Chapter 12 Enhancing Decision Making
LEARNING OBJECTIVES
After reading this chapter, you will be able to answer the following questions:
1.
What are the different types of decisions and how does the decision-making
process work?
2.
How do information systems support the activities of managers and management
decision making?
3.
How do decision-support systems (DSS) differ from MIS and how do they
provide value to the business?
4.
How do executive support systems (ESS) help senior managers make better
decisions?
5.
What is the role of information systems in helping people working in a group
make decisions more efficiently?
CHAPTER OUTLINE
12.1
DECISION MAKING AND INFORMATION SYSTEMS
Business Value of Improved Decision Making
Types of Decisions
The Decision-Making Process
Managers and Decision Making in the Real World
12.2
SYSTEMS FOR DECISION SUPPORT
Management Information Systems (MIS)
Decision-Support Systems (DSS)
Data Visualization and Geographic Information Systems
Web-Based Customer Decision-Support Systems
Group Decision-Support Systems (GDSS)
12.3
EXECUTIVE SUPPORT SYSTEMS (ESS) AND THE BALANCED
SCORECARD FRAMEWORK
The Role of Executive Support Systems in the Firm
Business Value of Executive Support Systems
12.4
HANDS-ON MIS PROJECTS
Management Decision Problems
Improving Decision Making: Using Pivot Tables to Analyze Sales Data
Improving Decision Making: Using a Web-Based DSS for Retirement Planning
LEARNING TRACK MODULE
Building and Using Pivot Tables
Interactive Sessions:
Too Many Bumped Fliers: Why?
Business Intelligence Turns Dick’s Sporting Goods into a Winner
EASTERN MOUNTAIN SPORTS FORGES A TRAIL TO BETTER
DECISIONS
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 company’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 merchandising systems, financial systems, and point-of-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 userfriendly view. It is Web-based and easy to implement, taking EMS only 90 days to be up and
running.
IWay extracts point-of-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 sales,
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-by-hour Web sales, visitors, 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 customfit 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 detailed 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 difficult 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 outdated 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, consolidate, 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 populates a data mart with data from point-of-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 access 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.
12.1
DECISION MAKING AND INFORMATION SYSTEMS
Decision making in businesses used to be limited to management. Today, lower-level
employees are responsible for some of these decisions, 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.
BUSINESS VALUE OF IMPROVED DECISION MAKING
What does it mean to the business to make better decisions? What is the monetary value of
improved decision making? Table 12-1 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 savings or increased revenue) from
improved decision making in selected areas of the business.
We can see from Table 12-1 that decisions are made at all levels of the firm and that some
of these decisions are common, routine, and numerous. Although the value 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.
TYPES OF DECISIONS
Chapters 1 and 2 showed that there are different levels in an organization. Each of these
levels has different information requirements for decision support and responsibility for
different types of decisions (see Figure 12-1). Decisions are classified as structured,
semistructured, and unstructured.
TABLE 12-1 BUSINESS VALUE OF ENHANCED
DECISION MAKING
EXAMPLE DECISION
DECISION MAKER
NUMBER OF ANNUAL DECISIONS
ESTIMATED VALUE TO FIRM OF A SINGLE IMPROVED DECISION
ANNUAL VAUE
Allocate support to most valuable customers
Accounts manager
12
$ 100,000
$1,200,000
Predict call center daily demand
Call center management
4
150,000
600,000
Decide parts inventory levels daily
Inventory manager
365
5,000
1,825,000
Identify competitive bids from major suppliers
Senior management
1
2,000,000
2,000,000
Schedule production to fill orders
Manufacturing manager
150
10,000
1,500,000
Allocate labor to complete a job
Production floor manager
100
4,000
400,000
FIGURE 12-1 INFORMATION REQUIREMENTS OF KEY
DECISION-MAKING GROUPS IN A FIRM
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, important,
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 each time as if they were
new. Many decisions have elements of both types of decisions and are semistructured,
where only part of the problem has a clear-cut answer provided by an accepted procedure.
In general, structured decisions are more prevalent at lower 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. Answering 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 judgment 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 report 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 Minneapolis 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-and-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 than 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 consulting 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.
THE DECISION-MAKING PROCESS
Making a decision is a multistep process. Simon (1960) described four different stages in
decision making: intelligence, design, choice, and implementation (see Figure 12-2).
FIGURE 12-2 STAGES IN DECISION MAKING
The decision-making process is broken down into four stages.
Intelligence consists of discovering, identifying, and understanding the problems occurring
in the organization—why a problem exists, where, and what effects it is having on the firm.
Design involves identifying and exploring various solutions to the problem.
Choice consists of choosing among solution alternatives.
Implementation 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 12-2 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.
MANAGERS AND DECISION MAKING IN THE REAL
WORLD
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 systems
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 responsibilities 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.
The classical 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,
coordinating, 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 for 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 written 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-to-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, counsel, 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 returned.
Informational Roles. In their informational role, managers act as the nerve centers of
their organizations, receiving the most concrete, up-to-date information and redistributing
it to those who need to be aware of it. Managers are therefore information 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 allocate resources to staff members who need them; and they negotiate
conflicts and mediate between conflicting groups.
Table 12-2, 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.
TABLE 12-2 MANAGERIAL ROLES AND SUPPORTING
INFORMATION SYSTEMS
Sources: Kenneth C. Laudon and Jane P. Laudon; and Mintzberg, 1971.
Real-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, investments
in information technology do not always produce positive results. There are three main
reasons: information quality, management filters, and organizational culture (see Chapter
3).
Information Quality. High-quality decisions require high-quality information. Table 123 describes information quality dimensions that affect the quality of decisions.
If the output of information systems does not meet these quality criteria, decision-making
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
making.
