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80 Part I • Decision Making and Analytics: An Overview

marketing department that implements an electronic commerce (e-commerce) site. Within
hours, orders far exceed production capacity. The production department, which plans
its own schedule, cannot meet demand. It may gear up for as high demand as possi-
ble. Ideally and independently, the department should produce only a few products in
extremely large quantities to minimize manufacturing costs. However, such a plan might
result in large, costly inventories and marketing difficulties caused by the lack of a variety
of products, especially if customers start to cancel orders that are not met in a timely way.
This situation illustrates the sequential nature of decision making.
A systems point of view assesses the impact of every decision on the entire sys-
tem. Thus, the marketing department should make its plans in conjunction with other
departments. However, such an approach may require a complicated, expensive, time-
consuming analysis. In practice, the MSS builder may close the system within narrow
boundaries, considering only the part of the organization under study (the marketing and/
or production department, in this case). By simplifying, the model then does not incorpo-
rate certain complicated relationships that describe interactions with and among the other
departments. The other departments can be aggregated into simple model components.
Such an approach is called suboptimization.
If a suboptimal decision is made in one part of the organization without considering
the details of the rest of the organization, then an optimal solution from the point of view
of that part may be inferior for the whole. However, suboptimization may still be a very
practical approach to decision making, and many problems are first approached from this
perspective. It is possible to reach tentative conclusions (and generally usable results) by
analyzing only a portion of a system, without getting bogged down in too many details.
After a solution is proposed, its potential effects on the remaining departments of the
organization can be tested. If no significant negative effects are found, the solution can
be implemented.
Suboptimization may also apply when simplifying assumptions are used in mod-
eling a specific problem. There may be too many details or too many data to incorporate
into a specific decision-making situation, and so not all of them are used in the model.
If the solution to the model seems reasonable, it may be valid for the problem and thus
be adopted. For example, in a production department, parts are often partitioned into
A/B/C inventory categories. Generally, A items (e.g., large gears, whole assemblies) are
expensive (say, $3,000 or more each), built to order in small batches, and inventoried in
low quantities; C items (e.g., nuts, bolts, screws) are very inexpensive (say, less than $2)
and ordered and used in very large quantities; and B items fall in between. All A items
can be handled by a detailed scheduling model and physically monitored closely by man-
agement; B items are generally somewhat aggregated, their groupings are scheduled, and
management reviews these parts less frequently; and C items are not scheduled but are
simply acquired or built based on a policy defined by management with a simple eco-
nomic order quantity (EOQ) ordering system that assumes constant annual demand. The
policy might be reviewed once a year. This situation applies when determining all criteria
or modeling the entire problem becomes prohibitively time-consuming or expensive.
Suboptimization may also involve simply bounding the search for an optimum
(e.g., by a heuristic) by considering fewer criteria or alternatives or by eliminating large
portions of the problem from evaluation. If it takes too long to solve a problem, a good-
enough solution found already may be used and the optimization effort terminated.

Descriptive Models
Descriptive models describe things as they are or as they are believed to be. These
models are typically mathematically based. Descriptive models are extremely useful in
DSS for investigating the consequences of various alternative courses of action under

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