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Chapter 10 - Supporting Decision Making
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Supporting Decision Making
CHAPTER OVERVIEW
Chapter 10: Supporting Decision Making shows how management information systems, decision support
systems, executive information systems, expert systems, and artificial intelligence technologies can be applied to
decision-making situations faced by business managers and professionals in today’s dynamic business environment.
LEARNING OBJECTIVES
After reading and studying this chapter, you should be able to:
1.
Identify the changes taking place in the form and use of decision support in business.
2.
Identify the role and reporting alternatives of management information systems.
3.
Describe how online analytical processing can meet key information needs of managers.
4.
Explain the decision support system concept and how it differs from traditional management information
systems.
5.
Explain how the following information systems can support the information needs of executives, managers, and
business professionals:
a. Executive information systems
b. Enterprise information portals
c. Knowledge management systems
6.
Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used
in business.
7.
Give examples of several ways expert systems can be used in business decision-making situations.
SUMMARY
• Information, Decisions, and Management. Information systems can support a variety of management decisionmaking levels and decisions. These include the three levels of management activity (strategic, tactical, and
operational decision making) and three types of decision structures (structured, semistructured, and unstructured).
Information systems provide a wide range of information products to support these types of decisions at all levels of
the organization.
• Decision Support Trends. Major changes are taking place in traditional MIS, DSS, and EIS tools for providing
the information, and modeling managers need to support their decision making. Decision support in business is
changing, driven by rapid developments in end-user computing and networking; Internet and Web technologies; and
Web-enabled business applications. The growth of corporate intranets and extranets, as well as the Web, has
accelerated the development of “executive-class” interfaces like enterprise information portals and Web-enabled
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Chapter 10 - Supporting Decision Making
business intelligence software tools, as well as their use by lower levels of management and individuals and teams of
business professionals. In addition, the growth of e-commerce and e-business applications has expanded the use of
enterprise portals and DSS tools by the suppliers, customers, and other business stakeholders of a company.
• Management Information Systems. Management information systems provide pre-specified reports and
responses to managers on a periodic, exception, demand, or push reporting basis to meet their need for information
to support decision making.
• OLAP and Data Mining. Online analytical processing interactively analyzes complex relationships among large
amounts of data stored in multidimensional databases. Data mining analyzes the vast amounts of historical data that
have been prepared for analysis in data warehouses. Both technologies discover patterns, trends, and exception
conditions in a company’s data that support business analysis and decision making.
• Decision Support Systems. Decision support systems are interactive, computer-based information systems that
use DSS software and a model base and database to provide information tailored to support semi-structured and
unstructured decisions faced by individual managers. They are designed to use a decision maker’s own insights and
judgments in an ad hoc, interactive, analytical modeling process leading to a specific decision.
• Executive Information Systems. Executive information systems are information systems originally designed to
support the strategic information needs of top management; however, their use is spreading to lower levels of
management and business professionals. EIS are easy to use and enable executives to retrieve information tailored to
their needs and preferences. Thus, EIS can provide information about a company’s critical success factors to
executives to support their planning and control responsibilities.
• Enterprise Information and Knowledge Portals. Enterprise information portals provide a customized and
personalized Web-based interface for corporate intranets to give their users easy access to a variety of internal and
external business applications, databases, and information services that are tailored to their individual preferences
and information needs. Thus, an EIP can supply personalized Web-enabled information, knowledge, and decision
support to executives, managers, and business professionals, as well as to customers, suppliers, and other business
partners. An enterprise knowledge portal is a corporate intranet portal that extends the use of an EIP to include
knowledge management functions and knowledge base resources so that it becomes a major form of knowledge
management system for a company.
• Artificial Intelligence. The major application domains of artificial intelligence (AI) include a variety of
applications in cognitive science, robotics, and natural interfaces. The goal of AI is the development of computer
functions normally associated with human physical and mental capabilities, such as robots that see, hear, talk, feel,
and move, and software capable of reasoning, learning, and problem solving. Thus, AI is being applied to many
applications in business operations and managerial decision making, as well as in many other fields.
