Decision Science

Decision science (ds) originated in Great Britain during World War II, when mathematical or quantitative approaches were used to solve logistic problems during military operations. Since then, it has evolved to be applicable to the management of all aspects of a system, product, or service. It is now considered an important input to decision-making in a wide variety of applications in business, industry, and government. The growing complexity of management since the 1940s has necessitated the development of sophisticated mathematical techniques for planning and decisionmaking. DS involves the quantitative evaluation of alternative policies, plans, and decisions and has become centered in the structured decision-making process cycle. It may also be called Operations Research (OR, American), Operational Research (OR, United Kingdom), Systems Science, Mathematical Modeling, Industrial Engineering, Critical Systems Strategic Thinking, Success Science (SS), and Systems Analysis and Design.

The study of DS involves the application of mathematical methods and tools for solving problems relating to the allocation of scarce resources subject to certain constraints. It contributes to the understanding of human decision-making as well as the development of methods and tools of analysis. Usually the problems deal with determining the least cost or greatest profit, subject to constraints such as some required quantities, capacity to manufacture or store, and available resources over a large number of variables.

The fundamental part of DS modeling is the “systems approach” to problem solving that indicates that the context of organizational problems is as important as the stated problem. The modeling process helps to improve operations through the use of scientific methods and the development of specialized techniques. It often involves defining a problem, collecting information, making decisions based on them, taking action, monitoring and evaluating the results of the implementation, and checking for new problems iteratively. There are two approaches to the decision process—sequential model or nonsequential model. The sequential model requires following certain linear steps, while the nonsequential model has certain phases that have a circular relationship. Orville G. Brim and others proposed one of the early sequential models in Personality and Decision Processes, Studies in the Social Psychology of Thinking (1962). They proposed following six steps as part of the methodology: Identification of the problem, obtaining necessary information, production of possible solutions, evaluation of such solutions, selection of a strategy for performance, and implementation of the decision.

However, a more realistic model should allow the various parts of the decision process to vary in order. One of the most accepted nonsequential models was proposed by Mintzberg, Raisinghani, and Théorêt in 1976 in “The Structure of ‘Unstructured’ Decision Processes,” Administrative Sciences Quarterly. They identified the decision process to have three phases: identification, development, and selection. In this model, one may cycle through one or all of the phases in any order until an acceptable solution is found.