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Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study

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  • Tino T. Herden

    (Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany)

  • Steffen Bunzel

    (Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany)

Abstract

While Big Data and Analytics are arguably rising stars of competitive advantage, their application is often presented and investigated as an overall approach. A plethora of methods and technologies combined with a variety of objectives creates a barrier for managers to decide how to act, while researchers investigating the impact of Analytics oftentimes neglect this complexity when generalizing their results. Based on a cluster analysis applied to 46 case studies of Supply Chain Analytics (SCA) we propose 6 archetypes of initiatives in SCA to provide orientation for managers as means to overcome barriers and build competitive advantage. Further, the derived archetypes present a distinction of SCA for researchers seeking to investigate the effects of SCA on organizational performance.

Suggested Citation

  • Tino T. Herden & Steffen Bunzel, 2018. "Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study," Logistics, MDPI, vol. 2(2), pages 1-20, May.
    • Handle: RePEc:gam:jlogis:v:2:y:2018:i:2:p:10-:d:145192
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    References listed on IDEAS

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    5. Buttermann, Garry & Germain, Richard & Iyer, Karthik N.S., 2008. "Contingency theory "fit" as gestalt: An application to supply chain management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(6), pages 955-969, November.
    6. Gunasekaran, A. & Patel, C. & McGaughey, Ronald E., 2004. "A framework for supply chain performance measurement," International Journal of Production Economics, Elsevier, vol. 87(3), pages 333-347, February.
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    Cited by:

    1. Elena Barzizza & Nicolò Biasetton & Riccardo Ceccato & Luigi Salmaso, 2023. "Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review," Stats, MDPI, vol. 6(2), pages 1-21, May.
    2. Tino T. Herden & Benjamin Nitsche & Benno Gerlach, 2020. "Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations," Logistics, MDPI, vol. 4(1), pages 1-27, February.

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