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

• 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.

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.