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Chapter 12

Enhancing Decision Making


After reading this chapter, you will be able to answer the following questions:


What are the different types of decisions and how does the decision
process work?


How do information
systems support the activities of managers and management
decision making?


How do decision
support systems (DSS) differ from MIS and how do they
provide value to the business?


How do executive support systems (ESS) help senior managers make better


What is the role of information systems in helping people working in a group
make decisions more efficiently?




Business Value of Improved Decision Making

Types of Decisions

Making Process

Managers and Decision Making in the Real World



Management Information Systems (MIS)

Support Systems (DSS)

Data Visualization and Geographic Information Systems

Based Customer Decision
upport Systems

Group Decision
Support Systems (GDSS)



The Role of Executive Support Systems in the Firm

Business Value of Executive Support Systems



Management Decision Problems

Improving Decision Making: Using Pivot Tables to Analyze Sales Data

Improving Decision Making: Using a Web
Based DSS for Retirement Planning


Building and Using Pivot Tables

Interactive Sessions:

Too Ma
ny Bumped Fliers: Why?

Business Intelligence Turns Dick’s Sporting Goods into a Winner


Founded in 1967 by two rock climbers, Eastern Mountain Sports (EMS) has grown into one
of the leading outdoor
specialty retailers in the United States, with more than 80 retail stores
in 16 states, a seasonal catalogue, and a growing online presence. EMS designs and offers a
wide variety of gear and clothing for outdoor enthusiasts.

Until recently, however, the co
mpany’s information systems for management reporting were
dated and clumsy. It was very difficult for senior management to have a picture of customer
purchasing patterns and company operations because data were stored in disparate sources:
legacy merchandi
sing systems, financial systems, and point
sale devices. Employees
crafted most of the reports by hand, wasting valuable people resources on producing
information rather than analyzing it.

After evaluating several leading business intelligence products,

EMS selected WebFOCUS
and iWay middleware from Information Builders Inc. EMS believed WebFOCUS was better
than other tools in combining data from various sources and presenting the results in a user
friendly view. It is Web
based and easy to implement, ta
king EMS only 90 days to be up and

IWay extracts point
sale data from EMS’s legacy enterprise system running on an IBM
AS/400 midrange computer and loads them into a data mart running Microsoft’s SQL Server
database management system. WebFOCUS
then creates a series of executive dashboards
accessible through Web browsers, which provide a common view of the data to more than
200 users at headquarters and retail stores.

The dashboards provide a high
level view of key performance indicators such as
inventory, and margin levels, but enable users to drill down for more detail on specific
transactions. Managers for merchandising monitor inventory levels and the rate that items
turn over. E
commerce managers monitor hour
hour Web sales, visitor
s, and conversion
rates. A color
coded system of red, yellow, and green alerts indicates metrics that are over,
under, or at plan.

EMS is adding wikis and blogs to enable managers and employees to share tips and initiate
dialogues about key pieces of data.

For example, in identifying top
selling items and stores,
EMS sales managers noticed that inner soles were moving very briskly in specialty stores.
These stores had perfected a multi
step sales technique that included the recommendation of
socks designed
for specific uses, such as running or hiking, along with an inner sole custom
fit to each customer. Wikis and blogs made it easier for managers to discuss this tactic and
share it with the rest of the retail network.

Longer term, EMS is planning for more d
etailed interactions with its suppliers. By sharing
inventory and sales data with suppliers, EMS will be able to quickly restock inventory to
meet customer demand, while suppliers will know when to ramp up production.

Eastern Mountain Sports’ executive
dashboards are a powerful illustration of how information
systems improve decision making. Management was unable to make good decisions about how
and where to stock stores because the required data were scattered in many different systems
and were difficul
t to access. Management reporting was excessively manual. Bad decisions
about how to stock stores and warehouses increased operating costs and prevented EMS stores
from responding quickly to customer needs.

EMS management could have continued to use its ou
tdated management reporting system or
implemented a large
scale enterprise
wide database and software, which would have been
extremely expensive and time
consuming to complete. Instead, it opted for a business
intelligence solution that could extract, cons
olidate, and analyze sales and merchandising data
from its various legacy systems. It chose a platform from Information Builders because the
tools were user
friendly and capable of pulling together data from many different sources.

The chosen solution popu
lates a data mart with data from point
sale and legacy systems and
then pulls information from the data mart into a central series of executive dashboards visible
to authorized users throughout the organization. Decision
makers are able to quickly acces
s a
unified high
level view of key performance indicators such as sales, inventory, and margin
levels or drill down to obtain more detail about specific transactions. Increased availability of
this information has helped EMS managers make better decisions
about increasing sales,
allocating resources, and propagating best practices.






Decision making in businesses used to be limited to management. Today, lower
employees are responsible for some of these dec
isions, as information systems make
information available to lower levels of the business. But what do we mean by better decision
making? How does decision making take place in businesses and other organizations? Let’s
take a closer look.



What does it mean to the business to make better decisions? What is the monetary value of
improved decision making?
Table 12

attempts to measure the monetary value of
improved decision making for a small U.S. manufacturing firm
with $280 million in annual
revenue and 140 employees. The firm has identified a number of key decisions where new
system investments might improve the quality of decision making. The table provides
selected estimates of annual value (in the form of cost s
avings or increased revenue) from
improved decision making in selected areas of the business.

We can see from
Table 12

that decisions are made at all levels of the firm and that some
of these decisions are common, routine, and numerous. Although the valu
e of improving
any single decision may be small, improving hundreds of thousands of “small” decisions
adds up to a large annual value for the business.


Chapters 1


showed that there are different levels in an organization. Each of t
levels has different information requirements for decision support and responsibility for
different types of decisions (see
Figure 12
). Decisions are classified as structured,
semistructured, and unstructured.








Allocate support to most valuable customers

Accounts manager


$ 100,000


Predict call center daily

Call center management




Decide parts inventory levels daily

Inventory manager




Identify competitive bids from major suppliers

Senior management




Schedule production to fill orders

anufacturing manager




Allocate labor to complete a job

Production floor manager






Senior managers, middle managers, operational
managers, and employees have
different types of decisions and information requirements.

Unstructured decisions

are those in which the decision maker must provide judgment,
evaluation, and insight to solve the problem. Each of these decisions is novel, impo
and nonroutine, and there is no well
understood or agreed
on procedure for making them.

Structured decisions
, by contrast, are repetitive and routine, and they involve a definite
procedure for handling them so that they do not have to be treated eac
h time as if they were
new. Many decisions have elements of both types of decisions and are
where only part of the problem has a clear
cut answer provided by an accepted procedure.
In general, structured decisions are more prevalent at lowe
r organizational levels, whereas
unstructured problems are more common at higher levels of the firm.

Senior executives face many unstructured decision situations, such as establishing the
firm’s five

or ten
year goals or deciding new markets to enter. Ans
wering the question
“Should we enter a new market?” would require access to news, government reports, and
industry views as well as high
level summaries of firm performance. However, the answer
would also require senior managers to use their own best judgm
ent and poll other managers
for their opinions.

