Management Science

Management Science (MS) can be loosely defined as a collection of advanced quantitative techniques whose application can increase profits by optimizing the cost efficiency of business operations. One could make a solid argument that MS came into being about 60 years ago through a technique known as Linear Programming (LP). Linear Programming was first utilized with overwhelming success during the Berlin Airlift to ensure that every available inch of cargo space of each plane that landed in Berlin was filled with the most essential supplies for the inhabitants of that beleaguered city. Six decades later LP is still one of the most powerful analytic weapons available in the management science arsenal, its great power being its ability to tell a business manger who has certain limited resources (e.g. – warehouse space. raw materials, labor hours, financing) what course of action will guarantee an optimal profit.

In the course of the ensuing 60 years a number of other MS techniques have been developed to help businesses increase their cost efficiency, techniques like Network Analysis, Non-linear Programming, Monte Carlo Simulation, and Genetic Algorithms. These advanced applied techniques are routinely utilized by Fortune 500 corporations to ensure that the operating decisions they make return optimal profits. Yet there is nothing inherently “mega-corporate“ about these techniques. The MS techniques that a multi-billion dollar corporation like EXXON uses to optimize their decision making processes are the exact same ones that can be employed to optimize the assembly line at a million dollar corporation like ACME Coat Hangers LLC.

Simply put, applying MS techniques assures the small business owner that he is taking the most profitable course of action possible when confronted with a critical business decision, whether that decision is as straight forward as deciding the most profitable way to manufacture a bookcase, or something as esoteric as simulating an entire factory in cyber-space to determine its optimal production capacity configuration before spending a dime on its construction.