• AI Technologies. The many application areas of AI are summarized in Figure 10.26 , including neural networks,
fuzzy logic, genetic algorithms, virtual reality, and intelligent agents. Neural nets are hardware or software systems
based on simple models of the brain’s neuron structure that can learn to recognize patterns in data. Fuzzy logic
systems use rules of approximate reasoning to solve problems when data are incomplete or ambiguous. Genetic
algorithms use selection, randomizing, and other mathematic functions to simulate an evolutionary process that can
yield increasingly better solutions to problems. Virtual reality systems are multisensory systems that enable human
users to experience computer-simulated environments as if they actually existed. Intelligent agents are knowledgebased software surrogates for a user or process in the accomplishment of selected tasks.
• Expert Systems. Expert systems are knowledge-based information systems that use software and a knowledge
base about a specific, complex application area to act as expert consultants to users in many business and technical
applications. Software includes an inference engine program that makes inferences based on the facts and rules
stored in the knowledge base. A knowledge base consists of facts about a specific subject area and heuristics (rules
of thumb) that express the reasoning procedures of an expert. The benefits of expert systems (such as preservation
and replication of expertise) must be balanced with their limited applicability in many problem situations.
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Chapter 10 - Supporting Decision Making
KEY TERMS AND CONCEPTS
1.
Analytical Modelling ():
Analytical modelling involves the interactive use of computer-based mathematical models to explore decision
alternatives using what-if analysis, sensitivity analysis, goal-seeking analysis, and optimization analysis.
a.
Goal-Seeking Analysis ():
Making repeated changes to selected variables until a chosen variable reaches a target value.
b.
Optimization Analysis ():
Finding an optimum value (usually maximum or minimum value) for a selected variable in a mathematical
model, given certain constraints.
c.
Sensitivity Analysis ():
Observing how repeated changes to a single variable affects other variables in a mathematical model.
d.
What-if Analysis ():
Observing how changes to selected variables affect other variables in a mathematical model.
2.
Artificial Intelligence ():
The goal of AI is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and
feel. A major thrust of artificial intelligence is the simulation of computer functions normally associated with
human intelligence, such as reasoning, learning, and problem solving
3.
Business Intelligence ():
Business intelligence comprises “concepts and methods to improve business decision making by using factbased support systems.”
4.
Data Mining ():
Using special-purpose software to analyze data from a data warehouse to find patterns and trends.
5.
Data Visualization Systems ():
Data visualization systems are systems that represent complex data using interactive three-dimensional
graphical forms such as charts, graphs, and maps. These tools help users to interactively sort, subdivide,
combine, and organize data while it is in its graphical form.
6.
Decision Structure ():
Information systems that support a variety of management levels and decisions. These include the three levels
of management activity (strategic, tactical, and operational), and three types of decision structures (structured,
semi-structured, and unstructured).
7.
Decision Support System ():
Decision support systems are computer-based information systems that provide interactive information support
to managers and business professionals during the decision making process.
8.
Enterprise Information Portal ():
EIPs provide web-enabled access to databases and decision support to executives, managers, employees,
suppliers, customers, and other business partners via a single, customizable interface.
9.
Enterprise Knowledge Portal ():
Enterprise knowledge portals provide access to enterprise knowledge bases through a single, customizable
interface.
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Chapter 10 - Supporting Decision Making
10. Executive Information System ():
An information system that provides strategic level information tailored to the needs of top management.
11. Expert system ():
A computer-based information system that uses a complex rule set to process inputs and acts as a consultant.
12. Expert System Shell ():
The software and user interface that allows knowledge engineers to build rule-bases.
13. Fuzzy Logic ():
A computer-based system that produces approximated answers given incomplete or partially incorrect data.
14. Genetic Algorithms ():
Sets of mathematical process rules (algorithms) that simulate the evolutionary (Darwinian) process in order to
arrive at an optimized solution.
15. Geographic Information System ():
Are a special category of a DSS that constructs and displays maps and other graphics displays that support
decisions affecting the distribution of people and other resources.
16. Inference Engine ():
The algorithms that process rules and facts and makes associations resulting in a recommended course of action.
17. Intelligent Agent ():
A software based user surrogate for gathering and processing information.
18. Knowledge Base ():
A computer-accessible collection of knowledge about a subject in a variety of forms, such as facts and rules of
inference, frames, and objects.
19. Knowledge Engineer ():
A specialist who works with experts to capture the knowledge they possess in order to develop a knowledge
base for expert systems and other knowledge-based systems.
20. Knowledge Management System ():
Knowledge management systems help organize and share unstructured information within an organization.
21. Management Information System ():
A management information system An MIS produces information products that support many of the day-to-day
decision-making needs of managers and business professionals using pre-specified reports, displays, and
responses on a periodic, exception, or demand basis.