Middle management faces more structured decision scenarios but their decisions may
include unstructured components. A typical middle
level management decision might be
“Why is the reported order fulfillment r
eport showing a decline over the past six months at
a distribution center in Minneapolis?” This middle manager will obtain a report from the
firm’s enterprise system or distribution management system on order activity and
operational efficiency at the Minn
eapolis distribution center. This is the structured part of
the decision. But before arriving at an answer, this middle manager will have to interview
employees and gather more unstructured information from external sources about local
economic conditions
or sales trends.

Operational management and rank
file employees tend to make more structured
decisions. For example, a supervisor on an assembly line has to decide whether an hourly
paid worker is entitled to overtime pay. If the employee worked more t
han eight hours on a
particular day, the supervisor would routinely grant overtime pay for any time beyond eight
hours that was clocked on that day.

A sales account representative often has to make decisions about extending credit to
customers by consultin
g the firm’s customer database that contains credit information. If
the customer met the firm’s prespecified criteria for granting credit, the account
representative would grant that customer credit to make a purchase. In both instances, the
decisions are
highly structured and are routinely made thousands of times each day in most
large firms. The answer has been preprogrammed into the firm’s payroll and accounts
receivable systems.


Making a decision is a multistep process. Simon

) described four different stages in
decision making: intelligence, design, choice, and implementation (see
Figure 12



The decision
making process is broken down into four stages.


consists of d
iscovering, identifying, and understanding the problems occurring
in the organization

why a problem exists, where, and what effects it is having on the firm.


involves identifying and exploring various solutions to the problem.


consists of cho
osing among solution alternatives.


involves making the chosen alternative work and continuing to monitor
how well the solution is working.

What happens if the solution you have chosen doesn’t work?
Figure 12

shows that you
can return to an

earlier stage in the decision
making process and repeat it if necessary. For
instance, in the face of declining sales, a sales management team may decide to pay the
sales force a higher commission for making more sales to spur on the sales effort. If this

does not produce sales increases, managers would need to investigate whether the problem
stems from poor product design, inadequate customer support, or a host of other causes that
call for a different solution.


The premise of this book and this chapter is that systems to support decision making
produce better decision making by managers and employees, above average returns on
investment for the firm, and ultimately higher profitability. However, information s
cannot improve all the different kinds of decisions taking place in an organization. Let’s
examine the role of managers and decision making in organizations to see why this is so.

Managerial Roles

Managers play key roles in organizations. Their resp
onsibilities range from making
decisions, to writing reports, to attending meetings, to arranging birthday parties. We are
able to better understand managerial functions and roles by examining classical and
contemporary models of managerial behavior.

lassical model of management
, which describes what managers do, was largely
unquestioned for the more than 70 years since the 1920s. Henri Fayol and other early
writers first described the five classical functions of managers as planning, organizing,
inating, deciding, and controlling. This description of management activities
dominated management thought for a long time, and it is still popular today.

The classical model describes formal managerial functions but does not address what
exactly managers
do when they plan, decide things, and control the work of others. For
this, we must turn to the work of contemporary behavioral scientists who have studied
managers in daily action.
Behavioral models

state that the actual behavior of managers
appears to be

less systematic, more informal, less reflective, more reactive, and less well
organized than the classical model would have us believe.

Observers find that managerial behavior actually has five attributes that differ greatly
from the classical description
. First, managers perform a great deal of work at an
unrelenting pace

studies have found that managers engage in more than 600 different
activities each day, with no break in their pace. Second, managerial activities are
fragmented; most activities last fo
r less than nine minutes, and only 10 percent of the
activities exceed one hour in duration. Third, managers prefer current, specific, and ad
hoc information (printed information often will be too old). Fourth, they prefer oral forms
of communication to wr
itten forms because oral media provide greater flexibility, require
less effort, and bring a faster response. Fifth, managers give high priority to maintaining a
diverse and complex web of contacts that acts as an informal information system and
helps them

execute their personal agendas and short

and long
term goals.

Analyzing managers’ day
day behavior, Mintzberg found that it could be classified
into 10 managerial roles.
Managerial roles

are expectations of the activities that
managers should perform
in an organization. Mintzberg found that these managerial roles
fell into three categories: interpersonal, informational, and decisional.

Interpersonal Roles.

Managers act as figureheads for the organization when they
represent their companies to the outside world and perform symbolic duties, such as
giving out employee awards, in their
interpersonal role
. Managers act as leaders,
attempting to motivate, counse
l, and support subordinates. Managers also act as liaisons
between various organizational levels; within each of these levels, they serve as liaisons
among the members of the management team. Managers provide time and favors, which
they expect to be return

Informational Roles.

In their
informational role
, managers act as the nerve centers of
their organizations, receiving the most concrete, up
date information and redistributing
it to those who need to be aware of it. Managers are therefore informatio
n disseminators
and spokespersons for their organizations.

Decisional Roles.

Managers make decisions. In their
decisional role
, they act as
entrepreneurs by initiating new kinds of activities; they handle disturbances arising in the
organization; they allo
cate resources to staff members who need them; and they negotiate
conflicts and mediate between conflicting groups.

Table 12
, based on Mintzberg’s

role classifications, is one look at where systems can
and cannot help managers. The table shows that information systems do not yet
contribute to some important areas of management life.




Kenneth C. Laudon and Jane P. Laudon; and Mintzberg,

World Decision Making

We now see that information systems are not helpful for all managerial roles. And in
those managerial roles where information systems might improve decisions, in
in information technology do not always produce positive results. There are three main
reasons: information quality, management filters, and organizational culture (see

Information Quality.

quality decisions require high
Table 12

describes information quality dimensions that affect the quality of decisions.

If the output of information systems does not meet these quality criteria, decision
will suffer.
Chapter 6

has shown that corporate databases and
files have varying levels of
inaccuracy and incompleteness, which in turn will degrade the quality of decision

Management Filters.

Even with timely, accurate information, some managers make bad
decisions. Managers (like all human beings) absorb inf
ormation through a series of filters
to make sense of the world around them. Managers have selective attention, focus on
certain kinds of problems and solutions, and have a variety of biases that reject
information that does not conform to their prior conc

For instance, Wall Street firms such as Bear Stearns and Lehman Brothers imploded in
2008 because they underestimated the risk of their investments in complex mortgage
securities, many of which were based on subprime loans that were more likely to

The computer models they and other financial institutions used to manage risk were
based on overly optimistic assumptions and overly simplistic data about what might go
wrong. Management wanted to make sure that their firms’ capital was not all t
ied up as a
cushion against defaults from risky investments, preventing them from investing it to
generate profits. So the designers of these risk management systems were encouraged to
measure risks in a way that did not pick them all up. Some trading desk
s also
oversimplified the information maintained about the mortgage securities to make them
appear as simple bonds with higher ratings than were warranted by their underlying
components (Hansell,

Organizational Inertia and Politics.

Organizations ar
e bureaucracies with limited
capabilities and competencies for acting decisively. When environments change and
businesses need to adopt new business models to survive, strong forces within
organizations resist making decisions calling for major change. Dec
isions taken by a firm
often represent a balancing of the firm’s various interest groups rather than the best
solution to the problem.






Do the data represent reality?


Are the structure of data and relationships among the entities and attributes


Are data elements consistently defined?


Are all the necessary data present?


Do data values fall within defined ranges?


Area data available when needed?


Are the data accessible, comprehensible, and usable?