Management Filters. Even with timely, accurate information, some managers make bad
decisions. Managers (like all human beings) absorb information 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 conceptions.
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 default.
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 tied 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 desks 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, 2008).
Organizational Inertia and Politics. Organizations are 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. Decisions taken by a firm
often represent a balancing of the firm’s various interest groups rather than the best
solution to the problem.
TABLE 12-3 INFORMATION QUALITY DIMENSIONS
QUALITY DIMENSION
DESCRIPTION
Accuracy
Do the data represent reality?
Integrity
Are the structure of data and relationships among the entities and attributes
consistent?
Consistency
Are data elements consistently defined?
Completeness
Are all the necessary data present?
Validity
Do data values fall within defined ranges?
Timeliness
Area data available when needed?
Accessibility
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 blame 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).
12.2
SYSTEMS FOR DECISION SUPPORT
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. Management
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. Decision-support systems (DSS) provide analytical models or tools for
analyzing large quantities of data for middle managers who face semistructured decision
situations. 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. Group decision-support systems (GDSS) are specialized systems that provide a
group electronic environment in which managers and teams are able to collectively make
decisions and design solutions for unstructured and semistructured problems.
MANAGEMENT INFORMATION SYSTEMS (MIS)
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. Today, many of these reports are available online through an intranet,
and more MIS reports are generated on demand. Table 12-4 provides some examples of
MIS applications.
DECISION-SUPPORT SYSTEMS (DSS)
Whereas MIS primarily address structured problems, DSS support 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.
TABLE 12-4
EXAMPLES OF MIS APPLICATIONS
COMPANY
MIS APPLICATION
California Pizza Kitchen
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
out-of-line portions and notifies their managers so that corrective actions will be
taken.
PharMark
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
States.
Taco Bell
Total Automation of Company Operations (TACO) system provides information
on food, labor, and period-to-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 solutions 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
in Chapter 6 are examples of these data-driven DSS, as are the spreadsheet pivot table
applications we describe in this section. Data-driven DSS support decision making by
enabling users to extract useful information that was previously buried in large quantities of
data. The Interactive Session on Technology provides an example.
Components of DSS
Figure 12-3 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.
The 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 interfere with critical operational
systems.
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.
The DSS 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 model is an abstract
representation 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).
INTERACTIVE SESSION: MANAGEMENT TOO MANY
BUMPED FLIERS: WHY?
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 save 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 available 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 passengers 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 flights 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 and 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 noshows on flights and looks at the rate at every fare level available. The lowest-priced
fares are generally nonrefundable, and passengers at those fare levels tend to carry their
reservations through. Business travelers with the high-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
accordingly.
Of course, the analysts don’t always guess correctly. And their efforts may be
hampered by a number of factors. Ticket agents report 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 available 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
passengers’ 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, such 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 leads 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.
With 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
no-show rate of 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, such 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, so 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 finding a compromise in the overbooking rate.
Unfortunately, analysts often leave their jobs for new challenges once they become
proficient at overbooking.
Sources: Dean Foust and Justin Bachman, “You Think Flying Is Bad Now...,” Business Week,
May 28, 2008; “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
Technology,” InformationWeek.com, June 11, 2007.
CASE STUDY QUESTIONS
1.
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.
2.
What is the impact on the airlines if they are bumping too many
passengers?
3.
What are the inputs, processes, and outputs of this DSS?
4.
What people, organization, and technology factors are responsible for
excessive bumping problems?
5.
How much of this is a “people” problem? Explain your answer.
MIS IN ACTION
Visit the Web sites for US Airways, JetBlue, and Continental. Search the sites to
answer the following questions:
1. 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.)
2. In your opinion, which airline has the best policy? What makes this policy better
than the others?
3. How are each of these policies intended to benefit customers? How do they
benefit the airlines?
FIGURE 12-3 OVERVIEW OF A DECISION-SUPPORT
SYSTEM
The 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.
INTERACTIVE SESSION: TECHNOLOGY BUSINESS
INTELLIGENCE TURNS DICK’S SPORTING GOODS INTO
A WINNER
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 business 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 conditions.
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
to tell whether or not a particular report was accurate. There were no standard
company-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 inventory 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 data 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
extract 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 launched 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 new 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 work 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 data 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 analyses
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 requested 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 since 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, the 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.
Sources: MicroStrategy, “Success Story: Dick’s Sporting Goods Inc.,” 2008; Brian P. Watson,
“Business Intelligence: Will It Improve Inventory?” www.baselinemag.com, 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,” www.seekingalpha.com, May 22, 2008.
CASE STUDY QUESTIONS
1.
What problems did Dick’s face with its data tracking and reporting? How
did they affect decision making and business performance?
2.
What did the company do to remedy those problems?
3.
Was MicroStrategy an appropriate selection for Dick’s? Why or why not?
4.
Has improved reporting solved all of this company’s problems? Explain
your answer.
MIS IN ACTION
Explore the MicroStrategy Web site and then answer the following questions:
1. Describe the capabilities of MicroStrategy software. List the capabilities that
would be most useful for supporting decisions about stocking Dick’s stores. Explain
how the software would help Dick’s employees with these decisions.
2. 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 establish 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 classic 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-priced
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. What-if analysis—working forward from
known or assumed conditions—allows the user to vary certain 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 same? Desktop spreadsheet software,
such as Microsoft Excel, is often used for this purpose (see Figure 12-4). Backwardsensitivity 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
Making
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
training 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 12-5 shows the first 25 records of transactions produced at the firm’s Web site on
that 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 purchase,
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-hour
time).
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 regional 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 regional 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.
FIGURE 12-4 SENSITIVITY ANALYSIS
This table displays the results 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 unit increase or decrease?”
FIGURE 12-5 SAMPLE LIST OF TRANSACTIONS FOR
ONLINE MANAGEMENT TRAINING INC.
