Management science

Management science (MS), is an interdisciplinary branch of applied mathematics devoted to optimal decision planning, with strong links with economics, business, engineering, and other sciences. It uses various scientific research-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms to improve an organization's ability to enact rational and meaningful management decisions by arriving at optimal or near optimal solutions to complex decision problems. The discipline is typically concerned with determining the maxima (of profit, assembly line performance, crop yield, bandwidth, etc) or minima (of loss, risk, etc.) of some objective function. In short, management sciences help businesses to achieve goals using various scientific methods.

The field was traditionally known as Operations research (OR) in the United States and Canada, or operational research in the United Kingdom. These three terms are often used interchangeably to describe the same field.

Overview

Management science is concerned with a number of different areas of study 1)developing and applying models and concepts that may prove useful in helping to illuminate management issues and solve managerial problems. The models used can often be represented mathematically, but sometimes computer-based, visual or verbal representations are used as well or instead.^[1] 2)designing and developing new and better models of organizational excellence. A leading influence in this area is the work of Dr. Mark Draper which combines insights from the fields of knowledge management, cognitive psychology, leadership training, learning theory, and modern behavioral psychology. Dr. Jim Collins's work at Stanford presents the important scientific facts about how to turn a good organization into a great one. Dr Draper's work focuses more on how to create new powerful and effective organizations.

Management science research can be done on three levels:^[2]

• The fundamental level lies in three mathematical disciplines: Probability, Optimization, and Dynamical systems theory.
• The modeling level is about building models, analyzing them mathematically, gathering and analyzing data, implementing models on computers, solving them, playing with them - all this is part of Management Science research on the modeling level. This level is mainly instrumental, and driven mainly by statistics and econometrics.
• The application level, just as any other engineering and economics' disciplines, has strong aspirations to make a practical impact and be a driver for change in the real world.

The management scientist's mandate is to use rational, systematic, science-based techniques to inform and improve decisions of all kinds. Of course, the techniques of management science are not restricted to business applications but may be applied to military, medical, public administration, charitable groups, political groups or community groups....

History

Its origins can be traced to operations research, which made its debut during World War II when the Allied forces recruited scientists of various disciplines to assist with military operations. In these early applications, the scientists utilized simple mathematical models to make efficient use of limited technologies and resources. The application of these models within the corporate sector became known as Management science.^[3]

In 1967 Stafford Beer characterized the field of management science as "the business use of Operations Research".^[4]

Theory

Some of the fields that are englobed within Management Science include:

as well as many others.

Applications

Applications of management science are abundant in industry as airlines, manufacturing companies, service organizations, military branches, and in government. The range of problems and issues to which management science has contributed insights and solutions is vast. It includes:.^[1]

• scheduling airlines, both planes and crew,
• deciding the appropriate place to site new facilities such as a warehouse or factory,
• managing the flow of water from reservoirs,
• identifying possible future development paths for parts of the telecommunications industry,
• establishing the information needs and appropriate systems to supply them within the health service, and
• identifying and understanding the strategies adopted by companies for their information systems

Management science is also concerned with so-called ”soft-operational analysis”, which concerns methods for strategic planning, strategic decision support, and Problem Structuring Methods (PSM). At this level of abstraction, mathematical modeling and simulation will not suffice. Therefore, during the past 30 years, a number of non-quantified modelling methods have been developed. These include morphological analysis and various forms of influence diagrams.

See also

• Operations Research
• Industrial engineering
• Econometrics
• INFORMS Institute for Operations Research and the Management Sciences
• John von Neumann Prize
• Managerial economics
• Management engineering
• Management Science: A Journal of the Institute for Operations Research and the Management Sciences

References

Further reading

• Kenneth R. Baker, Dean H. Kropp (1985). Management Science: An Introduction to the Use of Decision Models
• Stafford Beer (1967). Management Science: The Business Use of Operations Research
• David Charles Heinze (1982). Management Science: Introductory Concepts and Applications
• Lee J. Krajewski, Howard E. Thompson (1981). "Management Science: Quantitative Methods in Context"
• Thomas W. Knowles (1989). Management science: Building and Using Models
• Kamlesh Mathur, Daniel Solow (1994). Management Science: The Art of Decision Making
• Laurence J. Moore, Sang M. Lee, Bernard W. Taylor (1993). Management Science
• William Thomas Morris (1968). Management Science: A Bayesian Introduction.
• William E. Pinney, Donald B. McWilliams (1987). Management Science: An Introduction to Quantitative Analysis for Management
• Gerald E. Thompson (1982). Management Science: An Introduction to Modern Quantitative Analysis and Decision Making. New York : McGraw-Hill Publishing Co.