Management science (also known as operational, or operations, research) is an interdisciplinary field that assists decision making through the design and provision of quantitative and qualitative models of problematic situations.
More than any other war before it, World War II challenged the opposing armies with problems concerning logistics, equipment evaluation, and search tactics. Interdisciplinary teams of scientists tackled such problems, resulting in a set of applied mathematical methods that were used successfully to support decision making. Following the war, these methods were quickly appreciated as applicable to the challenges facing management and industry. The crowning achievement was the generic applicability of one particular mathematical algorithm known as the simplex method. This algorithm could find an optimum solution for the attainment of an objective based upon a set of mathematically structured constraints. Its relevance to problems concerning distribution, transportation, location, inventory, and scheduling provided management with a powerful method for controlling costs and maximizing revenues. The 1950s saw a host of new applied mathematical developments (such as the modeling of queues, multistage planning, and feedback modeling), serving to consolidate management science as the field that could provide much-needed support for making decisions in an increasingly competitive world.
The basic approach of management science begins by defining the problem of interest. Data is collected and fed into the structured mathematical model relevant to the particular situation. The model is developed, verified, and validated, and an optimum solution is generated. Since a problematic situation is liable to the perturbations of its contextual environment, an optimum solution is usually accompanied by a set of results that show how it changes with external circumstances—what is known as sensitivity analysis. The model, its results, and recommendations are then communicated to management in order to assist decision making.
Aside from the utility of finding optimum solutions, the logical thinking demanded by mathematical modeling facilitates learning about the situation of interest. Indeed, for many management scientists, finding an optimum solution is secondary to the knowledge gained through the process of modeling a problem. The reason is that although the model might find an optimum result, this result is based only upon what was inputted in the model. A wide array of extraneous variables, not easily amenable to mathematical modeling, will ultimately affect the implementation of the model’s recommendations in ways that the model cannot show. As such, what is optimum in a model should actually be interpreted as a good approximation of what will happen in a realworld implementation.
The challenge of accounting for extraneous variables, and the perceived value of learning about situations, has led management science to develop approaches for dealing with unquantifiable uncertainty, intimidating complexity, and requisite negotiation. Known as Problem Structuring Methods, their roots lie in psychology, choice theory, game theory, and systems thinking. They rely less on applied mathematics and more on an effective integration of qualitative and quantitative analyses. A good example of this integration can be found in Strategic Options Development and Analysis (SODA). This is a particular cognitive mapping approach, inspired by the psychological theory of personal constructs, whereby the model design renders the qualitative input amenable to the analytical tools of graph theory. Another leading method is Soft Systems Methodology (SSM). This promotes a particularly rigorous learning process that helps decision makers plan for the future systemically, that is to say, in a holistic manner. An effective qualitative substitute for probabilistic decision theory can be found in a method known as the Strategic Choice Approach (SCA).
Contrary to the purely quantitative models for which management science is famous, problem structuring methods have developed especially for dealing with situations whose structure is not readily manipulated within the bounds of mathematics. Problems and opportunities in such situations are more the product of human perception than of hard data. For this reason, the methods rely less on algebra, probabilities, and data collection, and more on diagrammatic representation, scenario development, and the clarification of managerial perceptions. Not unlike the challenges of strategic planning—as opposed to the resolution of operational problems—the methods require the active involvement of decision makers in a joint modeling effort with the structuring expert(s) or facilitator(s). For this reason, the process and content of the methods are more readily transparent than the abstract symbolism of mathematical models.
- Hans G. Daellenbach and Robert L. Flood, The Informed Student Guide to Management Science (Thomson, 2002);
- Raj Nigam, “Structuring and Sustaining Excellence in Management Science at Merrill Lynch,” Interfaces (v.38/3, 2008);
- Jonathan Rosenhead and John Mingers, eds., Rational Analysis for a Problematic World Revisited: Problem Structuring Methods for Complexity, Uncertainty and Conflict (Wiley, 2001);
- Terry Williams, Management Science in Practice (Wiley, 2008);
- Wayne L. Winston et al., Practical Management Science (SouthWestern Cengage, 2009).
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