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 evening)?
•
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
region.
Notice that these questions often involve multiple dimensions: region and average
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 simply 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 is 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 12-6. The PivotTable report shows most of our customers come from
the Western region.
FIGURE 12-6 A PIVOT TABLE THAT DETERMINES THE
REGIONAL DISTRIBUTION OF CUSTOMERS
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
sources of customers: e-mail campaigns and online banner advertising. In a few seconds
you will find the answer shown in Figure 12-7. The pivot table shows that Web banner
advertising produces most of the customers, and this is true of all the regions.
You can 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 answers to a number of other questions regarding this data file.
DATA VISUALIZATION AND GEOGRAPHIC INFORMATION
SYSTEMS
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, data
visualization tools help users see patterns and relationships in large amounts of data that
would be difficult to discern if the data 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.
FIGURE 12-7 A PIVOT TABLE THAT EXAMINES
CUSTOMER REGIONAL DISTRIBUTION AND
ADVERTISING SOURCE
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
e-mail campaigns, and there are some regional variations.
GIS support decisions 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
profitable new store locations, or to help banks identify the best locations for installing new
branches or automatic teller machine (ATM) terminals.
WEB-BASED CUSTOMER DECISION-SUPPORT SYSTEMS
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
information 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 visitors
to configure their desired car. Customer decision-support systems (CDSS) support the
decision-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 for 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 locate 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.
Web-based DSS have become especially popular in financial services because so many
people are trying to manage their own assets and retirement savings. For example,
RiskGrades.com, a Web site run by RiskMetrics Group, lets users input all their stock,
bond, and mutual fund holdings 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.
GROUP DECISION-SUPPORT SYSTEMS (GDSS)
The DSS we have just described 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 making.
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 videoconferencing
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.
GDSS-guided 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
collects, 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 sophisticated 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 transmitted
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 while 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 themselves 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.
12.3 EXECUTIVE SUPPORT SYSTEMS (ESS) AND THE BALANCED
SCORECARD FRAMEWORK
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.
Currently, the leading methodology for understanding the really important information
needed by a firm’s executives is called the balanced scorecard method (Kaplan and Norton,
2004; Kaplan and Norton, 1992). The balanced score card is a framework for
operationalizing 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 12-8). Performance on each dimension is measured using key performance
indicators (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
objectives 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
history—the result of past actions—and managers should focus on the things they are able to
influence today, such as business process efficiency, customer satisfaction, and employee
training.
FIGURE 12-8
THE BALANCED SCORECARD FRAMEWORK
In the balanced scorecard framework, the firm’s strategic objectives are operationalized
along four dimensions: 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 implemented,
they are typically referred to as “executive support systems.”
THE ROLE OF EXECUTIVE SUPPORT SYSTEMS IN THE FIRM
Use of ESS has migrated down several organizational levels so that the executive and
subordinates are able to look at the same data in 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.)
BUSINESS VALUE OF EXECUTIVE SUPPORT SYSTEMS
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-andrespond” strategy.
Well-designed ESS dramatically 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 enterprise-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 ESS 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
executive 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 by
broker, accessing data for monthly, quarterly, and annual time periods (Information
Builders, 2005).
Rohm and Haas and Pharmacia Corporation: Monitoring
Corporate Performance with Digital Dashboards and
Balanced Scorecard Systems
ESS are increasingly configured 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 the 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-level
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 based 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 management. 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 Accelerator 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 management 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, 2007).
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 balanced 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 compounds under study, to monitor the
number of patents in clinical trials, and to see how funds allocated for research are being
spent (Oracle, 2003).
12.4
HANDS-ON MIS PROJECTS
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
1.
Applebee’s is the largest casual dining chain in the world, with 1,970 locations
throughout the 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 contain 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
kinds of reports would be useful to help management make decisions on how to improve
menus and profitability?
2.
During the 1990s, the Canadian Pacific Railway used a tonnage-based operating
model in which freight trains ran only when there was sufficient traffic 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 inconsistent 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 Canadian 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 spreadsheet 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 different 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 while 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: Internet-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 decision 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 $100,000 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.
LEARNING TRACK MODULE
The following Learning Track provides content relevant to topics covered in this chapter:
1.
Building and Using Pivot Tables
Review Summary
1.
What are the different types of decisions and how does the decision-making
process work?
The different levels in an organization (strategic, management, operational) have different
decision-making requirements. Decisions can be structured, 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 firm performance because of problems with
information quality, management filters, and organizational inertia.
2.
How do information systems support the activities of managers and
management decision making?
Early classical models of managerial activities stress 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
allocating 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-making.
3.
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 scheduled
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 support 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
maps.
4.
How do executive support systems (ESS) help senior managers make better
decisions?
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. These 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
information 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 environmental 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 resources.
5.
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 special 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 method, 468
Behavioral models, 454
Choice, 454
Classical model of management, 465
Customer decision-support systems (CDSS), 466
Data-driven DSS, 458
Data visualization, 465
Decisional role, 455
Design, 454
Drill down, 469
DSS database, 458
DSS software system, 458
Geographic information systems (GIS), 465
Group decision-support systems (GDSS), 457
Implementation, 454
Informational role, 455
Intelligence, 454
Interpersonal role, 455
Key performance indicators KPIs), 468
Managerial roles, 455
Model, 458
Pivot table, 464
Sensitivity analysis, 462
Semistructured decisions, 452
Structured decisions, 452
Unstructured decisions, 452
Review Questions
1. What are the different types of decisions and how does the decision-making
process work?
• List and describe the different levels of decision making and decision-making
constituencies in organizations. Explain how their decision-making requirements
differ.
• Distinguish between an unstructured, semistructured, and structured decision.
• List and describe the stages in decision making.