22. Model Base ():
Model bases are an organized collection of conceptual, mathematical, and logical models that express business
relationships, computational routines, or analytical techniques. Such models are stored in the form of programs.
23. Neural Network ():
Neural networks are a system's architecture or programming logic that is based on the human brain’s mesh-like
structure. Such networks can learn to recognize patterns and programs themselves to solve problems without
human programming.
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Chapter 10 - Supporting Decision Making
24. Online Analytical Processing (OLAP) ():
OLAP is a system using multidimensional databases to help managers analyze transaction data summaries to
uncover patterns, trends, and exception conditions.
25. Robotics ():
Robotics technology produces machines with computer intelligence and computer-controlled, humanlike
physical capabilities.
26. Virtual Reality ():
The use of audio, visual, and tactile human/computer interfaces to enable human users to experience computergenerated environment.
ANSWERS TO REVIEW QUIZ
Q.
A.
Key Term
Q.
A.
Key Term
1
Decision structure
16
Knowledge management system
2
Executive Information System
17
Enterprise knowledge portal
3
Management information system
18
Artificial intelligence
4
Decision Support System
19
Robotics
5
Business intelligence
20
Virtual reality
6
Model base
21
Geographic information systems (GIS)
7
Analytical modeling
22
Expert system (ES)
8
What-if analysis
23
Knowledge base
9
Sensitivity analysis
24
Inference engine
10
Goal-seeking analysis
25
Expert system shell
11
Optimization analysis
26
Knowledge engineer
12
Online analytical processing (OLAP)
27
Neural network
13
Data mining
28
Fuzzy logic
14
Data visualization system
29
Intelligent agent
15
Enterprise information portal (EIP)
30
Genetic algorithms
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Chapter 10 - Supporting Decision Making
ANSWERS TO DISCUSSION QUESTIONS
1.
Are the form and use of information and decision support systems for managers and business
professionals changing and expanding? Why or why not?
Yes Changes are driven by the rapid developments in end user computing and networking as well as the rapid
adoption of the Internet, web browsers, and e-commerce activities. The growth of corporate intranets and
extranets has accelerated the development of “executive class” interfaces. The expansion of e-commerce has
increased the use of enterprise portals and DSS tools by the suppliers, customers, and other business
stakeholders of a company.
2.
Has the growth of self-directed teams to manage work in organizations changed the need for strategic,
tactical, and operational decision making in business?
Self-directed teams were largely a fad a decade ago. The basics for decision making have not changed
materially. Strategic, tactical, and operational decision making continues to be carried out in organizations
regardless of how the work is completed. What has changed are the tools available for managing and
completing projects.
3.
What is the difference between the ability of a manager to retrieve information instantly on demand
using an MIS versus the capabilities provided by a DSS?
Flexibility
Managers have traditionally relied on the capabilities of a management information system. These systems
produce pre-programmed reports. A DSS is more flexible and supports ad-hoc requests. They provide the
capabilities for managers to participate in interactive analytical modeling. They are designed to use decision
maker’s own insights and judgments about what information they need and the form it should take in order to
make a decision.
4.
Refer to the Real World Challenge at the beginning of the chapter. Does DHL stand to lose anything by
integrating its operations under a single, global brand? If so, what? Is there a way to keep the best of
both alternatives?
DHL is in the process of unifying its many brand names under a single international brand name and company.
Customers who are used to dealing with a smaller, localized brand may see this as a loss of control over their
products, and may vision the loos of personalized services. DHL recognizes the strengths of allowing
marketing to be handled separately in each country and locale, so they do recognize the strengths of this
localized, personalized service. However, they have determined to become an international service and wish to
pursue the larger and (hopefully) more profitable venture of international service and a single international
brand name. They believe that a single name covering all their operations will be stronger than various and
different names in different locales providing the same service.
What they stand to lose is the customer perception that DHL, as an international service provider, gives
localized and personalized services. The way to keep the customer perception of personalized service is to
provide exactly that on a localized basis, while still providing the international services for which they are
known internationally. This means training local employees to provide and to focus on personalized customer
services. By doing so, DHL will retain its local reputation of the previous brand names while coordinating them
all under the umbrella of the international name DHL.
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Chapter 10 - Supporting Decision Making
5.
In what ways does using an electronic spreadsheet package provide you with the capabilities of a decision
support system?
