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‘Modeling’ for the organization can be good,
September 22, 2D.

If you
were to receive word that one of your coworkers has begun
“modeling” for your organization, you might be shocked. The
fact is that organizations that support and practice
modeling can operate more efficiently than those that do not
and in the process, save precious organizational resources.
The modeling described here is not the type that involves
hairspray, nice clothes, and looking good in front of a
camera. Rather, it is the type of modeling that management
science practitioners use to solve complex organizational
problems.

For
example, suppose that your company produces three types of
products. Each product yields different amounts of profit
on each item sold. Additionally, each product incurs
different production costs and requires different
combinations of human and manufacturing inputs. Also,
assume that there are different limits on the quantities of
each of the three products that can be produced due to
different budget, raw materials, labor, and/or
production-capacity constraints. The question of “how many
of each of the three types of products should the company
produce to maximize profit?” ultimately arises.

Managers
who regularly encounter these types of problems have a
well-defined body of tools and techniques at their disposal
to solve such problems. The field of Management Science is
the area of business that works with and develops solutions
to these types of problems. Also known as quantitative
business analysis or decision systems, these tools and
techniques can be used to solve a wide variety of complex
organizational problems. The term “modeling” refers to
developing accurate representations of organizational
problems and decisions using mathematical expressions and
controls.

Variations of the linear programming method used to solve
the “product blend” problem described above can also be used
to assign workers (with different abilities, credentials,
and costs) to work shifts with differing personnel and
staffing requirements, minimize shipping costs for products
transported from factories and warehouses to distribution
outlets, and determine the lowest-cost combinations of raw
ingredients that meet minimum or maximum ingredient
requirements. Other quantitative modeling techniques can be
used to develop forecasting and “what if” scenarios for a
variety of complex and important business decisions.

These
tools are used when problems are complex, when solutions are
not readily apparent, when there is sufficient time to
create and run the models, and when the solutions can
provide significant payoffs to organizations. The
management science field is replete with examples of
large-scale organizations saving millions of dollars in
costs as a result of employing and implementing management
science techniques and solutions.

Once for
use only by people with advanced mathematical skills and
those with access to supercomputers, many of these modeling
techniques can now be run on personal computers using
Microsoft Excel. Business owners and managers should
investigate how these techniques could help make their
operations more efficient and profitable—the benefits they
might reap could far outweigh the costs of learning,
mastering, and using these techniques.

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