2. How do information systems support the activities of managers and management
decision making?
• Compare the descriptions of managerial behavior in the classical and behavioral
models.
• Identify the specific managerial roles that can be supported by information
systems.
3. 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 Internet can be
used for this purpose.
4. How do executive support systems (ESS) help senior managers make better
decisions?
• Define and describe the capabilities of an ESS.
• Describe how the balanced scorecard helps managers identify important
information requirements.
• Explain how ESS enhance managerial decision making and provide value for a
business.
5. 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
DSS.
• Explain how a GDSS works and how it provides value for a business.
Discussion Questions
1. 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?
2. 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
GDSS
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 stock 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—fell
by the wayside. Worldwide financial markets were in danger of collapsing. It was the
greatest financial 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 operates 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 opportunity 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 boom, 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 between 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 technique 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-rate
mortgages (ARMs) were unable to make their mortgage payments 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 than 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
along 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
others as investments. The ones HSBC kept provided revenue from the interest they
generated, assuming the borrowers kept current with their payments. If the borrowers fell
behind or defaulted, HSBC suffered the losses.
In its quest for higher revenue, HSBC began buying up subprime loans from other
sources. In 2005 and 2006, with the housing boom in its final stages, HSBC bought
billions of dollars of subprime loans from as many as 250 wholesale mortgage
companies, which had acquired the loans from independent brokers and banks. HSBC
found the high interest rates of these loans to be very alluring. Many of these loans were
second-lien, or piggyback loans, which allow home owners who are unable to come up
with a downpayment for a house to qualify for a mortgage by borrowing the
downpayment amount, so they actually borrow the entire purchase price of a home.
HSBC stated it had a process for forecasting how many of the loans it purchased from
wholesalers were likely to default. First, the bank would tell the wholesaler what types of
loans it was interested in, based on the income and credit scores of the borrowers. Once
the wholesaler offered a pool of mortgages, HSBC analysts evaluated the lot to determine
whether it met HSBC standards.
Perhaps due to the intense competition for mortgages, HSBC accepted pools that
included stated-income loans. These are loans for which the borrower simply states his or
her income without providing any documentation to verify it. According to Martin Eakes,
CEO of the Center for Responsible Lending, 90 percent of stated-income loan applicants
declare their incomes to be higher than they are in IRS records; 60 percent of these
people inflate their incomes by 50 percent or more. Many also exaggerate their
employment positions to coincide with the inflated income. As a result, they receive
approval for loans that are much larger than they can actually afford.
Between September 2005 and March 2006, HSBC bought nearly $4 billion in second-lien
loans. The surge increased the bank’s second-lien loans to a total of $10.24 billion.
Earlier in 2005, Bobby Mehta, the top HSBC executive in the United States, described
the development of the bank’s mortgage portfolio as disciplined. He reported to investors,
“We’ve done them conservatively based on analytics and based on our ability to earn a
good return for the risks that we undertake.”
On February 7, 2007, HSBC shook up Wall Street when it announced a much higher
percentage of its subprime loans defaulted than it had anticipated. It would have to make
provisions for $10.6 billion in bad debt stemming from loan delinquencies in 2006. In the
third quarter of 2006, the percentage of all HSBC Mortgage Services loans that were
overdue by 60 days or more jumped from 2.95 to 3.74. The bank announced that a similar
increase was expected for the fourth quarter. In short, the subprime mortgage market was
in distress, and profits from the high-risk loans were disappearing.
HSBC had begun lending to American consumers in 2003, when it purchased Household
International Inc., a major subprime lender based in Prospect Heights, Illinois.
Household’s CEO William Aldinger touted his company’s ability to assess credit risk
using modeling techniques designed by 150 Ph.Ds. The system, called the Worldwide
Household International Revolving Lending System, or Whirl, helped Household
underwrite credit card debt and support collection services in the United States, Mexico,
the United Kingdom, and the Middle East.
Lenders such as HSBC who are analyzing applicants for credit cards, car loans, and
fixed-rate mortgages use a credit rating from Fair Isaac Corp. of Minneapolis called a
FICO score. However, FICO scores had not yet been proven reliable tools for predicting
the performance, during a weakening housing market, of second-lien loans or of ARMs
taken out by subprime borrowers. Data on subprime borrowers who made small or no
down payments were scarce, and the FICO scores didn’t adequately distinguish between
loans where borrowers had put their own money down and loans with no downpayment.
Nor did the models take into account what would happen if housing prices fell to the
point where the amount owed on some mortgages exceeded the value of the homes they
covered. Nevertheless, HSBC used FICO scores to screen subprime applicants for both
second-lien loans and ARMs.
In response to its subprime loan crisis, HSBC made changes in both personnel and policy.
The company ceased originating and purchasing stated-income loans and boosted the
required FICO score for some loans. Tom Detelich, who had led the transition from
Household to HSBC’s consumer lending business, was appointed head of HSBC
Mortgage Services.
HSBC doubled the number of customer representatives who call on borrowers who have
missed payments and discuss payment plans that are more manageable. Those operations
now run seven days a week. HSBC is also utilizing information technology to pinpoint
ahead of time which customers are most in danger of failing to meet their monthly
payments once their ARMs jump from their initial teaser interest rates to higher rates. In
some cases, the adjustment can increase a monthly payment by $500. With so many
mortgages originated in 2005 and 2006, HSBC could be facing another onslaught of
delinquencies and defaults over the next two years—the Center for Responsible Lending
predicted that 20 percent of subprime mortgages sold during those two years would result
in foreclosure.
HSBC adopted business analytics software from Experian-Scorex to help support the
decision making of its credit application processing staff. The software provides users
with the ability to consistently deploy scoring models and portfolio segmentation. It also
includes tools for managing customer relationships and improving risk management
decisions. By using these tools, HSBC should be able to create strategies for individual
applicants, assess the value of each applicant, and then customize a loan offer that suits
the customer’s needs as well as the bank’s business.
