Page 60 - DCAP208_Management Support Systems

Unit 5: Modeling and Analysis

Introduction Notes

Modeling is a key element in most DSS and a necessity in a model-based DSS. In this unit, we
will discuss Modeling for MSS, explain static and dynamic models, discuss the concept of certainty,
uncertainty, and risk, describe MSS modeling in spreadsheets, explain the concept of Simulation,
discuss optimization via Mathematical Programming.
5.1 Modeling for MSS

There are many classes of models, and there are often many specialized techniques for solving
each one.

Simulation is a common modeling approach, but there are several others:
A general model, based on an algorithm (for example to make transportation cost
estimates). This model is programmed directly into the DSS.

A demand-forecasting model (statistics based).
A distribution center location model. This model uses aggregated data (a special modeling
technique) and is solved with a standard linear/integer optimization package.

A transportation model (i.e. a specialization of a linear programming model) to determine
the best shipping option from sources to distribution centers (fed to it from the previous
model) and hence to customers. This model is solved using commercial software and is
loosely integrated with the distribution location model.

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Caution The DSS must interface with commercial software and integrate the model.
A financial and risk simulation model that takes into consideration some qualitative
factors that require important human judgment.

A geographical information system (GIS; effectively a graphical model of the data) for a
user interface.

5.1.1 Some Major Modeling Issues

1. Identification of the Problem and Environmental Analysis: An important aspect of the
environmental analysis is environmental scanning and analysis, which is the monitoring,
scanning, and interpretation of collected information. No decision is made in a vacuum.
It is important to analyze the scope of the domain and the forces and dynamics of the
environment. A decision maker needs to identify the organizational culture and the
corporate decision-making process. It is entirely possible that environmental factors have
created the current problem. The problem must be understood, and everyone involved
should share the same frame of understanding because the problem will ultimately be
represented by the model in one form or another. Otherwise, the model will not help the
decision maker.
2. Variable identification: Identification of a model’s variables is critical, as are relationships
of the variables. Influence diagrams, which are graphical models of mathematical models,
can facilitate the identification process. A more general form of an influence diagram, a
cognitive map, can help a decision maker develop a better understanding of a problem,
especially of variables and their interactions.
3. Forecasting – Predictive Analytics: Forecasting is predicting the future. This form of
predictive analytics is essential for construction and manipulating models because when

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