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Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect

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  • Erik Hofmann

Abstract

The bullwhip effect is causing inefficiencies in today’s supply chains. This study deals with the potential of big data on the improvement of the various supply chain processes. The aim of this paper is to elaborate which characteristic of big data (lever) has the greatest potential to mitigate the bullwhip effect. From previous research, starting points for big data applications are derived. By using an existing system dynamics model, the big data levers ‘velocity’, ‘volume’ and ‘variety’ are transferred into a simulation. Overall, positive impacts of all the big data levers are elaborated. Findings suggest that the data property ‘velocity’ relatively bears the greatest potential to enhance performance. The results of this research will help in justifying the application of big data in supply chain management. The paper contributes to the literature by operationalising big data in the control engineering analyses.

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  • Erik Hofmann, 2017. "Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5108-5126, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:17:p:5108-5126
    DOI: 10.1080/00207543.2015.1061222
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    8. Huttunen, Henri & Seppälä, Timo & Lähteenmäki, Ilkka & Mattila, Juri, 2019. "What Are the Benefits of Data Sharing? Uniting Supply Chain and Platform Economy Perspectives," ETLA Reports 93, The Research Institute of the Finnish Economy.
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    14. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    15. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
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    18. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    19. Liu, Weihua & Wei, Shuang & Li, Kevin W. & Long, Shangsong, 2022. "Supplier participation in digital transformation of a two-echelon supply chain: Monetary and symbolic incentives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    20. Alberto Bertello & Alberto Ferraris & Stefano Bresciani & Paola Bernardi, 2021. "Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1035-1055, December.
    21. Antonello Cammarano & Vincenzo Varriale & Francesca Michelino & Mauro Caputo, 2023. "Blockchain as enabling factor for implementing RFID and IoT technologies in VMI: a simulation on the Parmigiano Reggiano supply chain," Operations Management Research, Springer, vol. 16(2), pages 726-754, June.
    22. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    23. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    24. Yaping Zhao & Zelong Yi, 2021. "Pricing of a Three-Stage Supply Chain with a Big Data Company," SN Operations Research Forum, Springer, vol. 2(4), pages 1-19, December.

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