4. Decision Support andBusiness Int

4. Decision Support and
Business Intelligence

CASE IV: DEBOER FARMS
The DeBoer family’s roots run deep in their South
Dakota farm. Carl DeBoer’s great-grandfather
Johann began farming in the Dakota Territory just
before it became a state in 1889. Through the years,
each generation worked hard, saved, built improvements,
and added to the farm, until it grew from its
original several hundred acres to its current size of
12,000 acres. His great-grandfather would hardly
recognize the place these days, Carl thought.
DeBoer Farms’ acreage is planted in corn, soybeans,
wheat, oats, and alfalfa. Carl inherited the farm last
month when his father passed, although he had
been managing the farm on his own for the past ten
years. Big changes lay ahead for both Carl and
DeBoer Farms.

10. Decision Support and
Expert Systems

LEARNING OBJECTIVES
Decision making plays a key role in managerial work. Managers often have to
consider large amounts of data, extract and synthesize only relevant information,
and make decisions that will benefit the organization. As the amount of available
data grows, so does the need for computer-based aids to assist managers in their
decision-making process.
When you finish this chapter, you will be able to:
 List and explain the phases in decision making.
 Articulate the difference between structured and unstructured decision making.
 Describe the typical software components that decision support systems and
expert systems comprise.
 Give examples of how decision support systems and expert systems are used in
various domains.
 Describe the typical elements and uses of geographic information systems.

DEBOER FARMS:
Farming Technology for Information
Carl DeBoer was finishing some paperwork for the
day in his farm office when his computer beeped.
Steve Janssen from the South Dakota Cooperative
Extension Service had sent him an instant message.
Carl knew many of the service staff, but Steve was
an old friend. Carl had consulted with Steve for the
past 20 years.

DECISION SUPPORT
The success of an organization largely depends on the quality of the decisions that its employees
make. When decision making involves large amounts of information and a lot of processing,
computer-based systems can make the process efficient and effective. This chapter discusses two
types of decision support aids: decision support systems (DSSs) and expert systems (ESs). In recent
years applications have been developed to combine several features and methods of these aids.
Also, decision support modules are often part of larger enterprise applications. For example, ERP
(enterprise resource planning) systems support decision making in such areas as production
capacity planning, logistics, and inventory replenishment.

THE DECISION-MAKING PROCESS
When do you have to make a decision? When you drive your car to a certain destination and
there is only one road, you do not have to make a decision. The road will take you there. But if
you come to a fork, you have to decide which way to go. In fact, whenever more than one possible action is available, a decision must be made. If you have to decide based only on
distance, making a decision is easy. If you have to choose between a short but heavily trafficked
road and a longer road with lighter traffic, the decision is a bit more difficult.

STRUCTURED AND UNSTRUCTURED PROBLEMS
A structured problem is one in which an optimal solution can be reached through a single set
of steps. Since the one set of steps is known, and since the steps must be followed in a known
sequence, solving a structured problem with the same data always yields the same solution.
Mathematicians call a sequence of steps an algorithm and the categories of data that are
considered when following those steps parameters. For instance, when considering the
problem of the shortest route for picking up and delivering shipments, the parameters are
shipment size, the time when shipments are ready for pickup, the time when shipments are
needed at their destinations, the distance of existing vehicles from the various destinations, the
mandatory rest times of the drivers, the capacities of the trucks, and so on.

Professionals encounter semistructured problems almost daily in many different industries
and in many different business functions (see Figure 10.2).
A manager solving a typical semistructured problem faces multiple courses of action. The task
is to choose the one alternative that will bring about the best outcome. For example:
• In manufacturing, managers must provide solutions to semistructured problems such as: (1)
Which supplier should we use to receive the best price for purchased raw materials while
guaranteeing on-time delivery? (2) Assembly line B has a stoppage; should we transfer
workers to another assembly line or wait for B to be fixed? (3) Demand for product X has
decreased; should we dismantle one of the production lines, or should we continue to
manufacture at the current rate, stock the finished products, and wait for an upswing in
demand?
• Managers of investment portfolios must face semistructured decision making when they
decide which securities to sell and which to buy so they can maximize the overall return on
investment. The purpose of research in stock investing is to minimize uncertainties by trying
to find patterns of behavior of stocks, among other trends. Managers of mutual funds spend
much of their time in semistructured decision making.
• Human resource managers are faced with semistructured problems when they have to decide
whom to recommend for a new position, considering a person’s qualifications and his or her
ability to learn and assume new responsibilities.
• Marketing professionals face semistructured problems constantly: should they spend money
on print, television, Web, e-mail, or direct-mail advertisements? Which sector of the
population should they target?
Because of the complexities of the problems they face, managers in many functional areas
often rely on decision support applications to select the best course of action.