Studies of business restructuring find that firms tend to ignore poor performance until
threatened by outside takeovers, and they systematically bla
me poor performance on
external forces beyond their control such as economic conditions (the economy), foreign
competition, and rising prices, rather than blaming senior or middle management for poor
business judgment (John, Lang, Netter, et al., 1992).





There are four kinds of systems for supporting the different levels and types of decisions we
have just described. We introduced some of these systems in
Chapter 2
information systems (MIS)

provide routine reports and summaries of transaction
level data to
middle and operational level managers to provide answers to structured and semistructured
decision problems.
support systems (DSS)

provide analytical models or tools for

large quantities of data for middle managers who face semistructured decision
Executive support systems (ESS)

are systems that provide senior management,
making primarily unstructured decisions, with external information (news, stock analyses,

and industry trends) and high
level summaries of firm performance.

In this chapter, you’ll also learn about systems for supporting decision
makers working as a
Group decision
support systems (GDSS)

are specialized systems that provide a
group elect
ronic environment in which managers and teams are able to collectively make
decisions and design solutions for unstructured and semistructured problems.


MIS, which we introduced in
Chapter 2

help managers monitor and control the business by
providing information on the firm’s performance. They typically produce fixed, regularly
scheduled reports based on data extracted and summarized from the firm’s underlying
transaction processing systems (
TPS). Sometimes, MIS reports are exception reports,
highlighting only exceptional conditions, such as when the sales quotas for a specific
territory fall below an anticipated level or employees have exceeded their spending limits
in a dental care plan. Tod
ay, many of these reports are available online through an intranet,
and more MIS reports are generated on demand.
Table 12

provides some examples of
MIS applications.


Whereas MIS primarily address structured problems, DSS s
upport semistructured and
unstructured problem analysis. The earliest DSS were heavily model
driven, using some
type of model to perform “what
if” and other kinds of analyses. Their analysis capabilities
were based on a strong theory or model combined with

a good user interface that made the
system easy to use. The voyage
estimating DSS and Air Canada maintenance system
described in
Chapter 2

are examples of model
driven DSS.





California Pizza

Inventory Express application “remembers” each restaurant’s ordering patterns
and compares the amount of ingredients used per menu item to predefined portion
measurements established by management. The system identifies restaurants with

portions and notifies their managers so that corrective actions will be


Extranet MIS identifies patients with drug
use patterns that place them at risk for
adverse outcomes.

Black & Veatch

Intranet MIS tracks construction costs for various

projects across the United

Taco Bell

Total Automation of Company Operations (TACO) system provides information
on food, labor, and period
date costs for each restaurant.

The Interactive Session on Management describes another model
driven DSS.
In this
particular case, the system did not perform as well as expected because of the assumptions
driving the model and user efforts to circumvent the system. As you read this case, try to
identify the problem this company was facing, what alternative sol
utions were available to
management, and how well the chosen solution worked.

Some contemporary DSS are data
driven, using online analytical processing (OLAP), and
data mining to analyze large pools of data. The business intelligence applications described

Chapter 6

are examples of these data
driven DSS, as are the spreadsheet pivot table
applications we describe in this section.
driven DSS

support decision making by
enabling users to extract useful information that was previously buried in large qu
antities of
data. The Interactive Session on Technology provides an example.

Components of DSS

Figure 12

illustrates the components of a DSS. They include a database of data used for
query and analysis; a software system with models, data mining, and other analytical
tools; and a user interface.

DSS database

is a collection of current or historical data
from a number of
applications or groups. It may be a small database residing on a PC that contains a subset
of corporate data that has been downloaded and possibly combined with external data.
Alternatively, the DSS database may be a massive data warehouse

that is continuously
updated by major corporate TPS (including enterprise applications) and data generated by
Web site transactions). The data in DSS databases are generally extracts or copies of
production databases so that using the DSS does not interfe
re with critical operational

The DSS user interface permits easy interaction between users of the system and the DSS
software tools. Many DSS today have Web interfaces to take advantage of graphical
displays, interactivity, and ease of use.

S software system

contains the software tools that are used for data analysis. It
may contain various OLAP tools, data mining tools, or a collection of mathematical and
analytical models that are accessible to the DSS user. A

is an abstract
ation that illustrates the components or relationships of a phenomenon. A model
may be a physical model (such as a model airplane), a mathematical model (such as an
equation), or a verbal model (such as a description of a procedure for writing an order).



In a seemingly simpler and less hectic time, overbooked flights presented an
opportunity. Frequent travelers regularly and eagerly chose to give up their seats and
delay their departures by a few
hours in exchange for rewards such as a voucher for a
free ticket.

Today, fewer people are volunteering to give up their seats for a flight because there are
fewer and fewer seats to be bumped to. Airlines are struggling to stay in business and
look to sav
e costs wherever possible. They are scheduling fewer flights and those
flights are more crowded. Instead of delaying his or her trip by a few hours, a passenger
that accepts a voucher for being bumped may have to wait several days before a seat
becomes ava
ilable on another flight. And passengers are being bumped from flights
involuntarily more often.

Airlines routinely overbook flights to compensate for the millions of no
shows that cut
into expected revenue. The purpose of overbooking is not to leave passe
ngers without a
seat, but to come as close as possible to filling every seat on every flight. The revenue
lost from an empty seat is much greater than the costs of compensating a bumped
passenger. Airlines are much closer today to filling every seat on fli
ghts than at any
point in their history. The problem is, the most popular routes often sell out, so bumped
passengers may be stranded for days.

The airlines do not approach overbooking haphazardly. They employ young, sharp
minds with backgrounds in math an
d economics as analysts. The analysts use computer
modeling to predict how many passengers will fail to show up for a flight. They
recommend overbooking based on the numbers generated by the software.

The software used by US Airways, for example, analyzes
the historical record of no
shows on flights and looks at the rate at every fare level available. The lowest
fares are generally nonrefundable, and passengers at those fare levels tend to carry their
reservations through. Business travelers with the

priced fares no
show more often.
The software examines the fares people are booking on each upcoming flight and takes
other data into account, such as the rate of no
shows on flights originating from certain
geographic regions. Analysts then predict
the number of no
shows on a particular
flight, based on which fares passengers have booked, and overbook the flight

Of course, the analysts don’t always guess correctly. And their efforts may be
hampered by a number of factors. Ticket agents r
eport that faulty computer algorithms
result in miscalculations. Changes in weather can introduce unanticipated weight
restrictions. Sometimes a smaller plane is substituted for the scheduled plane. All of
these circumstances result in fewer seats being av
ailable for the same number of
passengers, which might have been set too high already.

Regardless of how much support the analysts have from airline management, gate
attendants complain because they are the ones who receive the brunt of overbooked
rs’ wrath. Attendants have been known to call in sick to avoid dealing with the
havoc caused by overbooked flights.

Some gate attendants have gone as far as creating phony reservations, sometimes in the
names of airline executives or cartoon characters, su
ch as Mickey Mouse, in an effort to
stop analysts from overbooking. This tactic may save the attendants some grief in the
short term, but their actions often come back to haunt them. The modeling software
counts the phony reservations as no
shows, which le
ads the analysts to overbook the
flight even more the next time. Thomas Trenga, vice president for revenue management
at US Airways, refers to this game of chicken as “the death spiral.” US Airways
discourages the practice of entering phony reservations.

ith fewer passengers volunteering to accept vouchers, tensions often escalate. The
number of passengers bumped involuntarily in 2006 rose 23 percent from the previous
year and has continued to rise. The encouraging statistic is that only 676,408 of the 555

million people who flew in 2006 were bumped, voluntarily or involuntarily.

