Back in business: operations research in support of big data analytics for operations and supply chain management

Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment...

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Bibliographic Details
Published in:Annals of operations research 2018-11, Vol.270 (1-2), p.201-211
Main Authors: Hazen, Benjamin T., Skipper, Joseph B., Boone, Christopher A., Hill, Raymond R.
Format: Article
Language:English
Subjects:
Analysis
Analytics
Big Data
Big Data Analytics in Operations & Supply Chain Management
Business and Management
Combinatorics
Data analysis
Data management
Data mining
Decision making
Logistics
Management research
Mathematical analysis
Methods
Misalignment
Operations research
Operations Research/Decision Theory
Scholars
Studies
Supply chain management
Supply chains
Theory of Computation
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Summary:Few topics have generated more discourse in recent years than big data analytics. Given their knowledge of analytical and mathematical methods, operations research (OR) scholars would seem well poised to take a lead role in this discussion. Unfortunately, some have suggested there is a misalignment between the work of OR scholars and the needs of practicing managers, especially those in the field of operations and supply chain management where data-driven decision-making is a key component of most job descriptions. In this paper, we attempt to address this misalignment. We examine both applied and scholarly applications of OR-based big data analytical tools and techniques within an operations and supply chain management context to highlight their future potential in this domain. This paper contributes by providing suggestions for scholars, educators, and practitioners that aid to illustrate how OR can be instrumental in solving big data analytics problems in support of operations and supply chain management.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-016-2226-0