Mathematical Decision Making: Predictive Models and Optimization

Not so long ago, executives faced with complex problems made decisions based on experience, intuition, and no small measure of luck. But now there’s a better way. In recent decades, mathematics and computer science have perfected formerly top-secret techniques for predicting the best possible outcomes when faced with conflicting options. This field goes by different names—analytics, operations research, linear and nonlinear programming, management science—but its purpose is simple: to apply quantitative methods to help business managers, public servants, investors, scientific researchers, and problem solvers of all kinds make better decisions.

Consider the following applications of this powerful set of tools:

• Pricing: Costco rose to become one of the top-ranked retailers in the world by combining membership fees with the economy of selling in bulk. A mathematical technique—called genetic algorithms—shows the advantages of this strategy as well as the optimum prices to charge.
• Scheduling: Using nonlinear programming, many airlines employ scheduling software that can find the most favorable solution to unexpected disruptions—from weather to mechanical problems to crew availability—saving millions of dollars in operating costs.
• Bidding: Simulation models can take a lot of guesswork out of competitive bidding for a project. By running repeated simulations against competitors, a bidder can come up with a proposal that has a good chance of winning the job, while still making a profit.
• Queuing: Any process that reflects the behavior of waiting lines is known as queuing. Markov analysis shows how a small increase in input to a system can have a major impact on waiting times. The method also reveals surprising solutions for making long waits vanish.

These same techniques can be applied to retirement planning, stock portfolio analysis, budget forecasting, health care allocation, public relations, marketing and advertising, and many other tasks for enterprises large and small. The applications are truly endless!

Mathematical decision making got its first rigorous tests during World War II, when the Allies used top-secret operations research to protect convoys, improve the aim of anti-aircraft fire, and locate the weak points on Allied bombers. After the war, private industry adopted operations research with enthusiasm, but these new methods were expensive, computing speed was slow, and only specialized experts could take advantage of the algorithms. That situation has changed dramatically, and today anyone with a home computer and a spreadsheet program can harness the power of these methods to solve practical problems. The trick is knowing what you can do and how to do it.

About the Author

Dr. Scott P. Stevens is Professor of Computer Information Systems and Management Science at James Madison University in Harrisonburg, Virginia, where he has taught since 1984. Professor Stevens holds a Ph.D. in Mathematics from The Pennsylvania State University, where he received B.S. degrees in both Mathematics and Physics and graduated first in his class in the College of Science. Honored many times over for his remarkable abilities in the classroom, Professor Stevens has been a recipient of the Carl Harter Award, his university’s highest teaching award; been named the outstanding graduate teacher in JMU’s M.B.A. program; and has on five occasions been selected by students as the outstanding teacher in JMU’s undergraduate business program, the first teacher to be so honored. A frequent consultant in the business arena.

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