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Chapter 2 • Foundations and Technologies for Decision Making 77

sectiOn 2.4 revieW QuestiOns
1. What is the difference between a problem and its symptoms?
2. Why is it important to classify a problem?
3. What is meant by problem decomposition?
4. Why is establishing problem ownership so important in the decision-making process?

2.5 Decision Making: the Design Phase

The design phase involves finding or developing and analyzing possible courses of action.
These include understanding the problem and testing solutions for feasibility. A model
of the decision-making problem is constructed, tested, and validated. Let us first define
a model.

Models 1
A major characteristic of a DSS and many BI tools (notably those of business analytics) is the
inclusion of at least one model. The basic idea is to perform the DSS analysis on a model
of reality rather than on the real system. A model is a simplified representation or abstrac-
tion of reality. It is usually simplified because reality is too complex to describe exactly and
because much of the complexity is actually irrelevant in solving a specific problem.

Mathematical (Quantitative) Models
The complexity of relationships in many organizational systems is described mathemati-
cally. Most DSS analyses are performed numerically with mathematical or other quantitative
models.
the benefits of Models

We use models for the following reasons:
• Manipulating a model (changing decision variables or the environment) is much
easier than manipulating a real system. Experimentation is easier and does not
interfere with the organization’s daily operations.
• Models enable the compression of time. Years of operations can be simulated in
minutes or seconds of computer time.
• The cost of modeling analysis is much lower than the cost of a similar experiment
conducted on a real system.
• The cost of making mistakes during a trial-and-error experiment is much lower
when models are used than with real systems.
• The business environment involves considerable uncertainty. With modeling, a
manager can estimate the risks resulting from specific actions.
• Mathematical models enable the analysis of a very large, sometimes infinite, number
of possible solutions. Even in simple problems, managers often have a large number
of alternatives from which to choose.
• Models enhance and reinforce learning and training.
• Models and solution methods are readily available.
Modeling involves conceptualizing a problem and abstracting it to quantitative
and/or qualitative form (see Chapter 9). For a mathematical model, the variables are

1 Caution: Many students and professionals view models strictly as those of “data modeling” in the context of
systems analysis and design. Here, we consider analytical models such as those of linear programming, simula-
tion, and forecasting.

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