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Title: Forecast information sharing for managing supply chains in the big data era : recent development and future research
Authors: Shen, B
Chan, HL 
Keywords: Forecasting
Forecast information sharing
Big data
Supply chain
Issue Date: 2017
Publisher: World Scientific
Source: Asia-Pacific journal of operational research, 2017, v. 34, no. 1, 1740001 How to cite?
Journal: Asia-Pacific journal of operational research 
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.
ISSN: 0217-5959
EISSN: 1793-7019
DOI: 10.1142/S0217595917400012
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