W. Douglas Parker, CEO of US Airways, said that airlines have to overbook their
flights as long as they allow passengers to no
show without penalty. US Airways has a
show rate o
f between 7 and 8 percent. US Airways claimed that overbooking
contributed to at least $1 billion of its 2006 revenue of $11.56 billion. With a profit of
only $304 million, that extra revenue was critical to the survival of the business. Some
airlines, suc
h as JetBlue, have avoided the overbooking controversy by offering only
nonrefundable tickets. No
shows cannot reclaim the price of their tickets. Business
travelers often buy the most expensive seats, but also want the flexibility of refundable
tickets, s
o JetBlue is considering a change in its policy.

The airlines are supposed to hold their analysts accountable for their work, but they are
rarely subject to critical review. Some analysts make an effort to accommodate the
wishes of the airport workers by f
inding a compromise in the overbooking rate.
Unfortunately, analysts often leave their jobs for new challenges once they become
proficient at overbooking.


Dean Foust and Justin Bachman, “You Think Flying Is Bad Now...,”
Business Week
May 28, 200
8; “The Unfriendly Skies,”
USA Today
, June 4, 2008; Jeff Bailey, “Bumped Fliers and
Plan B,”
The New York Times
, May 30, 2007; and Alice LaPlante, “Travel Problems? Blame
, June 11, 2007.



Is the
decision support system being used by airlines to overbook flights
working well? Answer from the perspective of the airlines and from the
perspective of customers.


What is the impact on the airlines if they are bumping too many


What are t
he inputs, processes, and outputs of this DSS?


What people, organization, and technology factors are responsible for
excessive bumping problems?


How much of this is a “people” problem? Explain your answer.


Visit the Web sites for US Air
ways, JetBlue, and Continental. Search the sites to
answer the following questions:


What is the policy of each of these airlines for dealing with involuntary refunds
(overbookings)? (Hint: These matters are often covered in the Contract of Carriage.)


In your opinion, which airline has the best policy? What makes this policy better
than the others?


How are each of these policies intended to benefit customers? How do they
benefit the airlines?



main components of the DSS are the DSS database, the user interface, and the
DSS software system. The DSS database may be a small database residing on a PC or
a large data warehouse.



Dick’s Sporting Goods is a prominent retailer of sporting apparel and equipment based
primarily in the eastern half of the United States. The company was founded in 1948 by
Dick Stack, who was only 18 years old at the time. Stack’s b
usiness initially sold only
fishing supplies, but gradually expanded to sell general sporting goods. In the 1990s,
under the stew
ardship of Stack’s son Ed, the retailer began rapid growth in an effort to
become a national sporting goods chain. Today, Dick
’s operates over 300 stores in 34
states and earns annual revenue of just under $4 billion. It also owns Golf Galaxy, a
golf specialty retailer. The company planned to add 44 new stores in 2008 and has
maintained a strong position during difficult economic


Dick’s has flourished because it focuses on being an authentic sporting goods retailer
by offering a broad selection of high
quality, competitively priced brand
name sporting
goods equipment, apparel, and footwear that enhances its customers’
performance and
enjoyment of their sports activities. However, Dick’s has had problems managing its
inventory and making decisions about how to stock its stores. These problems stemmed
from outdated merchandise management software and threatened to curtail

Dick’s lofty
plans for the future.

The company initially used a merchandising system from STS as a basic reporting tool.
The system wasn’t well suited to the needs of the company. It was able to compile sales
figures for athletic gear and clothing, but it

wasn’t able to analyze how a specific item,
such as a Wilson Tennis n4 racquet, was performing regionally or in a particular store.
Instead, it automatically aggregated information from all stores and combined it into a
single report. Retrieving data from

the database was a long, inefficient process,
sometimes taking over an hour to complete, and wasn’t satisfactory for answering
questions requiring complex analysis.

Because there was no central repository for company information, it was also difficult
tell whether or not a particular report was accurate. There were no standard
wide sales and inventory reports. The company lacked a unified database that
all of the company’s employees could access. Employees kept their own analyses of
sales and in
ventory in their own departments and on their own machines. Sometimes
they lost their reports because they did not remember the names of their data files.
Recognizing the problems, Dick’s attempted to roll out new tools intended to update
the company’s dat
a storage and information retrieval processes. But employees resisted
the change, preferring the methods they were used to over new tools from Cognos, a
maker of business intelligence software.

Dick’s decided to perform a complete overhaul of their data storage system in 2003.
The new system featured software from MicroStrategy and a database from Oracle. The
database Dick’s selected was Oracle’s 8i database with customized capabilities to
t data and the ability to transform to meet different business requirements. It has
since been upgraded to a more advanced 10g model. The new system was able to track
the sale of apparel and equipment in each store and by region.

The new system was launche
d with a training program to promote user adoption, so
that employees didn’t persist in using the old system that they were more accustomed
to. Even with the new training system, employee adoption was slow, but the company
offered incentives to using the n
ew system and gradually phased out the old one. Only
when the old system was phased out completely did adoption of the new system
increase tenfold. Some of the failings of previous information systems were attributed
to lack of training programs to smooth
the difficulties of adopting new systems, and this
time around Dick’s ensured that the proper programs were in place.

The MicroStrategy software was a key element of Dick’s overhaul. What sets
MicroStrategy apart from competing products is its ability to w
ork with relational
databases via relational online analytical processing (ROLAP). Multidimensional
OLAP uses a multidimensional database for analysis (see
Chapter 6
), whereas ROLAP
accesses data directly from data warehouses. It dynamically consolidates d
ata for ad
hoc and decision support analyses and scales to a large number of business analysis
perspectives (dimensions) while MOLAP generally performs efficiently with 10 or
fewer dimensions. The software allows Dick’s employees to perform detailed analys
to track sales and inventory levels.

MicroStrategy allows Dick’s employees to create different types of reports. For
example, ‘canned’ reports are reports with settings frequently used by other employees.
If an employee needs a report with commonly requ
ested parameters, canned reports
save workers the time and energy required to expressly set those parameters. On the
other hand, ‘self
service’ reports have customized inputs and outputs for instances
when a unique piece of information is sought. Processes

that once took hours now take
mere minutes because of the system’s interaction with the master database, which
consists of multiple terabytes of data.

Recent results suggest that the implementation has paid off for Dick’s, as their earnings
have doubled s
ince the initiative began and their operating margin has been close to
double that of their competitors going forward. Sales in Q1 2008 were up 11 percent to
$912 million, and although the company hasn’t been immune to the difficult economic
conditions, th
e company is outperforming its competitors and has its sights set on
gaining market share during the downturn. Although the company’s stock price has not
reached levels that it was expected to in the past several years, the company’s future
outlook remains

positive, in large part due to their successful IT implementation.


MicroStrategy, “Success Story: Dick’s Sporting Goods Inc.,” 2008; Brian P. Watson,
“Business Intelligence: Will It Improve Inventory?”
, May 14, 2007; “Dick’s
Sporting Goods Form 10
K Annual Report,” March 27, 2008; “Dick’s Sporting Goods Inc., Q1 2008
Earnings Call Transcript,”
, May 22, 2008.



