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Chapter 2 • Foundations and Technologies for Decision Making 97

Models (Model Base)

• Strategic, tactical, operational
• Statistical, financial, marketing, Model
management science, Directory
accounting, engineering, etc.
• Model building blocks

Model Base Management
• Modeling commands: creation Model execution,
• Maintenance: update integration, and
• Database interface command processor
• Modeling language

Data Interface Knowledge-based
management management subsystem

figure 2.6 Structure of the Model Management Subsystem.

programming language. For small and medium-sized DSS or for less complex ones, a spread-
sheet (e.g., Excel) is usually used. We will use Excel for many key examples in this book.
Application Case 2.3 describes a spreadsheet-based DSS. However, using a spreadsheet
for modeling a problem of any significant size presents problems with documentation and
error diagnosis. It is very difficult to determine or understand nested, complex relationships
in spreadsheets created by someone else. This makes it difficult to modify a model built by
someone else. A related issue is the increased likelihood of errors creeping into the formu-
las. With all the equations appearing in the form of cell references, it is challenging to figure
out where an error might be. These issues were addressed in an early generation of DSS
development software that was available on mainframe computers in the 1980s. One such
product was called Interactive Financial Planning System (IFPS). Its developer, Dr. Gerald
Wagner, then released a desktop software called Planners Lab. Planners Lab includes the
following components: (1) an easy-to-use algebraically oriented model-building language
and (2) an easy-to-use state-of-the-art option for visualizing model output, such as answers
to what-if and goal seek questions to analyze results of changes in assumptions. The com-
bination of these components enables business managers and analysts to build, review, and
challenge the assumptions that underlie decision-making scenarios.
Planners Lab makes it possible for the decision makers to “play” with assumptions
to reflect alternative views of the future. Every Planners Lab model is an assemblage of
assumptions about the future. Assumptions may come from databases of historical per-
formance, market research, and the decision makers’ minds, to name a few sources. Most
assumptions about the future come from the decision makers’ accumulated experiences
in the form of opinions.
The resulting collection of equations is a Planners Lab model that tells a readable
story for a particular scenario. Planners Lab lets decision makers describe their plans
in their own words and with their own assumptions. The product’s raison d’être is that
a simulator should facilitate a conversation with the decision maker in the process of

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