Conclusions and Commentary
Learning to build models and Model-Driven DSS is a complex task that requires extensive preparatory work. MIS professionals who want to build models need a strong background in management science and operations research. If managers and MIS professionals want to design and build successful Model-Driven DSS, they may need to expand their skills. If management scientists want to contribute more in building these DSS, they should cultivate a very broad understanding of Decision Support Systems and focus less on specific quantitative tools and technologies.
Models are very important components in many DSS, but "bad" models result in "bad" decisions. Many models can be implemented quickly using prototyping. Using prototyping a new DSS can be constructed in a short time, tested, and improved in several iterations. This development approach helps us test the effectiveness of the overall design. The downside of prototyping is that a new DSS may be hard to deploy to a wide group of users. Managers and DSS analysts need to make sure the scaled down DSS will work when it is deployed more widely in a company.
On-line Analytical Processing (OLAP) is one example of a hybrid system that uses simple analytical techniques to analyze large data sets. Many other Model-Driven DSS can be built that use a variety of organizational and external data sets. Managers should be consumers and developers of Model-Driven DSS. Widely used Model-Driven DSS need to be built systematically by a team of model specialists, MIS and network specialists and managers. Small-scale systems can be purchased or built using tools like Microsoft Excel. New Model-Driven Decision Support Systems must capture the complexity of a decision and be easily implemented and integrated into existing systems.
Model-Driven DSS remain important support tools for managers. The interest in Data-Driven DSS and Group DSS should not distract managers from the need to update existing model-based systems and develop new capabilities that can be implemented using Web technologies. The development environment for building Model-Driven DSS is powerful and increasingly Web "friendly".
Historically, a small number of experts in management science and operations research have performed sophisticated model-driven analyses for companies. As the emphasis upon flexibility and competition increases, more and more individuals within companies will need to build and use Model-Driven DSS. Managers and DSS analysts need to be actively involved in identifying the need for and purpose of Model-Driven Decision Support Systems.