Sources: Phil Mintz, “Seven Days That Shook Wall Street,” Business Week, September 19, 2008;
“Subprime Crisis: A Timeline,” CNN Money.com September 15, 2008; Roger Lowenstein, “Triple-A
Failure,” The New York Times Magazine, April 27, 2008; Robert J. Shiller, “How a Bubble Stayed Under
the Radar,” The New York Times, March 2, 2008; Carrick Mollenkamp, “In Home-Lending Push, Banks
Misjudged Risk,” The Wall Street Journal, February 8, 2007; Edward Chancellor and Mike Verdin,
“Subprime Lenders’ Miscue,” The Wall Street Journal, February 9, 2007; “Rising Subprime Defaults Hit
Local HSBC Unit,” and “HSBC Implements ExperianScorex Decision Support Software,”
www.finextra.com, February 23, 2007.
CASE STUDY QUESTIONS
1.
What problem did HSBC face in this case? What management,
technology, and organization factors were responsible for the problem? Did HSBC
management correctly identify the problem?
2.
HSBC had sophisticated information systems and analytical tools for
predicting the risk presented by subprime mortgage applicants. Why did HSBC still
run into trouble? If HSBC had a solution to the problem all along, why was the right
solution not used?
3.
What solutions is HSBC relying on to deal with its problem going
forward? Will these solutions be sufficient to turn the subprime mortgage business
around? Are there additional factors for which HSBC has not accounted? What are
they?
4.
What are the possible consequences of HSBC changing its approach to
subprime lending? How might these changes affect the business? How might they
affect the customer? How might they affect the U.S. economy?
5.
HSBC made a decision to pursue subprime as a segment of its business.
Explain how this was a structured, unstructured, or semistructured decision. Then,
present your opinion about where in the decision-making process HSBC went
wrong. Finally, apply the decision quality concepts of accuracy and
comprehensiveness to this case.
(Laudon 448)
Laudon, Laudon &. Management Information Systems: Managing the Digital Firm, VitalSource
eBook for EDMC, 11th Edition. Pearson Learning Solutions. <vbk:0558503799#outline(18)>.
Kapoor, B. (2010). Business intelligence and its use for
human resource management. The Journal of Human
Resource and Adult Learning, 6(2), 21–30.
INTRODUCTION
Business Intelligence (BI) refers to the ability to use information to gain a competitive edge over
competitors. It is rated as the most wanted technology by businesses across the world. Even in
current times of economic downturn, when IT budgets are being cut, BI is still among the top of
executive's priorities (Gartner, 2009).
Business Intelligence is a broad field that combines people skills, technologies, applications, and
business processes to make better strategic and tactical business decisions. The technologies and
applications include data management methods for planning, collecting, storing, and structuring data
into data warehouses and data marts as well as analytical tasks for querying, reporting, visualizing,
generating online active reports, and running advanced analytical techniques for clustering,
classification, segmentation, and prediction. Data warehouse focuses on enterprise wide data, and
data mart is restricted to a single process or a department, such as Human Resources (HR)
department.
Modern businesses face several challenges, and the challenges almost always bring some
opportunities along side. The goal of BI is to help businesses face many of the challenges and seize
opportunities that exist in the market place. Due to advances in technologies (e.g., improved data
storage capabilities, wide use of the Internet and Intranets) and regulatory changes (e.g., the
Sarbanes Oxley act of 2002, which mandates advance and accurate reporting capabilities in corporate
setting), businesses are collecting and storing data at an alarming rate. Companies collect large
volumes of data on their employees, such as salary information, performance reviews, and education
level. As a result, most organizations face an information overload. By 2020, the amount of data
generated each year is projected to reach 35 zettabytes (1 zettabyte = 1 billion terabytes, 1 terabyte
= 1000 gigabytes) (International data Corporation, May 2010). And yet this same data, through
proper management and analysis, will afford companies the opportunity to optimize HR and other
business operations. Organizations are competing on Business Intelligence not just because they canbusiness today is awash in data and data crunchers-but also because they should (Davenport, 2006).
For example, data on education, skills and past performance of their employees will help companies
identify the critical talent within the organization and ensure HR retains it. The data management and
analytics components of Business Intelligence systems are designed to be able to collect data from
various sources, convert the raw data into useful actionable information or knowledge. Employee data
is generally housed in separate HR systems based on vertical HR functions, such as benefits, payroll
and compensation, leave, training and surveys and/or horizontally across functional areas. Companies
need to identify all internal and external data sources and then consolidate the data into a HR data
mart. Some examples of external data sources are US Bureau of Labor Statistics, Employment and
Earnings, Collective agreements, Industry benchmarks, and Labor regulations. There are numerous
Extract, Transform, and Load (ETL) tools on the market to extract data from both internal and
external sources and combine them into a data mart or a data warehouse (IBM White Paper, March
2009).
Another challenge contemporary businesses are facing today is that the business environment is
constantly evolving into a more complex system. And with global competition and the flat and
connected new world (Friedman, 2004), decision-making in organizations has become increasingly
intricate and convoluted. The availability of relatively cheap labor and growing consumers in
developing countries, and aging population in the US, has put pressure on the organizations to go
global for business opportunities. This creates challenges for global organizations' HR departments to
manage workforce diverse in cultures, time zones, expertise, benefits, and compensations. Given that
total workforce compensation represents 60% to 70% of the general expenses, businesses are under
pressures to respond quickly to the dynamic conditions of the business environment. The response
often involves redesigning organizational structures, redefining value propositions, and streamlining
processes. Business Intelligence and analytics can aid in making informed decisions based on
knowledge extracted from the data and options at hand. Organizations that have successfully
implemented BI are able to make decisions quickly and with more accuracy. They have better and
faster access to the key activities and processes that the organizations and its functional departments
must pursue to meets its goals and objectives.
