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

Bin Shen () and Hau-Ling Chan
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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

Asia-Pacific Journal of Operational Research (APJOR), 2017, vol. 34, issue 01, 1-26

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.

Keywords: Forecasting; forecast information sharing; big data; supply chain (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (17)

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DOI: 10.1142/S0217595917400012

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