What problems did Dick’s face with its data tracking and reporting? How

they affect decision making and business performance?


What did the company do to remedy those problems?


Was MicroStrategy an appropriate selection for Dick’s? Why or why not?


Has improved reporting solved all of this company’s problems? Explain
our answer.


Explore the MicroStrategy Web site and then answer the following questions:


Describe the capabilities of MicroStrategy software. List the capabilities that
would be most useful for supporting decisions about stocking Dick’s sto
res. Explain
how the software would help Dick’s employees with these decisions.


Review the section on MicroStrategy’s Dynamic Enterprise Dashboards. Then
design a dashboard for a manager deciding how to stock Dick’s stores.

Statistical modeling helps es
tablish relationships, such as relating product sales to
differences in age, income, or other factors between communities. Optimization models
determine optimal resource allocation to maximize or minimize specified variables, such
as cost or time. A classi
c use of optimization models is to determine the proper mix of
products within a given market to maximize profits.

Forecasting models often are used to forecast sales. The user of this type of model might
supply a range of historical data to project future

conditions and the sales that might
result from those conditions. The decision maker could vary those future conditions
(entering, for example, a rise in raw materials costs or the entry of a new, low
competitor in the market) to determine how new
conditions might affect sales.

Sensitivity analysis

models ask “what
if” questions repeatedly to determine the impact
on outcomes of changes in one or more factors.
if analysis

working forward from
known or assumed conditions

allows the user to vary c
ertain values to test results to
better predict outcomes if changes occur in those values. What happens if we raise
product price by 5 percent or increase the advertising budget by $100,000? What happens
if we keep the price and advertising budgets the sam
e? Desktop spreadsheet software,
such as Microsoft Excel, is often used for this purpose (see
Figure 12
). Backward
sensitivity analysis software helps decision makers with goal seeking: If I want to sell one
million product units next year, how much must

I reduce the price of the product?

Using Spreadsheet Pivot Tables to Support Decision

Managers also use spreadsheets to identify and understand patterns in business
information. For instance, let’s a take a look at one day’s worth of transactions at an
online firm Online Management Training Inc. (OMT Inc.) that sells online management
ing videos and books to corporations and individuals who want to improve their
management techniques. On a single day the firm experienced 517 order transactions.
Figure 12

shows the first 25 records of transactions produced at the firm’s Web site on
t day. The names of customers and other identifiers have been removed from this list.

You might think of this list as a database composed of transaction records (the rows). The
fields (column headings) for each customer record are: customer ID, region of p
payment method, source of contact (e
mail versus Web banner ad), amount of purchase,
the product purchased (either online training or a book), and time of day (in 24

There’s a great deal of valuable information in this transaction list

that might help
managers answer important questions and make important decisions:

Where do most of our customers come from? The answer might tell managers
where to spend more marketing resources, or to initiate new marketing efforts.

Are there regiona
l differences in the sources of our customers? Perhaps in some
regions, e
mail is the most effective marketing tool, whereas in other regions, Web
banner ads are more effective. The answer to this more complicated question might
help managers develop a reg
ional marketing strategy.

Where are the average purchases higher? The answer might tell managers where
to focus marketing and sales resources, or pitch different messages to different regions.



This table displays the r
esults of a sensitivity analysis of the effect of changing the
sales price of a necktie and the cost per unit on the product’s break
even point. It
answers the question, “What happens to the break
even point if the sales price and the
cost to make each uni
t increase or decrease?”



This list shows a portion of the order transactions for Online Management Training
Inc. (OMT Inc.) on October 28, 2008.

What form of payment is the
most common? The answer might be used to
emphasize in advertising the most preferred means of payment.

Are there any times of day when purchases are most common? Do people buy
products while at work (likely during the day) or at home (likely in the eveni

Are there regional differences in the average purchase? If one region is much
more lucrative, managers could focus their marketing and advertising resources on that

Notice that these questions often involve multiple dimensions: region and av
purchase; time of day and average purchase; payment type and average purchase; and
region, source of customer, and purchase. Also, some of the dimensions are categorical,
such as payment type, region, and source. If the list were small, you might sim
ply inspect
the list and find patterns in the data. But this is impossible when you have a list of over
500 transactions.

Fortunately, the spreadsheet pivot table provides a powerful tool for answering such
questions using large data sets. A
pivot table


a table that displays two or more
dimensions of data in a convenient format. Microsoft Excel’s PivotTable capability
makes it easy to analyze lists and databases by automatically extracting, organizing, and
summarizing the data.

For instance, let’s take the first question, “Where do our customers come from?” We’ll
start with region and ask the question, “How many customers come from each region?”
To find the answer using Excel 2007, you would create a pivot table by selecting the
range of data, fields you want to analyze, and a location for the PivotTable report, as
illustrated in
Figure 12
. The PivotTable report shows most of our customers come from
the Western region.



This PivotTable report was created using Excel 2007 to quickly produce a table
showing the relationship between region and number of customers.

Does the source of the customer make a difference in addition to region? We have two
rces of customers: e
mail campaigns and online banner advertising. In a few seconds
you will find the answer shown in
Figure 12
. The pivot table shows that Web banner
advertising produces most of the customers, and this is true of all the regions.

You ca
n use pivot tables to answer all the questions we have posed about the Online
Management Training data. The complete Excel file for these examples is available on
our companion Web site. One of the Hands
on MIS Projects for this chapter asks you to
find an
swers to a number of other questions regarding this data file.


Data from information systems are made easier for users to digest and act on by using
graphics, charts, tables, maps, digital images, three
dimensional presentations, animations,
and other data visualization technologies. By presenting data in graphical form,

tools help users see patterns and relationships in large amounts of data that
would be difficult to discern if the d
ata were presented as traditional lists of text. Some data
visualization tools are interactive, enabling users to manipulate data and see the graphical
displays change in response to the changes they make.

Geographic information systems (GIS)

are a special

category of DSS that use data
visualization technology to analyze and display data for planning and decision making in
the form of digitized maps. The software assembles, stores, manipulates, and displays
geographically referenced information, tying data
to points, lines, and areas on a map. GIS
have modeling capabilities, enabling managers to change data and automatically revise
business scenarios to find better solutions.



In this pivot table, we are able to examine where customers come from in terms of
region and advertising source. It appears nearly 30 percent of the customers respond to
mail campaigns, and there are some regional variations.

GIS support de
cisions that require knowledge about the geographic distribution of people
or other resources. For example, GIS might be used to help state and local governments
calculate emergency response times to natural disasters, to help retail chains identify
able new store locations, or to help banks identify the best locations for installing new
branches or automatic teller machine (ATM) terminals.


The growth of electronic commerce has encouraged many companies to develop DSS for
customers that use Web information resources and capabilities for interactivity and
personalization to help users select products and services. People are now using more
ormation from multiple sources to make purchasing decisions (such as purchasing a car
or computer) before they interact with the product or sales staff. For instance, nearly all
automobile companies use customer decision
support systems that allow Web site

to configure their desired car.
Customer decision
support systems (CDSS)

support the
making process of an existing or potential customer.

South Carolina used a GIS
based program called HAZUS to estimate and map the
regional damage and
losses resulting from an earthquake of a given location and
intensity. HAZUS estimates the degree and geographic extent of earthquake damage
across the state based on inputs of building use, type, and construction materials. The
GIS helps the state plan fo
r natural hazards mitigation and response.

