Management Science Models in Decision Support Systems

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Historically management science models have been applied to the solution of specific types of problems; for example, a waiting line model is used to analyze a specific waiting line system at a store or bank. However, the evolution of computer and information technology has enabled the development of expansive computer systems that combine several management science models and solution techniques in order to address more complex, interrelated organizational problems. A decision support system (DSS ) is a computer-based system that helps decision makers address complex problems that cut across different parts of an organization and operations.

A DSS is normally interactive , combining various databases and different management science models and solution techniques with a user interface that enables the decision maker to ask questions and receive answers. In its simplest form any computer-based software program that helps a decision maker make a decision can be referred to as a DSS. For example, an Excel spreadsheet like the one shown for break-even analysis in Exhibit 1.1 or the QM for Windows model shown in Exhibit 1.4 can realistically be called a DSS. Alternatively enterprisewide DSSs can encompass many different types of models and large data warehouses, and they can serve many decision makers in an organization. They can provide decision makers with interrelated information and analyses about almost anything in a company.

Figure 1.7 illustrates the basic structure of a DSS with a database component, a modeling component, and a user interface with the decision maker. As noted earlier, each of these components can be small and singular, with one analytical model linked to a database, or they can be very large and complex, linking many models and large databases. A DSS can be primarily a data-oriented system, or it can be a model-oriented system. A new type of DSS, called an online analytical processing system, or OLAP , focuses on the use of analytical techniques such as management science models and statistics for decision making. A desktop DSS for a single user can be a spreadsheet program such as Excel to develop specific solutions to individual problems. Exhibit 1.1 includes all the components of a DSScost, volume, and price data, a break-even model, and the opportunity for the user to manipulate the data and see the results (i.e., a user interface). Expert Choice is another example of a desktop DSS that uses the analytical hierarchy process (AHP) described in Chapter 9 to structure complex problems by establishing decision criteria, developing priorities, and ranking decision alternatives.

On the other end of the DSS spectrum, an enterprise resource planning (ERP) system is software that can connect the components and functions of an entire company. It can transform data, such as individual daily sales, directly into information that supports immediate decisions in other parts of the company, such as ordering, manufacturing, inventory, and distribution. A large-scale DSS such as an ERP system in a company might include a forecasting model (Chapter 15) to analyze sales data and help determine future product demand; an inventory model (Chapter 16) to determine how much inventory to keep on hand; a linear programming model (Chapters 25) to determine how much material to order and product to produce, and when to produce it; a transportation model (Chapter 6) to determine the most cost-effective method of distributing a product to customers; and a network flow model (Chapter 7) to determine the best delivery routes. All these different management science models and the data necessary to support them can be linked in a single enterprisewide DSS that can provide many decisions to many different decision makers.

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Figure 1.7. A decision support system

In addition to helping managers answer specific questions and make decisions, a DSS may be most useful in answering what-if? questions and performing sensitivity analysis. In other words, a DSS provides a computer-based laboratory to perform experiments. By linking various management science models together with different databases, a user can change a parameter in one model related to one company function and see what the effect will be in a model related to a different operation in the company. For example, by changing the data in a forecasting model, a manager could see the impact of a hypothetical change in product demand on the production schedule, as determined by a linear programming model.

Advances in information and computer technology have provided the opportunity to apply management science models to a broad array of complex organizational problems by linking different models to databases in a DSS. These advances have also made the application of management science models more readily available to individual users in the form of desktop DSSs that can help managers make better decisions relative to their day-to-day operations. In the future it will undoubtedly become even easier to apply management science to the solution of problems with the development of newer software, and management science will become even more important and pervasive as an aid to decision makers as managers are linked within companies with sophisticated computer systems and between companies via the Internet.

Many companies now interface with new types of DSS over the Internet. In e-business applications companies can link to other business units around the world through computer systems called intranets , with other companies through systems called extranets , and over the Internet. For example, electronic data interchange (EDI) and point-of-sale data (through bar codes) can provide companies with instantaneous records of business transactions and sales at retail stores that are immediately entered into a company's DSS to update inventory and production scheduling, using management science models. Internet transportation exchanges enable companies to arrange cost-effective transportation of their products at Web sites that match shipping loads with available trucks at the lowest cost and fastest delivery speed, using sophisticated management science models.

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Management Science Application: A Decision Support System for Aluminum Can Production at Coors

Valley Metal Container (VMC), a joint venture between Coors Brewing Company and American National Can, operates the largest single facility for aluminum can production in the world in Golden, Colorado. The plant, encompassing six production lines, manufactures over 4 billion cans per year for seven Coors beer labels produced at Coors breweries in Colorado, Virginia, and Tennessee. The breweries' weekly production schedules are unpredictable, and if sufficient cans are not available, brewery fill lines can be shut down, at a cost of $65 per minute. In order to cope with variable brewery demand, VMC builds up inventories of cans for all seven Coors labels during winter and spring, when beer demand is lower, in order to meet higher demand in the summer. Each production line at the can production plant produces multiple labels, and when a line is switched from one label to another, a label change occurs. Finished cans go into either short-term inventory (in trailers ), from which they are shipped to a brewery within a day, or two types of longer- term inventory, in which cans are stored on pallets for future delivery.

VMC developed a DSS to determine the weekly production schedule for cans that would meet brewery demand while minimizing the number of costly label changes and associated inventory costs. The DSS includes an Excel-based user interface for data entry. The Excel spreadsheets are linked to a linear programming model that develops the optimal production schedule that minimizes the costs associated with label changes while meeting demand. The spreadsheet format allows data to be entered and adjusted easily on one worksheet, and the production schedule is developed on another worksheet. The production schedule is then reported on another specially formatted spreadsheet, which the user can view, print, and edit. Costs savings with this DSS average about $3,000 per week and annual savings of over $160,000.

Source: E. Katok and D. Ott, "Using Mixed-Integer Programming to Reduce Label Changes in the Coors Aluminum Can Plant," Interfaces 30, no. 2 (MarchApril 2000): 112.

Introduction to Management Science
Introduction to Management Science (10th Edition)
ISBN: 0136064361
EAN: 2147483647
Year: 2006
Pages: 358

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