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Smart supply chain risk management - a conceptual framework

In: Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 23

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  • Schlüter, Florian
  • Henke, Michael

Abstract

Screening existing literature on Supply Chain Risk Management (SCRM) shows that only sporadic attention is paid on real data driven SCRM. Most tools and approaches lead to an expert knowledge based SCRM. Due to the arising topic of digitalization in supply chains, leading to Industry 4.0 (I4.0), there is huge potential in building a data driven, smart SCRM. To speed up research in this direction it is worthwhile to define a new research framework giving direction. To create a consistent framework and define smart SCRM in more detail a literature review will take place to select appropriate dimensions like SCRM phases, readiness stages of Digitalization/ I4.0 and SC perspectives describing the degree of SC collaboration. Afterwards the SCRM and I4.0 dimensions will be put into focus describing what impact I4.0 will have on SCRM leading to future requirements. The new framework serves as a basis for future SSCRM research. It helps to categorize research projects through multiple dimensions and to identify potential research gaps. The developed SSCRM requirements framework is a practical tool guiding the requirement specification when designing a company specific SSCRM system.

Suggested Citation

  • Schlüter, Florian & Henke, Michael, 2017. "Smart supply chain risk management - a conceptual framework," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg Inter, volume 23, pages 361-380, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:209317
    DOI: 10.15480/882.1466
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    References listed on IDEAS

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    1. Tang, Christopher & Tomlin, Brian, 2008. "The power of flexibility for mitigating supply chain risks," International Journal of Production Economics, Elsevier, vol. 116(1), pages 12-27, November.
    2. Tang, Ou & Nurmaya Musa, S., 2011. "Identifying risk issues and research advancements in supply chain risk management," International Journal of Production Economics, Elsevier, vol. 133(1), pages 25-34, September.
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    Cited by:

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    2. Meike Schroeder & Sebastian Lodemann, 2021. "A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management," Logistics, MDPI, vol. 5(3), pages 1-17, September.

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