Demand Forecast Information Sharing in Low-Carbon Supply Chains

Yanjie Wang, Qinpeng Wang () and Jingao Shi
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Yanjie Wang: Research Center for Green Development of Great Wall Cultural Economic Belt, Hebei University of Economics and Business, Shijiazhuang 050061, China
Qinpeng Wang: School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China
Jingao Shi: School of Management Science and Information Engineering, Hebei University of Economics and Business, Shijiazhuang 050061, China

Sustainability, 2024, vol. 16, issue 20, 1-15

Abstract: In view of the crucial role of consumer data concerning low-carbon preferences, a low-carbon supply chain was established, encompassing one manufacturer and one retailer. Both entities employed technological tools to predict consumer demand. This study assessed the profitability of supply chain participants under two strategies, “make-to-order” and “make-to-stock”, considering scenarios with and without demand forecast sharing information. Furthermore, we investigated how factors such as demand variability, forecast biases from the manufacturer and the retailer, and the forecast correlation coefficient affect the performance of the supply chain and the benefits of sharing information. Our findings indicate that the strategies of supply chain members remain unaffected by the chosen production models and that information sharing proves advantageous for the manufacturer and the retailer. Especially for manufacturers, profits in a “make-to-order” scenario surpass those in a “make-to-stock” scenario. Our numerical analysis showed that profits for the manufacturer and the retailer were consistently higher in scenarios where information was shared than in cases where it was not for “make-to-order” and “make-to-stock” strategies.

Keywords: supply chain management; carbon emission reduction; consumer low-carbon preference; information sharing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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