Many companies use models to assist managers. For example, Dresdner Bank uses a Model-Driven DSS when making credit and lending decisions. USA Truck uses OptiStop to generate optimal routes and fueling stop recommendations. Also, at USA Truck, managers use a DSS called Strategic Profitability Analysis to allocate equipment and establish pricing for customers. Jones Lang LaSalle uses a Web-based system for planning, budgeting, reporting and analysis. Several John Deere factories are using an optimization add-in to Microsoft Excel for balancing manufacturing constraints while achieving more production output. A number of railroad companies use DSS for train dispatching. This list of Model-Driven DSS could go on for many pages.
Many Decision Support Systems use models. For example, a sales forecasting DSS uses a moving average or econometric model; accounting and financial DSS generate estimates of income statements, balance sheets, or other outcome measures; representational DSS use simulation models; and optimization DSS generate optimal solutions consistent with constraints and assist in scheduling and resource allocation. Model-Driven DSS may assist in forecasting product demand, aid in employee scheduling, develop pro forma financial statements or assist in choosing plant or warehouse locations. All of these systems are Model-Driven DSS.
Model-Driven Decision Support Systems (MDSS) provide managers with models and analysis capabilities that can be used during the process of making a decision. The range and scope of this category of DSS is very large. New commercial products are regularly announced, new Web-Based applications are being developed for established tools, and companies are developing their own proprietary systems. To exploit these opportunities, DSS analysts and managers need to understand analytical tools and modeling. Building some types of models requires considerable expertise. Many specialized books discuss and explain how to implement specific types of models like simulation or linear programming. Companies use both custom and off-the-shelf Model-Driven DSS applications.
This chapter is only a starting point for those who want to build or buy Model-Driven DSS. It provides a brief overview of how to build Model-Driven DSS. It summarizes commonly used models with a primary focus on terminology. The major objectives are to help managers and MIS specialists work with model builders and evaluate "off-the-shelf" development products.
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