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The Strategic Analysis of Demand Forecast-Sharing in a Hybrid-Format Online Platform Supply Chain

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Abstract

This paper examines the optimal forecast-sharing strategy in a hybrid-format online platform supply chain where a supplier sells a product through agency format and reselling format provided by a platform retailer who possesses demand forecasts from two channels. Forecast asymmetry and co-opetitive relationship arise between the platform retailer and the supplier, which affect their operational decisions and the supply chain’s performance. To improve supply chain efficiency, we compare different forecast-sharing strategies (i.e., no forecast sharing, sharing a single forecast, and sharing two forecasts), and analyze the effects of co-opetitive parameters on the optimal forecast-sharing strategy. Our analysis shows that forecast sharing is always beneficial to the supplier, and sharing two forecasts is more beneficial than sharing a single forecast. Whereas for the platform retailer and the whole supply chain, forecast sharing is beneficial only under certain conditions, depending on the co-opetitive parameters. The optimal forecast-sharing strategy is the result of a combination of the negative effect of double marginalization in reselling channel and the positive effect of responding pricing to demand uncertainty in agency channel. We illustrate the parameter regions of the platform retailer’s voluntary sharing, contract sharing, and no sharing, and also find that higher channel competition intensity, higher market share of agency channel, and higher commission rate can promote the platform retailer’s voluntary sharing. Our study extends the research scope of demand forecast-sharing and sheds light on the decision-making processes for managing a hybrid-format online platform supply chain.

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Acknowledgments

The authors would like to thank the reviewers for their constructive comments, which helped to improve the quality of this paper. This research was supported in part by National Natural Science Foundation of China under Grant Nos. 72171169 and 71971076.

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Authors and Affiliations

  1. School of Mathematical Sciences, Tiangong University, Tian**, 300387, China

    **g Zhao, Zijun Yin & Guobiao Zhou

Authors
  1. **g Zhao
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  2. Zijun Yin
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  3. Guobiao Zhou
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Corresponding author

Correspondence to **g Zhao.

Additional information

**g Zhao is a professor in School of Mathematical Sciences at Tiangong University, Tian**, China. She received her Ph.D in system engineering from Tian** University in 2011. Her research interests include supply chain management and coordination, decision and countermeasure, information sharing, online platform, competition and cooperation. Her articles have been published by journals such as European Journal of Operational Research, International Journal of Production Economics, International Journal of Production Research, Annals of Operations Research, Applied Mathematical Modelling, Applied Mathematics and Computation.

Zijun Yin is a graduate student in School of Mathematical Sciences at Tiangong University, Tian**, China. Her research interests include online platform and supply chain management.

Guobiao Zhou is a graduate student in School of Mathematical Sciences at Tiangong University, Tian**, China. His research interests include online platform, multichannel strategy and information sharing.

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Zhao, J., Yin, Z. & Zhou, G. The Strategic Analysis of Demand Forecast-Sharing in a Hybrid-Format Online Platform Supply Chain. J. Syst. Sci. Syst. Eng. 33, 281–310 (2024). https://doi.org/10.1007/s11518-024-5596-x

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