A structured modeling based methodology to design decision support systems

Abstract

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.