COMPONENTS OF A BUSINESS INTELLIGENCE SYSTEM
The BI system consists of a number of component systems that are interdependent. For the system to
function effectively the components must work in an integrated and coordinated way. The various BI
components may be broadly classified into the following four sub-systems: Data Management,
Advanced Analytics, Business Performance Management, and Information Delivery.
1. The Data Management sub-system includes components, relating to Data warehouses, Data marts,
and Online Analytical Processing (OLAP). The people who work mainly in this area are "technologists",
who specialize in Computer Science, Management Information Systems (MIS), or a related discipline.
This sub-system deals with all aspects of managing the development, implementation and operations
of a data warehouse or data mart including extraction, transformation, cleaning, and loading of data
from different sources. The subsystem also includes meta-data management, security management,
backup and recovery, and data distribution.
The data warehouse is the foundation for business intelligence system operations, two of which are
multi dimensional analysis through OLAP and data analytics. The core of any OLAP system is an OLAP
cube (also called a 'multidimensional cube' or a hypercube). An OLAP cube is a data structure that
allows fast and efficient analysis of large volumes of data from multiple dimensional views.
Online analytical processing, as defined by the OLAP Council, is a category of software technology that
enables analysts, managers and executives to gain insight into data through fast, consistent, and
interactive access to a wide variety of possible views of information that have been transformed from
raw data to reflect real dimensionality of the enterprise as understood by the user. OLAP functionality
is characterized by dynamic multi-dimensional analysis of consolidated enterprise data supporting end
user analytical and navigational activities including calculations and modeling applied across
dimensions, through hierarchies and/or across members, trend analysis over sequential time periods,
slicing subsets for on-screen viewing, drill down to deeper levels of consolidation, reach-through to
underlying detail data, and rotation to new dimensional comparisons in the viewing area. OLAP is
implemented in a multiuser environment and offers consistent, quick response, regardless of database
size and complexity. OLAP helps the user synthesize information through comparative, personalized
viewing, as well as through analysis of historical and projected data in various "what-if" data model
scenarios. This is achieved through use of an OLAP server.
The data warehousing and OLAP focus on gaining insight into their historic data stored in their data
warehouses. They use the past data to answer questions, such as - What happened? Why did it
happen? For example, the employees' past data can shed light on employee attrition fluctuations and
factors that were responsible for the fluctuations.
While there is value in knowing what happened in the past, The Advanced Analytics subsystem
enables organizations to answer deeper questions, such as - What if the trend continues? What are
the best actions to take? What will happen as a result of these actions?
2. The Advanced Analytics sub-system includes analytic functions based on statistics, data mining,
forecasting, predictive modeling, predictive analytics, and optimization. The people who work mainly
in this area are "super users", who specialize in Mathematics, Statistics, Management Science or a
related discipline.
Large BI vendors have incorporated comprehensive statistics packages within their BI software
system. For example, IBM has incorporated the SPSS with their BI Cognos system, and SAS Inc. has
developed their BI software with a core consisting of their celebrated statistical packaged
software. Microsoft has developed XLSTAT, an add-on to Excel for Statistics and multivariate data
analysis. Along with the inclusion of statistical capabilities, BI vendors have also incorporated Data
mining capabilities in their software. Data mining is an extension of statistical techniques such as,
classical and artificial intelligence. Statistical techniques are generally applied to relatively small size
random sample data specifically collected to validate a hypothesis, and the techniques conform to a
set of assumptions about the population. These statistical techniques are called 'verification driven
techniques'. Data mining includes techniques - called 'discovery driven techniques' - that can attempt
to discover information by using appropriate algorithms automatically. These techniques can be
regarded as discovering information by exploration (Kim, 2002; tan, Steinbach, Kumar, 2006;
Shmueli, Patel, Bruce, 2010).
3. The Business Performance Management sub-system consists of processes for strategic goals and
objectives, performance measurement and mentoring, analyzing performance and making decisions to
improve business performance.
Strategic Goals and Objectives - A strategic goal is a broad statement of what the management wants
to achieve, and an objective is usually time bound specific course of action that contributes to the
achievement of the strategic goal. There may be several objectives pertaining to a goal. Here is an
example of a goal and its two objectives as planned by Human Resources department of the state of
California (http://www.dpa.ca.gov/hr-mod/ accomplishments-and-goals/mission-statement-goals-andobjectives.htm):
Goal: Improve and instill high performance in the workplace.
The primary purpose of employee learning and performance management is to improve both
employee and organizational performance. Within state service, staff development is too often
considered an expense rather than an investment. This goal will establish performance management
as a basic objective for improving service to internal and external customers including the citizens of
California.
Objective #1: Ensure supervisors/managers acquire, implement, and apply principles necessary to
foster high performance in the workplace.
Currently, employee appraisals are done irregularly, despite the requirements for annual appraisals
and individual development plans. As we convert to a competency-based HR system, performance will
be used to assess current competencies, identify training needs, and better align resources to achieve
organizational effectiveness. It is essential supervisors/managers continually provide meaningful
feedback.
Properly trained, informed and accountable management is the key to establish and foster high
performance. To achieve this, performance management training and training assessment methods
must be established to evaluate the effectiveness of the training received. Continuing education and
resources (e.g. communication forums, webcasts) must be made available to keep supervisors/
managers informed and apprised of industry best practices. Automated tools must also be created and
made available to support improved methods for ascertaining the achievement of performance goals
and objectives. These will further provide opportunities to more effectively measure management
compliance. A compliant, consistent, and available 80-hour supervisor/manager training program will
be deployed by winter 2009.
