures. No human judgment was involved.

Decision tree analysis might not include important

data and results may reinforce social stereotypes. Results might not be organizationally, legally, or

socially feasible.

G S S


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Guide: Semantic Security

Security is a difficult problem Unintended release of protected information

Physical security Protect through passwords and permissions

Delivery system must be secure

Semantic security Unintended release of protected information through

release of unprotected reports

Equally serious and more problematic

G id S ti S it


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Guide: Semantic Security

Megan is able to combine data in variousreports to infer protected information aboutcompany employees.

She was not supposed to see thisinformation, but only use reports she wasauthorized to see.

What, if anything, can be done to preventwhat Megan did?

Guide: Data Mining in the


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Guide: Data Mining in the

Real World

Real-world data mining is different from the way it isshown in textbooks because: Data is dirty Values are missing or outside of ranges Time values make no sense

You add parameters as you gain knowledge, forcingreprocessing Over fitting data to a model Results based on probabilities, not certainty Seasonality problems

Should you let people think resulting model makesaccurate predictions?

A ti R i


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Active Review







Q8 2020?

Case Study 9: Business


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Intelligence for Decision Making

at Home Depot Home depot is a major retail chain specializing in construction

and home repair and maintenance products.

Company has 2,200 retail stores worldwide

Generated $71 billion in sales in 2008 Carries more than 40,000 products in its stores and employs

more than 300,000 people

Its stores are visited by more than 22 million people eachweek.



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at Home Depot Suppose you are a buyer for the clothes washer and dryer

product line at Home Depot. You work with seven differentbrands and numerous models within each brand.

One of your goals is to turn your inventory as many times a

year as you can. In order to do so, you want to identify poorlyselling models (and even brands) as quickly as you can.

Risks

New model can quickly capture a substantial portion of anothermodels market share. Thus, a big seller this year can be a dog

(a poor seller) next year Geography: Some brands are unavailable in some countries.

Within a country some sales trends are national, others areregional.



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at Home Depot Assume you have total sales data for each brand

and model, for each store, for each month. Assumealso that you know the stores city and state.


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mechanical, photocopying, recording, or otherwise, without the prior writtenpermission of the publisher. Printed in the United States of America.

Copyright 2011 Pearson Education, Inc.Publishing as Prentice Hall

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