People interested in purchasing a product or service are able to use Internet search engines,
intelligent agents, online catalogs, Web directories, newsgroup discussions, e
mail, and
other tools to help them locat
e the information they need to help with their decision.
Companies have developed specific customer Web sites where all the information, models,
or other analytical tools for evaluating alternatives are concentrated in one location.

based DSS have beco
me especially popular in financial services because so many
people are trying to manage their own assets and retirement savings. For example,
, a Web site run by RiskMetrics Group, lets users input all their stock,
bond, and mutual fund holdi
ngs to determine how much their portfolios might decline
under various conditions. Users see how the addition or subtraction of a holding might
affect overall portfolio volatility and risk.


The DSS we have just describ
ed focus primarily on individual decision making. However,
so much work is accomplished in groups within firms that a special category of systems
called group decision
support systems (GDSS) has been developed to support group and
organizational decision m

A GDSS is an interactive computer
based system for facilitating the solution of
unstructured problems by a set of decision makers working together as a group in the same
location or in different locations. Groupware and Web
based tools for videoconf
and electronic meetings described earlier in this text support some group decision
processes, but their focus is primarily on communication. GDSS, however, provide tools
and technologies geared explicitly toward group decision making.

meetings take place in conference rooms with special hardware and software
tools to facilitate group decision making. The hardware includes computer and networking
equipment, overhead projectors, and display screens. Special electronic meeting software
lects, documents, ranks, edits, and stores the ideas offered in a decision
making meeting.
The more elaborate GDSS use a professional facilitator and support staff. The facilitator
selects the software tools and helps organize and run the meeting.

A sophis
ticated GDSS provides each attendee with a dedicated desktop computer under
that person’s individual control. No one will be able to see what individuals do on their
computers until those participants are ready to share information. Their input is transmit
over a network to a central server that stores information generated by the meeting and
makes it available to all on the meeting network. Data can also be projected on a large
screen in the meeting room.

GDSS make it possible to increase meeting size w
hile at the same time increasing
productivity because individuals contribute simultaneously rather than one at a time. A
GDSS promotes a collaborative atmosphere by guaranteeing contributors’ anonymity so
that attendees focus on evaluating the ideas themse
lves without fear of personally being
criticized or of having their ideas rejected based on the contributor. GDSS software tools
follow structured methods for organizing and evaluating ideas and for preserving the
results of meetings, enabling nonattendees

to locate needed information after the meeting.
GDSS effectiveness depends on the nature of the problem and the group and on how well a
meeting is planned and conducted.









The purpose of
executive support systems (ESS)
, introduced in
Chapter 2
, is to help managers
focus on the really important performance information that affect the overall profitability and
success of the firm. There are two parts to developing ESS. First,
you will need a
methodology for understanding exactly what is “the really important performance
information” for a specific firm, and second, you will need to develop systems capable of
delivering this information to the right people in a timely fashion.

urrently, the leading methodology for understanding the really important information
needed by a firm’s executives is called the
balanced scorecard method

(Kaplan and Norton,
; Kaplan and Norton,
). The balanced score card is a framework for
ionalizing a firm’s strategic plan by focusing on measurable outcomes on four
dimensions of firm performance: financial, business process, customer, and learning and
growth (
Figure 12
). Performance on each dimension is measured using
key performance
cators (KPIs)

which are the measures proposed by senior management for
understanding how well the firm is performing along any given dimension. For instance, one
key indicator of how well an online retail firm is meeting its customer performance
s is the average length of time required to deliver a package to a consumer. If your
firm is a bank, one KPI of business process performance is the length of time required to
perform a basic function like creating a new customer account.

The balanced
scorecard framework is thought to be “balanced” because it causes managers to
focus on more than just financial performance. In this view, financial performance is past

the result of past actions

and managers should focus on the things they are abl
e to
influence today, such as business process efficiency, customer satisfaction, and employee



In the balanced scorecard framework, the firm’s strategic objectives are operationalized
along four dim
ensions: financial, business process, customer, and learning and growth.
Each dimension is measured using several key performance indicators (KPIs).

Source: Authors.

Once a scorecard is developed by consultants and senior executives, the next step is
automating a flow of information to executives and other managers for each of the key
performance indicators. There are literally hundreds of consulting and software firms that
offer these capabilities, which are described below. Once these systems are imp
they are typically referred to as “executive support systems.”


Use of ESS has migrated down several organizational levels so that the executive and
subordinates are able to look at the same data i
n the same way. Today’s systems try to
avoid the problem of data overload by filtering data and presenting it in graphical or
dashboard format. ESS have the ability to
drill down
, moving from a piece of summary
data to lower and lower levels of detail. The

ability to drill down is useful not only to senior
executives but also to employees at lower levels of the firm who need to analyze data.
OLAP tools for analyzing large databases provide this capability.

A major challenge of executive support systems has
been to integrate data from systems
designed for very different purposes so that senior executives are able to review
organizational performance from a firm
wide perspective. Most ESS now rely on data
provided by the firm’s existing enterprise applications

(enterprise resource planning, supply
chain management, and customer relationship management) rather than entirely new
information flows and systems.

While the balanced scorecard framework focuses on internal measures of performance,
executives also need
a wide range of external data, from current stock market news to
competitor information, industry trends, and even projected legislative action. Through
their ESS, many managers have access to news services, financial market databases,
economic information
, and whatever other public data they may require.

Contemporary ESS include tools for modeling and analysis. With only a minimum of
experience, most managers are able to use these tools to create graphic comparisons of data
by time, region, product, price
range, and so on. (Whereas DSS use such tools primarily for
modeling and analysis in a fairly narrow range of decision situations, ESS use them
primarily to provide status information about organizational performance.)


Much of the value of ESS is found in their flexibility and their ability to analyze, compare,
and highlight trends. The easy use of graphics enables the user to look at more data in less
time with greater clarity and insight than paper
based systems

provide. Executives are
using ESS to monitor key performance indicators for the entire firm and to measure firm
performance against changes in the external environment. The timeliness and availability
of the data result in needed actions being identified
and carried out earlier than previously
possible. Problems will handled before they become too damaging, and opportunities will
be identified earlier. These systems thus help businesses move toward a “sense
respond” strategy.

designed ESS dramatic
ally improve management performance and increase upper
management’s span of control. Immediate access to so much data increases executives’
ability to monitor activities of lower units reporting to them. That very monitoring ability
enables decision making

to be decentralized and to take place at lower operating levels.
Executives are often willing to push decision making further down into the organization as
long as they are assured that all is going well. Alternatively, executive support systems
based on
wide data potentially increase management centralization, enabling
senior executives to monitor the performance of subordinates across the company and to
take appropriate action when conditions change.

To illustrate the different ways in which E
SS enhance decision making, we now describe
important types of ESS applications for gathering business intelligence and monitoring
corporate performance.