Objective #2: Ensure supervisors/managers conduct meaningful timely performance appraisals.
This objective will equip supervisors/managers with the skills necessary to provide constructive
feedback to staff. Supervisors/managers will be held accountable in their personal performance
appraisals for assessing their staff. Initial learning management tools will be available by summer
2009. Identification of performance management tool(s) will be accomplished by fall 2009.
Organizations make operational and financial plans to achieve organization's strategic goals and
objectives, and then initiate projects to implement the plans.
(1) Performance Measurement and Monitoring - During the entire period of the project, the outcomes
are measured and trends are monitored in real time. The measurements are done according to certain
key performance indicators (KPIs). The KPIs need to be designed for each functional area and for each
of the levels in the organization starting from the highest, moving towards the lowest.
Performance indicators are leading indicators that gauge the outcome by examining incremental
progress of the project. Performance indicators with respect to human resources may include
employee retention, job satisfaction, compensation and rewards, employee training, accident levels,
employee absenteeism, and employee performance.
(2) Analyzing Performance - KPIs are compared to the strategic goals and objectives. The results are
utilized to monitor, further analyze and act to improve performance. The Advanced Analytics
subsystem enables organizations to make informed decisions to align their goals and objectives, as
well as programs and budgets to the performance indicators.
(3) Decision Making and Performance Feedback - The organizations are able to adjust their goals and
objectives, modify programs, and re-allocate resources and funds. Performance measures in essence
provide a feedback loop in the process of business performance management.
4. The Information Delivery sub-system gives the business users the ability to access reports and
continuously monitor organizational performance at enterprise and lower levels. According to his or
her role as a technocrat, super user, middle manager, executive manager, or operational user, he or
she will be given role-based rights to access relevant reports in summary and/or detailed formats. End
users are also able to monitor the key activities such as trends, metrics, and KPIs in easy-tounderstand designs, such as configurable information portals, scorecards and dashboards. Depending
on an individual's role and responsibility, he or she is presented with the trends, metrics, and KPIs at
appropriate aggregate levels with security to block non-privileged items.
BUSINESS INTELLIGENCE ADOPTION IN INDUSTRY AND HUMAN RESOURCES
BI is gaining rapidly in popularity, faster than most anticipated. In a report done in April 2010, it was
found that the BI platforms, analytic applications and performance management (PM) software
revenue surpassed $9.3 billion in 2009, a 4.2 percent increase from the 2008 revenue of $8.9 billion
(Gartner, April 2010).
BI platforms were found to have a $5.98 billion (64.2%) market share of total worldwide BI software
revenue. Corporate performance management (CPM) suites were found to have a $1.94 billion
(20.8%) market share, and analytic applications and performance management software were found
to have a $1.40 billion (15%) market share. Details are given in Table 1.
The BI software market significantly outperformed the overall enterprise software market in 2009.
Seeing tremendous opportunities in Business Intelligence, there has been a large number of
acquisitions and mergers of BI companies by software giant vendors reported in the past few years.
Major software vendors have made business intelligence software their focal product to develop or
acquire, and sell or service. The top four vendors (SAP, SAS Institute, Oracle, and IBM) makeup most
of the BI market with 64% market share in 2009 - SAP (23.4%), SAS
Institute (14.4%), Oracle (14.4%), and IBM (11.1%) (Gartner, April 2010). The following table gives
the major BI vendors, their recent BI acquisitions, and price paid.
SAP paid 6.8 billion to acquire Business Objects in 2007, which was number one in business
intelligence at that time. The same year, Oracle bought Hyperion for 3.3 billion and IBM bought
Cognos for 5 billion. Business Objects, Hyperion and Cognos were leaders in business intelligence
before they were bought over by the software giants. IBM and Oracle acquired the most BI software
and paid a hefty price for that. Each software giant has benefitted tremendously because of acquiring
these BI focused companies. The software giant vendors now offer several BI products and are leaders
in the area. Each one of them also offers HR modules along with business intelligence and data
analytics capabilities. The following table gives HR module names, and names of the embedded BI and
data analytics products.
BUSINESS INTELLIGENCE AND DATA ANALYTICS FEATURES IN HUMAN RESOURCES
As stated in the previous section, the software joints offer many BI products, including HR embedded
with business intelligence and data analytics capabilities. The vendors claim the following features and
functions in their software:
SAP ERP Workforce Analytics
This product includes features and functions that support these business activities
(http://www.sap.com/solutions/business-suite/erp/featuresfunctions/workforceanalysis/index.epx):
* Workforce Planning
Understand current workforce trends - and plan future needs - by using workforce demographic data.
Use predefined reports to analyze headcount development, turnover rates, and workforce composition.
Link the results of this analysis directly into headcount planning, budgeting, and key talent processes,
such as recruiting and learning.
* Workforce Cost Planning and Simulation
Support HR professionals in all workforce cost-planning tasks, and empower HR executives to develop
effective strategies. Provide access to a broad range of workforce-related data to support accurate
planning, facilitate simulated planning scenarios, and enable continuous monitoring of actual
performance relative to plan.
* Workforce Benchmarking
Measure standard workforce processes. Compare the measurements with external benchmarks and
internal operating thresholds.
* Workforce Process Analytics and Measurement
Measure and analyze typical core HR processes, such as payroll, employee administration, time
management, and benefits. Analyze organizational structures, relationships, and attributes of jobs and
positions.
* Talent Management Analytics and Measurement
Analyze employee skills and qualifications. Evaluate the efficiency of your recruiting processes.
