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Operations research (British English: operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a discipline that deals with the development and application of analytical methods to improve decision-making. [1]
This is an accepted version of this page This is the latest accepted revision, reviewed on 5 January 2025. Set of software development practices DevOps is a methodology integrating and automating the work of software development (Dev) and information technology operations (Ops). It serves as a means for improving and shortening the systems development life cycle. DevOps is complementary to ...
Operationalization is the scientific practice of operational definition, where even the most basic concepts are defined through the operations by which we measure them. The practice originated in the field of physics with the philosophy of science book The Logic of Modern Physics (1927), by Percy Williams Bridgman, whose methodological position is called "operationalism".
Most of all, it is an engineering practice that leverages three contributing disciplines: machine learning, software engineering (especially DevOps), and data engineering. MLOps is aimed at productionizing machine learning systems by bridging the gap between development (Dev) and operations (Ops).
However, many theorists emphasize the differences between the terms "method" and "methodology". [ 1 ] [ 7 ] [ 2 ] [ 11 ] In this regard, methodology may be defined as "the study or description of methods" or as "the analysis of the principles of methods, rules, and postulates employed by a discipline".
Research and development are very difficult to manage, since the defining feature of research is that the researchers do not know in advance exactly how to accomplish the desired result. As a result, "higher R&D spending does not guarantee more creativity, higher profit or a greater market share". [ 14 ]
DataOps is a set of practices, processes and technologies that combines an integrated and process-oriented perspective on data with automation and methods from agile software engineering to improve quality, speed, and collaboration and promote a culture of continuous improvement in the area of data analytics. [1]
In this example a company should prefer product B's risk and payoffs under realistic risk preference coefficients. Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine).