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Open AccessFeature PaperArticle

Impact of Information Sharing and Forecast Combination on Fast-Moving-Consumer-Goods Demand Forecast Accuracy

Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, Singapore 138634, Singapore
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Information 2019, 10(8), 260; https://doi.org/10.3390/info10080260
Received: 23 July 2019 / Revised: 13 August 2019 / Accepted: 14 August 2019 / Published: 16 August 2019
(This article belongs to the Special Issue Big Data Research, Development, and Applications––Big Data 2018)
This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model. View Full-Text
Keywords: forecasting; information sharing; fast moving consumer goods; hierarchical reconciliation forecasting; information sharing; fast moving consumer goods; hierarchical reconciliation
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Yang, D.; Zhang, A.N. Impact of Information Sharing and Forecast Combination on Fast-Moving-Consumer-Goods Demand Forecast Accuracy. Information 2019, 10, 260.

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