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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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    1. Yao, Yuliang & Dresner, Martin, 2008. "The inventory value of information sharing, continuous replenishment, and vendor-managed inventory," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(3), pages 361-378, May.
    2. Disney, Stephen M. & Towill, Denis R. & Warburton, Roger D.H., 2006. "On the equivalence of control theoretic, differential, and difference equation approaches to modeling supply chains," International Journal of Production Economics, Elsevier, vol. 101(1), pages 194-208, May.
    3. Adamopoulos, George I. & Pappis, Costas P., 1996. "A fuzzy-linguistic approach to a multi-criteria sequencing problem," European Journal of Operational Research, Elsevier, vol. 92(3), pages 628-636, August.
    4. Sila Çetinkaya & Chung-Yee Lee, 2000. "Stock Replenishment and Shipment Scheduling for Vendor-Managed Inventory Systems," Management Science, INFORMS, vol. 46(2), pages 217-232, February.
    5. Gaalman, Gerard & Disney, Stephen M., 2009. "On bullwhip in a family of order-up-to policies with ARMA(2,2) demand and arbitrary lead-times," International Journal of Production Economics, Elsevier, vol. 121(2), pages 454-463, October.
    6. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    7. Srinivasan Raghunathan & Arthur B. Yeh, 2001. "Beyond EDI: Impact of Continuous Replenishment Program (CRP) Between a Manufacturer and Its Retailers," Information Systems Research, INFORMS, vol. 12(4), pages 406-419, December.
    8. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2004. "The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective," European Journal of Operational Research, Elsevier, vol. 153(3), pages 727-750, March.
    9. Mason-Jones, Rachel & Towill, Denis R., 1999. "Total cycle time compression and the agile supply chain," International Journal of Production Economics, Elsevier, vol. 62(1-2), pages 61-73, May.
    10. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    11. Wang, Reay-Chen & Chuu, Shian-Jong, 2004. "Group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system," European Journal of Operational Research, Elsevier, vol. 154(3), pages 563-572, May.
    12. Gérard P. Cachon & Martin A. Lariviere, 2001. "Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain," Management Science, INFORMS, vol. 47(5), pages 629-646, May.
    13. Zhou, Li & Disney, Stephen & Towill, Denis R., 2010. "A pragmatic approach to the design of bullwhip controllers," International Journal of Production Economics, Elsevier, vol. 128(2), pages 556-568, December.
    14. Machuca, José A. D. & Barajas, Rafael P., 2004. "The impact of electronic data interchange on reducing bullwhip effect and supply chain inventory costs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 40(3), pages 209-228, May.
    15. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2003. "Measuring and avoiding the bullwhip effect: A control theoretic approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 567-590, June.
    16. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
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    15. Lin, Junyi & Naim, Mohamed M. & Spiegler, Virginia L.M., 2020. "Delivery time dynamics in an assemble-to-order inventory and order based production control system," International Journal of Production Economics, Elsevier, vol. 223(C).
    16. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    17. 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.
    18. Lineth Rodríguez & Mihalis Giannakis & Catherine da Cunha, 2018. "Investigating the Enablers of Big Data Analytics on Sustainable Supply Chain," Post-Print hal-01982533, HAL.
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    20. Xu, Jinou & Pero, Margherita & Fabbri, Margherita, 2023. "Unfolding the link between big data analytics and supply chain planning," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    21. Kristoffersen, Eivind & Blomsma, Fenna & Mikalef, Patrick & Li, Jingyue, 2020. "The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies," Journal of Business Research, Elsevier, vol. 120(C), pages 241-261.
    22. 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).
    23. 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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