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Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research

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  • Bin Shen

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, P. R. China)

  • Hau-Ling Chan

    (Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

Abstract

Sharing forecast information helps supply chain parties to better match demand and supply. The extant literature has shown that sharing forecast information improves supply chain performance. In the big data era, supply chain managers have the ability to deal with a massive amount of data by big data technologies and analytics. Big data technologies and analytics provide more accurate forecast information and give an opportunity to transform business models. In this paper, a comprehensive review on forecast information sharing for managing supply chain in the big data era is conducted. The value and obstacles of sharing forecast information are discussed. Given the sufficient data, the appropriate approaches of analyzing and sharing forecast information are highlighted. Insights on the current state of knowledge in each respective area are discussed and some associated pertinent challenges are explored. Inspired by various timely and important issues, future research directions are suggested.

Suggested Citation

  • Bin Shen & Hau-Ling Chan, 2017. "Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-26, February.
    • Handle: RePEc:wsi:apjorx:v:34:y:2017:i:01:n:s0217595917400012
    DOI: 10.1142/S0217595917400012
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    7. Zhong, Qinjia & Wang, Jianjun & Zou, Zongbao & Lai, Xiaofan, 2023. "The incentives for information sharing in online retail platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    8. Shen, Bin & Xu, Xiaoyan & Guo, Shu, 2019. "The impacts of logistics services on short life cycle products in a global supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 153-167.
    9. Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Supply chain analytics implementation: A TOE perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 411-434, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    10. Zongbao Zou & Fan Wang & Xiaofan Lai & Jingxian Hong, 2019. "How Does Licensing Remanufacturing Affect the Supply Chain Considering Customer Environmental Awareness?," Sustainability, MDPI, vol. 11(7), pages 1-23, March.
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    12. Niu, Baozhuang & Xu, Haotao & Dai, Zhipeng, 2022. "Check Only Once? Health Information Exchange between Competing Private Hospitals," Omega, Elsevier, vol. 107(C).
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    15. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    16. Shoukohyar, Sajjad & Seddigh, Mohammad Reza, 2020. "Uncovering the dark and bright sides of implementing collaborative forecasting throughout sustainable supply chains: An exploratory approach," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    17. Niu, Baozhuang & Dai, Zhipeng & Zhuo, Xiaopo, 2019. "Co-opetition effect of promised-delivery-time sensitive demand on air cargo carriers’ big data investment and demand signal sharing decisions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 29-44.

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