Spreadsheet's similarities to a DSS:
 Statistical tools
 Data management tools
 Programming tools
 Graphics generating tools
 Reporting tools
 Goal seeking & optimization tools
 Modeling tools
 Cross-tabulation tools
6.
Are enterprise information portals making executive information systems unnecessary? Explain your
reasoning.
EIPs are deployed by organizations as a way to provide web-enabled information, knowledge, and decision
support to executives, managers, employees, suppliers, customers, and other business partners. EISs on the
other hand, are designed to provide strategic information tailored to the needs of top management.
Whether or not EIP’s will eventually make EIS systems unnecessary is a matter of debate. Students may agree
that as more and more enriched features are added to EIP systems their importance will be heightened. On the
other hand, EIS systems are also being developed with enriched features such as Web browsing, electronic mail,
groupware tools, DSS, and expert systems capabilities to make them even more useful to managers and business
professionals.
One might argue that these tools will merge.
7.
Refer to the Real World Solution in the chapter. How would a local marketing manager decide that the
tool is no longer applicable, or maybe to a particular situation? If the use of the tool eventually does away
with the need for local marketing managers, then what?
A local marketing manager might decide that the tool is not measuring properly, or is measuring incorrectly, or
that he/she has figures that show to tool to be incorrect. It is important to note that the tool measures customer
“perception” of the brand name and service, not necessarily actual service. If the manager believes the tool is
not working correctly, or that the tool was not reliable, or was no longer applicable, he/she would need to show
figures that prove the tool is not giving the correct figures. This might be difficult to do if studies of bottom line
improvements from decisions based on the tool continue to be favorable and show increased
sales/revenues/profits. But actually providing numbers that show the fallacies of the tool would be the best way
to prove the tool is flawed or no longer applicable in a certain region.
If it were true that the tool had outlived its usefulness then it would need to be replaced with a tool that did the
job correctly and perception would need to be reevaluated in light of the new tool.
8.
Can computers think? Will they ever be able to? Explain why or why not.
Yes. If by thinking we mean employ reasoning to solve problems. We simply program in specific sets of
reasoning skills.
No. The human brain is too complex to model. Computers will never be able to match human thinking,
reasoning, or creativity.
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Chapter 10 - Supporting Decision Making
9.
Which applications of AI have the most potential value for use in the operations and management of a
business? Defend your choices.
Fuzzy logic: machine control (at the sensor/movement level)
Neural networks: pattern recognition, visual processing
Genetic algorithm: product design, engineering
Expert system: diagnostics, troubleshooting, product configuration, process control
10. What are some of the limitations or dangers you see in the use of AI technologies such as expert systems,
virtual reality, and intelligent agents? What could be done to minimize such effects?
Limitations:
First, these systems may put people out of work, just as robots did with many production line employees.
Second, there will likely be a human bias against a computer's "cold logic" or a neural system's inability to
explain its reasoning. Third, AI applications are still limited in scope and generally susceptible to becoming out
of date as the business environment evolves.
Dangers:
Will these systems make targeting decisions in war? Will these systems decide who will receive medical care
and of what type? These are life and death decisions, and any failure might result in death. Even if these
systems work as well as or better than humans, organizations employing these technologies in these capacities
will face backlash from Luddites, victims, and pandering politicians.
Minimizing effects:
Human supervision, monitoring, and review might help ensure that an AI system avoids obvious mistakes.
ANSWERS TO ANALYSIS EXERCISES
1.
BizRate.com: eCommerce Website Reviews
a.
Use BizRate.com to check out a product of interest. How thorough, valid, and valuable were the product
and retailer reviews to you? Explain.
When searching for digital cameras I found users could select cameras by price, megapixels, type of user, built
in zoom factor, LCD size, camera type, and brand. This helps the user identify camera models that fit their
criteria.
b.
How could nonretail businesses use a similar Web-enabled review system? Give an example.
Many examples exist. Some news media outlets use review systems to build community. Readers are invited
to comment on stories. In addition to building community, this may provide a mechanism for editors to monitor
consumer preferences. Some media outlets use selected participant quotes as part of their story follow-ups.
Some software companies use a review system that allows end users to rate the usefulness of their help
instructions and search engine results. Facebook takes an interesting approach to ad reviews. Users are invited
to rate advertising that appears on their page using a thumbs up/thumbs down icon and a combo-box of reasons
why they did or did not like the ad. This innovative approach allows Facebook to better target its ads, and it
encourages users to look at the ads in the first place.