National Life: ESS for Business Intelligence

Headquartered in Toronto, Canada, National Life markets life insurance, health
insurance, and retirement/investment products to individuals and groups. The company
has more than 370 employees in Toronto and its regional offices. National Life uses an
tive information system based on Information Builders’ WebFOCUS, which allows
senior managers to access information from corporate databases through a Web interface.
The system provides statistical reporting and the ability to drill down into current sales

information, which is organized to show premium dollars by salesperson. Authorized
users are able to drill down into these data to see the product, agent, and client for each
sale. They can examine the data many different ways

by region, by product, and b
broker, accessing data for monthly, quarterly, and annual time periods (Information

Rohm and Haas and Pharmacia Corporation: Monitoring
Corporate Performance with Digital Dashboards and
Balanced Scorecard Systems

ESS are increasingly con
figured to summarize and report on key performance indicators
for senior management in the form of a digital dashboard or “executive dashboard.” The
dashboard displays on a single screen all of the critical measurements for piloting a
company, similar to t
he cockpit of an airplane or an automobile dashboard. The
dashboard presents key performance indicators as graphs and charts in a Web browser
format, providing a one
page overview of all the critical measurements necessary to
make key executive decisions.

Rohm and Haas, a chemical and specialty materials firm headquartered in Philadelphia,
has 13 different business units, each operating independently and using more than 300
disparate information systems. To obtain a company
wide overview of performance, it
implemented a series of Web
based dashboards built with SAP tools atop an enterprise
system and enterprise data warehouse.

Management defined a handful of key performance indicators to provide high
measurements of the business and had the information

delivered on dashboards. The
KPIs may be broken down into their components. For example, a gross profit KPI can be
broken down into figures for sales and cost of sales. A manager is able to drill down
further to see that the cost of sales figures are base
d on manufacturing and raw materials
costs. If raw materials costs appear to be problematic, the system allows the manager to
drill down further to identify the costs of individual raw materials.

The dashboards are customized for multiple layers of managem
ent. An Executive
Dashboard is geared toward the CEO, CFO, and other senior managers, and consists of
Business Financials, which include all of the major KPIs. The Pulse is aimed at a wider
range of users and displays only three KPIs: sales, standard gross

profit, and volume. The
Reporting and Analysis Toolkit provides a set of analysis tools that allow managers and
business analysts to drill down into specifics to answer questions such as “Why are raw
materials costs higher than expected?” The Analysis Acc
elerator focuses on standard
sales and gross
profit analyses, allowing users to drill down to the individual customer
level. The most popular dashboard is the daily sales report against Plan.

Rohm and Haas claims that the dashboards have made its managemen
t decision making
more proactive. Managers are able to quickly anticipate problems before they erupt and
take corrective action. For example, even though the cost of raw materials based on
petrochemicals has escalated in recent years, the company has been
able to maintain a
high level of profitability by working out pricing changes and modifying its sales
techniques (Maxcer,

Pharmacia Corporation, a global pharmaceutical firm based in Peapack, New Jersey, uses
Oracle’s Balanced Scorecard software and

a data warehouse to ensure the entire
organization is operating in a coordinated manner. Pharmacia spends about $2 billion
annually on research and development, and the company wanted to make more effective
use of the funds allocated for research. The bal
anced scorecard reports show, for
example, how Pharmacia’s U.S. or European clinical operations are performing in
relation to corporate objectives and other parts of the company. Pharmacia uses the
scorecard system to track the attrition rate of new compou
nds under study, to monitor the
number of patents in clinical trials, and to see how funds allocated for research are being
spent (Oracle,




The projects in this section give you hands
on experience analyzing opportunities
for DSS,
using a spreadsheet pivot table to analyze sales data, and using online retirement planning
tools for financial planning.

Management Decision Problems


Applebee’s is the largest casual dining chain in the world, with 1,970 locations
throughout t
he United States and nearly 20 other countries worldwide. The menu features
beef, chicken, and pork items, as well as burgers, pasta, and seafood. The Applebee’s
CEO wants to make the restaurant more profitable by developing menus that are tastier
and cont
ain more items that customers want and are willing to pay for despite rising costs
for gasoline and agricultural products. How might information systems help management
implement this strategy? What pieces of data would Applebee’s need to collect? What
ds of reports would be useful to help management make decisions on how to improve
menus and profitability?


During the 1990s, the Canadian Pacific Railway used a tonnage
based operating
model in which freight trains ran only when there was sufficient tra
ffic to justify the
expense. This model focused on minimizing the total number of freight trains in service
and maximizing the size of each train. However, it did not necessarily use crews,
locomotives, and equipment efficiently, and it resulted in inconsi
stent transit times and
delivery schedules. Canadian Pacific and other railroads were losing business to trucking
firms, which offered more flexible deliveries that could be scheduled at the times most
convenient for customers. How could a DSS help Canadia
n Pacific and other railroads
compete with trucking firms more effectively?

Improving Decision Making: Using Pivot Tables to Analyze
Sales Data

Software skills: Pivot tables

Business skills: Analyzing sales data

This project gives you an opportunity to
learn how to use Excel’s PivotTable functionality
to analyze a database or data list.

Use the data list for Online Management Training Inc. described earlier in the chapter. This
is a list of the sales transactions at OMT for one day. You can find this spr
eadsheet file at
the Companion Web site for this chapter.

Use Excel’s PivotTable to help you answer the following questions:

Where are the average purchases higher? The answer might tell managers where
to focus marketing and sales resources, or pitch dif
ferent messages to different regions.

What form of payment is the most common? The answer might be used to
emphasize in advertising the most preferred means of payment.

Are there any times of day when purchases are most common? Do people buy
products w
hile at work (likely during the day) or at home (likely in the evening)?

What’s the relationship between region, type of product purchased, and average
sales price?

Improving Decision Making: Using a Web
Based DSS for
Retirement Planning

Software skills:

based software

Business skills: Financial planning

This project will help develop your skills in using Web
based DSS for financial planning.

The Web sites for CNN Money and MSN Money Magazine feature Web
based DSS for
financial planning and decis
ion making. Select either site to plan for retirement. Use your
chosen site to determine how much you need to save to have enough income for your
retirement. Assume that you are 50 years old and plan to retire in 16 years. You have one
dependant and $100,0
00 in savings. Your current annual income is $85,000. Your goal is to
be able to generate an annual retirement income of $60,000, including Social Security
benefit payments.

To calculate your estimated Social Security benefit, use the Quick Calculator at

the Social Security Administration Web site.

Use the Web site you have selected to determine how much money you need to
save to help you achieve your retirement goal.

Critique the site

its ease of use, its clarity, the value of any conclusions reached
and the extent to which the site helps investors understand their financial needs and the
financial markets.




The following Learning Track provides content relevant to topics covered in this chapter:


Building and Using Pivot

Review Summary


What are the different types of decisions and how does the decision
process work?

The different levels in an organization (strategic, management, operational) have different
making requirements. Decisions can be str
uctured, semistructured, or
unstructured, with structured decisions clustering at the operational level of the
organization and unstructured decisions at the strategic level. Decision making can be
performed by individuals or groups and includes employees
as well as operational, middle,
and senior managers. There are four stages in decision making: intelligence, design, choice,
and implementation. Systems to support decision making do not always produce better
manager and employee decisions that improve fir
m performance because of problems with
information quality, management filters, and organizational inertia.


How do information systems support the activities of managers and
management decision making?

Early classical models of managerial activities str
ess the functions of planning, organizing,
coordinating, deciding, and controlling. Contemporary research looking at the actual
behavior of managers has found that managers’ real activities are highly fragmented,
variegated, and brief in duration and that
managers shy away from making grand, sweeping
policy decisions.

