Pricing strategies of dual-channel green supply chain considering Big Data information inputs

• Published in: Soft computing (Berlin, Germany) Germany), 2022-03, Vol.26 (6), p.2981-2999
• Main Authors: Liu, Pan, Zhang, Feng-jie
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Intelligence; Control; Engineering; Mathematical Logic and Foundations; Mechatronics; Robotics; Soft Computing in Decision Making and in Modeling in Economics
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-021-06611-6

In the Big Data environment, when green manufacturers invest in the green production technology, to satisfy consumer demand timely and accurately, they may begin to gain consumer performance information (hereafter, CBDI) to design and produce product. However, these will go up their extra costs. Meanwhile, for a green manufacturer who sells the green product through the online channel and the offline channel, the expression of its market demand needs to rethink in the new environment. In these conditions, for a dual-channel green supply chain (hereafter, DGSC), chain members pay more attention on the pricing problems considering the inputs of CBDI and greening R&D. Hence, to resolve this question, a DGSC a green manufacturer selling by the online channel and with one retailer selling by the offline channel was chosen. Afterward, the demand function of the DGSC was revised, and we analyzed the profits models and its pricing rules in the proposed four common cost-sharing models. Results indicate that whether the retailer bears the CBDI costs or the greening R&D costs, the retailer will not change its retail price. If the retailer can bear some CBDI costs, the alteration tendencies of the best wholesale price are related to the cost-sharing parameter.