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Chapter 10 - Supporting Decision Making
c.
How is BizRate’s Web site functionality similar to a decision support system (DSS)?
Decision support systems provide summaries of critical information, real-time monitoring, and exception
reporting to decision makers. They also allow decision makers to drill down into the information in order to
receive more detailed information on specific topics. BizRate.com is similar to a DSS in those regards. One
could easily imagine a similarly designed system that provided information about authorized vendors, products,
bids, availability, performance, order tracking, and account status to an organization's purchasing agents.
2.
Enterprise Application Integration
a.
Visit one of the portal sites listed above. Configure the site to meet your own information needs. Provide a
printout of the result.
This exercise will give students some first hand experience with the topic. Individual results will vary.
b.
Look up Digital Dashboard on the 20/20 Software Web site ( http://www.2020software.com/ ), read about
products with this feature, and describe these products in your own words.
Various types of features include web accessibility to support mobile executives, analytical tools, single page
point of access, the ability to drill down into detail, and customizable multiple information sources. Information
sources include personal (e-mail, calendar, etc.), corporate, and external databases.
3.
Case Based Marketing
a.
What is the source of expertise behind Amazon’s online book recommendations?
While the logic used by Amazon remains proprietary, the website suggests that its expertise comes from a
history of customer purchases.
b.
How do you feel about online merchants tracking your purchases and using this information to
recommend additional purchases?
This would make a good discussion question. Consider introducing Opt-in v. Opt-out approaches as a
discussion subject. Most students probably favor lower prices and higher quality recommendations, but this
assumption should be tested.
c.
What measures protect consumers from the government’s obtaining their personal shopping histories
maintained by Amazon?
Various laws and courts protect privacy. These laws and practices may be changed or broken. Thus, in the
long term, no effective constraints protect consumers from their government's inquisitiveness.
d.
Although Amazon doesn’t share personal information, it still capitalizes on its customers’ shopping data.
Is this ethical? Should Amazon offer its customers the right to opt out of this information gathering?
Student opinions will vary. Even in brick-and-mortar stores, successful merchants capitalize on what they learn
from their customers. Due to privacy issues discussed in the question above, it seems reasonable to offer
customers the opportunity to opt out of any data gathering.
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Chapter 10 - Supporting Decision Making
e.
What obligations do organizations have to secure your private data from unauthorized access?
There is a wealth of information that should be secured from prying eyes on the Internet. Some of these data
are:
a)
names
b) addresses
c)
social security numbers
d)
credit card numbers
e)
bank account numbers
f)
personally identifying information
g) passwords
Different states have differing regulations regarding such information. Some industries have accepted certain
data as being private. Legal questions abound and courts address this question, changing the laws with some
frequency. Answers to this question may vary based on location, background, the latest court cases, and
personal opinions.
4.
Palm City Police Department
a.
Build a spreadsheet to perform this analysis and print it out.
See Analysis Exercise Data Solutions files [Chapter 10 - Solutions.xls]
b.
Currently, no funds are available to hire additional officers. On the basis of the citywide ratios, the
department has decided to develop a plan to shift resources as needed to ensure that no precinct has more
than 1,100 residents per police officer and no precinct has more than seven violent crimes per police
officer. The department will transfer officers from precincts that easily meet these goals to precincts that
violate one or both of these ratios. Use “goal seeking” on your spreadsheet to move police officers
between precincts until the goals are met. You can use the goal-seek tool to see how many officers would
be required to bring each precinct into compliance and then judgmentally reduce officers in precincts
that are substantially within the criteria. Print out a set of results that allow the departments to comply
with these ratios and a memorandum to your instructor summarizing your results and the process you
used to develop them.
See Analysis Exercise Data Solutions files [Chapter 10 - Solutions.xls]
5. Are You Getting all the Information You Need?
a) Recruit a classmate who routinely uses the same search engine you use. Conduct identical searches. What
search engine did you use? Did you indeed get different results?
Searches conducted using lab computers may return the same results. Encourage students to use their own
computers.
b) Analyze the first page of results from the experiment in question "a". How are your results different?
What assumptions did the search engine make about your preferences?
Results may be more or less business, culture, fashion, politically, geographically, or academically oriented
among others.
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Chapter 10 - Supporting Decision Making
c)
Assume you have been tasked with finding a suitable location in the U.S. for a new manufacturing site.
