Management science

For the academic journal, see Management Science (journal).

Management science (or managerial science) is a wide and interdisciplinary study of solving complex problems and making strategic decisions as it pertains to institutions, corporations, governments and other types of organizational entities. It is closely related to management, economics, business, engineering, management consulting, and other fields. It uses various scientific research-based principles, strategies, and analytical methods including mathematical modeling, statistics and numerical algorithms and aims to improve an organization's ability to enact rational and accurate management decisions by arriving at optimal or near optimal solutions to complex decision problems. ^{[1] : 113}

Management science looks to help businesses achieve goals using a number of scientific methods. The field was initially an outgrowth of applied mathematics, where early challenges were problems relating to the optimization of systems which could be modeled linearly, i.e., determining the optima (maximum value of profit, assembly line performance, crop yield, bandwidth, etc. or minimum of loss, risk, costs, etc.) of some objective function. Today, the discipline of management science may encompass a diverse range of managerial and organizational activity as it regards to a problem which is structured in mathematical or other quantitative form in order to derive managerially relevant insights and solutions. ^{[2] [3]}

Overview

Management science is concerned with a number of areas of study:

• 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. ^[4]
• Designing and developing new and better models of organizational excellence.
• Helping to improve, stabilize or otherwise manage profit margins in enterprises.^{[ citation needed ]}

Management science research can be done on three levels: ^[5]

• 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, experimenting 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 in any other engineering and economics disciplines, strives 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 and science-based techniques to inform and improve decisions of all kinds. 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. The norm for scholars in management science is to focus their work in a certain area or subfield of management like public administration, finance, calculus, information and so forth. ^[6]

History

Although management science as it exists now covers a myriad of topics having to do with coming up with solutions that increase the efficiency of a business, it was not even a field of study in the not too distant past. There are a number of businessmen and management specialists who can receive credit for the creation of the idea of management science. Most commonly, however, the founder of the field is considered to be Frederick Winslow Taylor in the early 20th century. Likewise, administration expert Luther Gulick and management expert Peter Drucker both had an impact on the development of management science in the 1930s and 1940s. Drucker is quoted as having said that, "the purpose of the corporation is to be economically efficient." This thought process is foundational to management science. Even before the influence of these men, there was Louis Brandeis who became known as "the people's lawyer". In 1910, Brandeis was the creator of a new business approach which he coined as "scientific management", a term that is often falsely attributed to the aforementioned Frederick Winslow Taylor. ^[7]

These men represent some of the earliest ideas of management science at its conception. After the idea was born, it was further explored around the time of World War II. It was at this time that management science became more than an idea and was put into practice. This sort of experimentation was essential to the development of the field as it is known today. ^[8]

The origins of management science can be traced to operations research, which became influential during World War II when the Allied forces recruited scientists of various disciplines to assist with military operations. In these early applications, the scientists used simple mathematical models to make efficient use of limited technologies and resources. The application of these models to the corporate sector became known as management science. ^[9]

In 1967 Stafford Beer characterized the field of management science as "the business use of operations research". ^[10]

Theory

Some of the fields that management science involves include:

• Contract theory
• Data mining
• Decision analysis
• Engineering
• Forecasting
• Marketing
• Finance
• Operations
• Game theory
• Industrial engineering
• Logistics
• Management consulting
• Mathematical modeling
• Optimization
• Operational research
• Probability and statistics
• Project management
• Psychology
• Simulation
• Social network / Transportation forecasting models
• Sociology
• Supply chain management

Applications

Management science's applications are diverse allowing the use of it in many fields. ^[11] Below are examples of the applications of management science.

In finance, management science is instrumental in portfolio optimization, risk management, and investment strategies. By employing mathematical models, analysts can assess market trends, optimize asset allocation, and mitigate financial risks, contributing to more informed and strategic decision-making.

In healthcare, management science plays a crucial role in optimizing resource allocation, patient scheduling, and facility management. Mathematical models aid healthcare professionals in streamlining operations, reducing waiting times, and improving overall efficiency in the delivery of care.

Logistics and supply chain management benefit significantly from management science applications. Optimization algorithms assist in route planning, inventory management, and demand forecasting, enhancing the efficiency of the entire supply chain.

In manufacturing, management science supports process optimization, production planning, and quality control. Mathematical models help identify bottlenecks, reduce production costs, and enhance overall productivity.

Furthermore, management science contributes to strategic decision-making in project management, marketing, and human resources. By leveraging quantitative techniques, organizations can make data-driven decisions, allocate resources effectively, and enhance overall performance across diverse functional areas.

In summary, the applications of management science are far-reaching, providing valuable insights and solutions across a spectrum of industries, ultimately fostering more efficient and effective decision-making processes.

See also

• Fayolism
• Institute for Operations Research and the Management Sciences
• John von Neumann Theory Prize
• Managerial economics
• Management engineering
• Management cybernetics
• Innovation management
• Organization studies
• Outline of management

References

1. An Introduction to Management Science: Quantitative Approaches to Decision Making (15 ed.). Boston: Cengage Learning, Inc. 2019. ISBN   978-1-337-40652-9 . Retrieved 14 October 2022.
2. "Tools for Thinking — Modelling in Management Science". Taylor & Francis Online. Retrieved 30 March 2023.
3. "Management Models and Industrial Applications of Linear Programming". Management Science. 4 (1): 38–91. 1957. doi:10.1287/mnsc.4.1.38 . Retrieved 30 March 2023.
4. What is Management Science? Archived 2009-07-25 at the Wayback Machine Lancaster University, 2008. Retrieved 5 June 2008.
5. What is Management Science Research? Archived 2008-11-04 at the Wayback Machine University of Cambridge 2008. Retrieved 5 June 2008.
6. "Sub-disciplines in Management Sciences: Review of Classifications in Polish and Worldwide Research Practice". International Journal of Contemporary Management. 17 (1): 137–156. 2018. doi: 10.4467/24498939IJCM.18.008.8387 . Retrieved 30 March 2023.
7. Bridgman, Stephen Cummings and Todd (2021-11-15). "The Progressive Roots of Management Science". MIT Sloan Management Review. Retrieved 2023-11-18.
8. "Management Science | Encyclopedia.com". www.encyclopedia.com. Retrieved 2023-11-18.
9. What is Management Science? Archived 2008-12-07 at the Wayback Machine The University of Tennessee, 2006. Retrieved 5 June 2008.
10. Stafford Beer (1967). Management Science: The Business Use of Operations Research
11. III, B.W.T (2018). Introduction to Management Science (13th ed.). US: Pearson Education (US). ISBN   9780134731315.

Further reading

• Kenneth R. Baker, Dean H. Kropp (1985). Management Science: An Introduction to the Use of Decision Models
• 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.