Decision Support Systems

4.0 Enhancing Decision Making with MIS

Management information system (MIS) support systems rely on models for computational and analytical routines that mathematically express relationships among variables. For instance, a spread sheet program, such as Microsoft Office Excel, might contain models that calculate market or Return On Investment. Management information system have the capability and functionality to express far more complex modelling relationship that provide information, business intelligence and knowledge. There are three primary types of management information systems available to support decision making across the company levels which are operational support systems, managerial support systems and strategic support systems

Decision Support Systems a term coined by P. G. Keen et al. at MIT in the mid‐1970s, are used to describe systems which support managers in their decision‐making activities.

Sprague and Carlson define decision support system as computer‐based systems which help decision makers confront ill‐structured problems through direct interaction with data and analysis models. Sprague and Carlson make the point that a good decision support system should have a balance among the DDM paradigms which are dialogue, data and modelling. It should have access to a wide variety of data and it should provide analysis and modelling in a variety of ways. ( Sprague, R.H. and Carlson, E.D., 1982)

Bonczek et al. define an intelligent support system, the next generation of decision support system, as a form of decision support system in which the human decision maker combines heuristics and a knowledge base to produce answers for a certain class of unstructured problems. (Bonczek, R.H., Holsapple, C.W. and Whinston, A.B.,

Most sensitivity analyses examine the impact that changes in input variables have on output variables. Sensitivity analysis is extremely valuable because it enables the system to adapt to changing conditions and to the varying requirements of different decision-making situations. Sensitivity analysis provides a better understanding of the model and the problem it purports to describe. Sensitivity analysis also may increase the user’s confidence in the model, especially if it indicates that the model is not very sensitive to changes. A sensitive model means that small changes in conditions dictate a different solution. In a non-sensitive model, changes in conditions do not significantly change the recommended solution. For this reason, the chances for a solution to succeed are much higher in a non-sensitive model than a sensitive