How might a search engine's assumptions affect your research regarding public acceptance?
Searches oriented toward identifying a location's business, economic, and political climate may be skewed by
the search engine resulting in an incomplete view of the region. Incomplete information could lead to a bad
decision.
ANSWERS TO REAL WORLD CHALLENGE/SOLUTION
Real World Challenge
1.
What are some of the ways in which local marketing managers can choose to invest their marketing
budgets? What are the pros and cons of each? How would you make that allocation decision?
Marketing outlets

print
pros: cheap; flexible; temporary
cons: limited reach; no click-through (depending on QR Code penetration in the market)

billboards
pros: hard to miss or ignore; high repeat exposure rates
cons: limited reach; often perceived as a blight; deteriorates over time; no click-through

signage
pros: helps customers locate the business; customer can just walk in; lasting
cons: heavily regulated by local ordinances; limited reach

radio
pros: cheap; flexible; temporary
cons: limited reach; no click-through
television
pros: may be attention getting; good audience demographic data available
cons: expensive; no click-through
online
pros: click-through to sales; data regarding reach, response/conversion rates available; cheap
cons: limited reach



direct mailing
pros: customizable, targeted, click through (with QR Codes)
cons: expensive, often ignored, no repeat exposure

local sponsorships (youth teams, fundraisers, etc.)
pros: cheap; builds relationships within the community
cons: limited exposure; no click-through
Allocation rationale
 personal experience
 performance metrics
2.
Consider the four questions that the company is attempting to answer. How can technology help with
those kinds of situations? How would you apply technology to this problem?
Note: the case does not specifically address these points, so answers will vary significantly
Potential brand growth
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Chapter 10 - Supporting Decision Making



business intelligence applications
economic modeling applications
marketing information systems
Brand investments
 marketing information systems
Key attributes
 market research
Marketing budget allocation
 market research
How to applied technologies
 sponsor research
 gather data from internal and external sources
 use analysis tools
 combine information with experience to make informed decisions
3.
Is it possible for information technology to supplement (or even replace) the expertise, experience, and
judgment of a local marketing manager who knows his industry and/or country? Why?
Can technology help?
 technology can provide the local expert with hard data
 technology can provide the local expert with the tools necessary to analyze data
Can technology replace local managers?
 no, marketing depends significantly on building local relationships
 it's not likely technology will succeed in building local relationships
 experience plays an important role in interpreting data
Real World Solution
1.
What are some of the assumptions on which the new allocation tool was built? How likely are those
assumptions to stay the same in the future? In other words, how enduring would this tool be?
Assumptions
 consumers progress through a series of steps in the purchasing process that represent different stages of
consumer development
o brand awareness
o brand consideration
o brand usage
o choice of main provider
o choice of sole provider
Stability
 the model is fundamental to marketing and likely very stable
 the data feeding into the model will be updated every five years
2.
What are some of the challenges Deutsche Post DHL may face as it seeks to implement the new tools
worldwide? How do these challenges change the job of the local marketing managers?
Challenges
 cultural
 legal
 skills
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Chapter 10 - Supporting Decision Making
Effect


they increase the job's complexity - having to adapt global practices to local standards
the require local marketing managers to learn and use new technologies
ANSWERS TO REAL WORLD CASES
RWC 1: Valero Energy, Elkay Manufacturing, J&J, and Overstock.com
Case Study Questions
1.
What is the difference between a “dashboard” and a “scorecard”? Why is it important that managers
know the difference between the two? What can they learn from each?
Differences
Dashboard
 Not attached to a methodology
 Monitors the health of a business
 Flexible
Scorecard
 Tracking against defined metrics
 Attached to a methodology
Importance
Dashboards and scorecards are hot topics. Managers should understand the differences and implications of
these differences in order to be conversant in these subjects. The outputs from these systems may help
determine, in large part, their performance review.
Utility
Scorecards measure the organization's progress against a defined set of objectives. They help managers stay
focused on achieving predefined goals. As a result, scorecards are very team-oriented.
Dashboards provide operational measures. They leave it up to managers how to interpret and use them. This
gives managers greater flexibility in determining when and how to act.
2.
In what ways have the companies mentioned in the case benefited from their adoption of “fact-based”
decision making? Provide several examples from the case to illustrate your answer.