Information technology provides new tools for managers to carry out both their traditional
and newer roles, enabling them to monitor, plan, and forecast with more precision and
speed than ever

before and to respond more rapidly to the changing business environment.
Information systems have been most helpful to managers by providing support for their
roles in disseminating information, providing liaisons between organizational levels, and
ting resources. However, information systems are less successful at supporting
unstructured decisions. Where information systems are useful, information quality,
management filters, and organizational culture can degrade decision


How do decision
support systems (DSS) differ from MIS and how do they
provide value to the business?

Management information systems (MIS) provide information on firm performance to help
managers monitor and control the business, often in the form of fixed regularly sched
reports based on data summarized from the firm’s transaction processing systems. MIS
support structured decisions and some semistructured decisions.

DSS combine data, sophisticated analytical models and tools, and user
friendly software
into a single
powerful system that can support semistructured or unstructured decision
making. The components of a DSS are the DSS database, the user interface, and the DSS
software system. There are two kinds of DSS: model
driven DSS and data
driven DSS.
DSS can help s
upport decisions for pricing, supply chain management, and customer
relationship management as well model alternative business scenarios. DSS targeted
toward customers as well as managers are becoming available on the Web. A special
category of DSS called
geographic information systems (GIS) uses data visualization
technology to analyze and display data for planning and decision making with digitized


How do executive support systems (ESS) help senior managers make better

ESS help senior
managers with unstructured problems that occur at the strategic level of
the firm, providing data from both internal and external sources. ESS help senior
executives monitor firm performance, spot problems, identify opportunities, and forecast
trends. Thes
e systems can filter out extraneous details for high
level overviews, or they can
drill down to provide senior managers with detailed transaction data if required. The
balanced scorecard is the leading methodology for understanding the most important
mation needed by a firm’s executives.

ESS help senior managers analyze, compare, and highlight trends so that the managers may
more easily monitor organizational performance or identify strategic problems and
opportunities. They are very useful for environ
mental scanning, providing business
intelligence to help management detect strategic threats or opportunities from the
organization’s environment. ESS can increase the span of control of senior management,
allowing them to oversee more people with fewer re


What is the role of information systems in helping people working in a group
make decisions more efficiently?

Group decision
support systems (GDSS) help people working together in a group arrive at
decisions more efficiently. GDSS feature speci
al conference room facilities where
participants contribute their ideas using networked computers and software tools for
organizing ideas, gathering information, making and setting priorities, and documenting
meeting sessions.

Key Terms

Balanced scorecard

Behavioral models,


Classical model of management,

Customer decision
support systems (CDSS),

driven DSS,

Data visualization,

Decisional role,


Drill down,

DSS database,

DSS software system,

Geographic information systems (GIS),

Group decision
support systems (GDSS),


Informational role,


Interpersonal role,

Key performance indicators KPIs),

Managerial roles,


Pivot table,

Sensitivity analysis,

Semistructured decisions,

Structured decisions,

Unstructured decisions,

Review Questions


What are the different types of decisions and how does the decision
process work?

and describe the different levels of decision making and decision
constituencies in organizations. Explain how their decision
making requirements

Distinguish between an unstructured, semistructured, and structured decision.

List and desc
ribe the stages in decision making.


How do information systems support the activities of managers and management
decision making?

Compare the descriptions of managerial behavior in the classical and behavioral

Identify the specific manageria
l roles that can be supported by information


How do decision
support systems (DSS) differ from management information
systems (MIS) and how do they provide value to the business?

Distinguish between DSS and MIS.

Compare a data
driven DSS to

a model
driven DSS. Give examples.

Identify and describe the three basic components of a DSS.

Define a geographic information system (GIS) and explain how it supports
decision making.

Define a customer decision
support system and explain how the Int
ernet can be
used for this purpose.


How do executive support systems (ESS) help senior managers make better

Define and describe the capabilities of an ESS.

Describe how the balanced scorecard helps managers identify important

Explain how ESS enhance managerial decision making and provide value for a


What is the role of information systems in helping people working in a group
make decisions more efficiently?

Define a group decision
support system (
GDSS) and explain how it differs from a

Explain how a GDSS works and how it provides value for a business.

Discussion Questions


As a manager or user of information systems, what would you need to know to
participate in the design and use of a DSS

or an ESS? Why?


If businesses used DSS, GDSS, and ESS more widely, would managers and
employees make better decisions? Why or why not?

Video Cases

You will find video cases illustrating some of the concepts in this chapter on the Laudon

Web site along with questions to help you analyze the cases.

Collaboration and Teamwork: Designing a University

With three or four of your classmates, identify several groups in your university that might
benefit from a GDSS. Design a GDSS for one of

those groups, describing its hardware,
software, and people elements. If possible, use Google Sites to post links to Web pages,
team communication announcements, and work assignments; to brainstorm; and to work
collaboratively on project documents. Try to

use Google Docs to develop a presentation of
your findings for the class.

HSBC’s Lending Decisions and the Subprime Mortgage
Crisis: What Went Wrong? CASE STUDY

The week ending September 19, 2008, was the darkest on Wall Street since the October
1929 stoc
k market crash. Giant investment banks Lehman Brothers and Merrill Lynch

which had survived the Great Depression, the crash of 1987, and the trauma of 9/11

by the wayside. Worldwide financial markets were in danger of collapsing. It was the
greatest f
inancial crisis since the Great Depression.

At the heart of this financial crisis were soured mortgages. A major player in this crisis
was HSBC Holdings PLC, the third largest bank in the world based on market value.
With headquarters in London, HSBC opera
tes in 76 countries and territories. By 2006, it
had become one of the largest lenders of subprime mortgages in the United States.

Subprime mortgages are targeted toward low
end borrowers who represent a risk of
default, but, at times, a good business oppo
rtunity to the lender. Subprime customers
often have blemished credit histories, low incomes, or other traits that suggest a greater
likelihood of defaulting on a loan. Generally speaking, lenders try to avoid making such
loans. However, during a housing b
oom, competition for customers motivates lenders to
relax their lending standards. During such a time, subprime mortgages, including those
that do not require a down payment and have very low introductory rates, become far
more prevalent, as they did betwe
en 2001 and 2006 in the United States.

By 2007, 12 percent of the total $8.4 trillion U.S. mortgage market consisted of subprime
mortgages, up from just 7.5 percent near the end of 2001. In early February 2007, HSBC
revealed that this risky lending techniq
ue had become a major problem.

As the U.S. real estate market slowed in 2006, the growth rate of home values also
slowed. With the coinciding rise in interest rates, many borrowers with adjustable
mortgages (ARMs) were unable to make their mortgage pa
yments and defaulted on their
loans. HSBC anticipated seeing the number of delinquent and defaulted accounts grow,
but not to the level it actually discovered.

Mortgage lenders in the United States participate in a complicated business that involves
more t
han a simple lender
borrower relationship. A bank or mortgage broker that
originates a mortgage might not keep it. Mortgage wholesalers often buy loans and then
turn right around and resell them to large financial institutions. The default risk passes
g to whomever winds up with the account last. HSBC participated in several zones of
the mortgage market. One unit of HSBC Mortgage Services originated mortgages, often
of the subprime variety. HSBC flipped some of these loans to other companies, but kept
thers as investments. The ones HSBC kept provided revenue from the interest they