Measure the effectiveness of your learning programs. Assess how well your succession programs
prepare your employees to assume key positions - and ensure continuity of operations. Monitor the
progress of aligning employee goals with corporate goals. Analyze the cost-effectiveness of employee
compensation programs
* Strategic Alignment
Ensure that all business activities are in line with the strategic goals of your organization. Help
Employee teams work toward common objectives, regardless of location. Use a balanced scorecard
framework, with predefined workforce scorecards that can be integrated into department and
individual management-by-objective (MBO) documents to align employee goals with corporate
strategy.
SAS Human Capital Predictive Analytics and Retention Modeling
This product includes the following features (http://www.sas.com/solutions/hci/hcretention):
* Predicted Turnover Percentage
Ranking by high, medium and low risk of voluntary termination
* Causes of Voluntary Termination
Showing each cause identified by the model in an individual report.
* Organization Exposure
Looking at the high-risk group only and showing a hierarchical view starting at the top and drilling
down through all organizational levels.
* High Risk by Job Category
Identifying those in the high-risk group by EEO job category
* Top 50 Employees at High Risk
Pinpointing the 50 employees most likely to voluntarily leave
* Top Performer
Identify high-risk top performers and define their reasons for leaving.
Oracle Human Resources Analytics
This product includes the following features (http://www.oracle.com/us/solutions/ent-performancebi/hr-analytics-066536.html):
* Workforce Insight
Monitor workforce demographics in line with your recruitment and retention objectives. Analyze
efficiency of the entire recruitment process lifecycle, understand and prevent the drivers of employee
turnover.
* Targeted Workforce Development
Gain insight into the movement of top and bottom performers in the organization to engage and
develop internal talent. Gain insight into learning demand by analyzing course enrollments by job,
delivery methods, and organizations.
* Improved Compensation
Understand how compensation impacts performance, ensure compensation is equitable and consistent
across roles, and align variable compensation with your organization's objectives and goals.
* Leave and Absence
Get a comprehensive view into employees' current, planned, and historical absence events; monitor
absence trends as a predictor for employee engagement.
* Better Understanding of HR Performance
Assess HR's overall performance and employee productivity using industry benchmarks such as
revenue per employee, contribution per headcount, and return on human capital.
* US Statutory Compliance
Monitor US EEO, AAP, and Vets100 compliance reporting.
* Workforce Planning
Monitor workforce demographics in line with your recruitment and retention objectives. Analyze
efficiency of the entire recruitment process lifecycle, understand and prevent the drivers of employee
turnover.
* Workforce Cost Planning and Simulation
Support HR professionals in all workforce cost-planning tasks, and empower HR executives to develop
effective strategies. Provide access to a broad range of workforce-related data to support accurate
planning, facilitate simulated planning scenarios, and enable continuous monitoring of actual
performance relative to plan.
* Workforce Benchmarking
Measure standard workforce processes. Compare the measurements with external benchmarks and
internal operating thresholds.
* Workforce Process Analytics and Measurement
Measure and analyze typical core HR processes, such as payroll, employee administration, time
management, and benefits. Analyze organizational structures, relationships, and attributes of jobs and
positions.
* Talent Management Analytics and Measurement
Analyze employee skills and qualifications. Evaluate the efficiency of your recruiting processes.
Measure the effectiveness of your learning programs. Assess how well your succession programs
prepare your employees to assume key positions - and ensure continuity of operations. Monitor the
progress of aligning employee goals with corporate goals. Analyze the cost-effectiveness of employee
compensation programs
IBM Cognos Business Intelligence and Human Resource Performance Management
This product includes the following features pertaining to the following five HR core areas
(http://www-01.ibm.com/software/data/cognos/solutions/human-resources/index.html):
* Organization and Staffing
What job functions, positions, roles and capabilities are required to drive the business performance?
* Compensation
How should we reward our employees to retain and motivate them for full performance?
* Talent and Succession
What are the talent and succession gaps we must address to ensure sustained performance?
* Training and Development
What training and development do we need to maximize employee performance? Is there a clear
payback?
* Benefits
How do we manage costs and incentives for better performance?
SUMMARY AND CONCLUSION
Business Intelligence is helping businesses become more competitive. Because of technological
progress and regulatory changes, businesses are collecting and storing data at an alarming rate.
Because the business environment is constantly changing, decision making in organizations has
become increasingly intricate. Business Intelligence is helping organizations make faster and more
reliable information based business decisions.
Business Intelligence is seen as consisting of four major inter-dependent sub-systems - Data
Management, Advanced Analytics, Business Performance, and Information Delivery sub-system. The
Data Management subsystem handles data storage into databases, data warehouses, and data marts.
With On-line Analytical Processing (OLAP) built into the system, it is possible to do multi-dimensional
analysis on the data. The Advanced Analytics processing system includes analytic methods based on
statistics, data mining, forecasting, predictive modeling, predictive analytics, and optimization. The
Business Performance system consists of processes for performance measurement and mentoring and
making decisions to improve business performance. Key Performance Indicators (KPIs) take a
prominent role in this sub system. KPIs are defined to measure progress toward organizational goals.
The Information Delivery subsystem provides information to users in real time and in a format they
want it. End users are able to monitor the key activities in easy-to-understand formats, such as
configurable information portals, scorecards and dashboards.
Having seen Business Intelligence as the core enterprise strategy, the giant software vendors have
spent billions on acquiring other business intelligence focused companies. The giant vendors have now
business intelligence solutions for organizations of most types and sizes. While many organizations
have purchased their BI software and are starting to use in many areas of their businesses and make
substantial gains, but they have not taken advantage of this in Human Resource Management area.
Executives view Human Resources more of a cost center, and less of a strategic asset within their
organizations. In the last section of the paper we have examined the leading vendors to look into the
business intelligence and data analytics features incorporated in their Human Resource Management
software. By taking advantage of the rich business intelligence features in these and other similar
products, Human Resources can position itself as essential value-adding department of the
organization.