A perspective on applications of in-memory analytics in supply chain management

Big data, advanced analytics, and in-memory database technology are on the agenda of top management since they are seen as key enablers for enhanced business decision-making. In this paper, we provide a comprehensive perspective on applications of in-memory analytics in the field of supply chain man...

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Bibliographic Details
Published in:Decision Support Systems Vol. 76; pp. 45 - 52
Main Authors: Hahn, G.J., Packowski, J.
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.08.2015
Elsevier Sequoia S.A
Subjects:
Advanced planning
Big Data
Business
Business analytics
Business intelligence
Decision making
Decision support systems
Effectiveness
Empirical analysis
In-memory database systems
Information technology
Management
Studies
Supply chain management
Supply chains
Supply chain management
In-memory database systems
Business intelligence
Business analytics
Advanced planning
ISSN:0167-9236, 1873-5797
Online Access:Get full text
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Summary:Big data, advanced analytics, and in-memory database technology are on the agenda of top management since they are seen as key enablers for enhanced business decision-making. In this paper, we provide a comprehensive perspective on applications of in-memory analytics in the field of supply chain management (SCM) that use the aforementioned concepts. Our contribution is threefold: First, we develop a top-down framework to position in-memory analytics applications against extant IT systems in SCM. Second, we conduct a bottom-up categorization of 41 in-memory analytics applications in SCM to provide supporting empirical evidence of the efficacy of the framework. Third, by contrasting top-down and bottom-up perspectives we derive implications for research and industrial practice. •In-memory analytics applications in SCM can be structured along four use cases.•Real-time analytics is the predominant focus of emerging in-memory applications.•Integrated data models further support functional integration in adjacent domains.•Emerging applications do not substitute but complement current APS systems.•A stochastic planning approach in APS systems still remains open for research.
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ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2015.01.003