Benefits
 Improved efficiency
 Develop innovative products
 Get closer to customers
 Outsell competitors
Examples
 Valero's system gives managers time to take corrective action – specifically in energy consumption.
 Elkey's system helps its sales staff focus on profits and not just revenues.
 Johnson & Johnson uses metrics as the basis for their continuing improvement cycles.
 Overstock.com's system helps drive its CEO's daily call schedule.
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© 2013 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
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Chapter 10 - Supporting Decision Making
3.
Information quality is central to the approach toward decision making taken by these organizations.
What other elements must be present for this approach to be successful (technology, people, culture, and
so forth)?
Required elements
 Forward looking metrics
 Appropriate goals
 Up to date budgeting approaches
 Technology literate executives
 An organizational culture prepared to share key performance indicators
 Executives prepared to draw insights from information
Real World Activities
1.
A number of major companies have launched projects geared toward improving their business analytics
and decision-making capabilities in the last few years. Go online and research other examples in this
trend. What are the similarities with the ones chronicled in the case? What are the differences? Prepare a
report that includes a section contrasting your new examples with the ones in the case.
Search terms
"digital dashboards", "scorecard dashboard"
2.
If you had to apply the ideas discussed in the case to your academic career, what would your dashboard
and/ or scorecard look like? What would be the sources of information? How you would measure
whether you are making progress toward attaining your goals? Break into small groups to discuss these
issues.
Data sources
 Online sources (Blackboard, etc for grades, syllabus, assignments)
 Syllabus
 Class notes
 University catalogue
 Job placement services
 Self monitoring (e.g. study time)
 Prospective employers
 Grades returned
Metrics
 Hours studied per week (graph)
 Assignments outstanding (count)
 Assignments late (count)
 Next assignment due (date)
 Next test subject
 Next test date
 Current grade average by class
 Assignment (task) list (drill down)
 Progress toward degree (chart)
 Progress toward awesome job
 Non-academic tasks (drill down from awesome job)
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© 2013 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.
Chapter 10 - Supporting Decision Making
RWC 2: Kimberly-Clark Corp.
Case Study Questions
1.
What are the business benefits derived from the technology implementation described in the case? Also
discuss benefits other than those explicitly mentioned in the case.
Benefits – case
 Rapid testing
 Secrecy
 Presentations
 Market research
Benefits – other
 "Halo effect"1
 Demographic profiling (do all customers shop the same way?)
 Hiring (people want to work for companies with leading edge technology)
2.
Are virtual stores like this one just an incremental innovation on the way marketing tests new product
designs? Or do they have the potential to radically reinvent the way these companies work? Explain your
reasons.
Incremental innovation
Kimberly-Clark is just automating/virtualizing existing methods. Otherwise, testing products, packaging,
placement, and promotions by gauging test subjects' responses is nothing new.
Radical reinvention
Conventional testing has never been able to track body and eye movements let alone correlate them against
purchase choices. Likewise, the speed at which Kimberly-Clark can test new ideas is a game-changer.
3.
What other industries could benefit from deployments of virtual reality like the one discussed in the case?
Leaving aside the cost of the technology, what new products or services could you envision within those
industries? Provide several examples.
Other industries
 Entertainment
 Engineering
 Real-estate
 Military
 Adult entertainment
 Education
New products/services
 Kitchen design
 Interior design
 Architectural design
 Home sales
 Games
 Battlefield simulators
1
http://en.wikipedia.org/wiki/Halo_effect
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© 2013 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.
Chapter 10 - Supporting Decision Making
Real World Activities
1.
What is the current cutting-edge technology in virtual reality, and how are companies using it? Go online
to research this topic and prepare a presentation to share your work.
A search on "virtual reality" through Google News will turn up numerous current applications.
2.
With technologies like these, will consumers entirely do away with retailers sometime in the future,
shopping only through virtual representations of a retail store? Will consumers even want it to look like a
retail store? Break into small groups to propose arguments for and against these questions.
Yes – customers will appreciate the efficiency and cost savings associated with shopping online in virtual
reality.
No – online shopping for non-digital products can't provide instant gratification. Besides, shopping is a
physical event that can't be enjoyed in the same manner online.
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© 2013 by McGraw-Hill Education. This is proprietary material solely for authorized instructor use. Not authorized for sale or distribution in any
manner. This document may not be copied, scanned, duplicated, forwarded, distributed, or posted on a website, in whole or part.