Management Science Essay

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 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.

Bibliography:  

  1. Hans G. Daellenbach and Robert L. Flood, The Informed Student Guide to Management Science (Thomson, 2002);
  2. Raj Nigam, “Structuring and Sustaining Excellence in Management Science at Merrill Lynch,” Interfaces (v.38/3, 2008);
  3. Jonathan Rosenhead and  John Mingers, eds., Rational Analysis for a Problematic World Revisited: Problem Structuring Methods  for Complexity, Uncertainty  and Conflict (Wiley, 2001);
  4. Terry Williams, Management Science in  Practice (Wiley, 2008);
  5. Wayne L. Winston  et al., Practical Management Science (SouthWestern  Cengage, 2009).

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