Ordering Strategies That Consider Positive and Negative Incentives for Designers Under Clothing Crowdsourcing

Yipeng Li, Shu Zhang () and Kaifu Yuan
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Yipeng Li: Zhongnan University of Economics and Law, School of Information and Safety Engineering
Shu Zhang: Zhongnan University of Economics and Law, School of Information and Safety Engineering
Kaifu Yuan: Guizhou University of Finance and Economics, School of Business Administration

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 910-924 from Springer

Abstract: Abstract The combination of clothing industry and crowdsourcing supply chain (CSC) is becoming a research hotspot. For the clothing industry, “crowdsourcing” is introduced into the traditional supply chain operation. Firstly, based on the newsboy model, a basic ordering model (BM) is constructed. Then the optimal pricing and ordering decisions under decentralized decision-making and centralized decision-making are compared and analyzed. Furthermore, in order to continuously obtain high-quality creativity, considering the participation incentive of crowdsourcing designers and enterprise inventory risk avoidance, a positive and negative incentive ordering model (PNI) for designers is proposed. Through mathematical derivation and theorem proof, the optimal utility and optimal order quantity of supply chain and crowdsourcing designers are obtained. Finally, the optimal pricing order problem of the two policy models is solved through data simulation, and further sensitivity analysis of the fabric consumption rate, order quantity and other parameters are carried out. It is concluded that centralised decision-making under BM is superior to decentralised decision-making, and PNI has some practical value in improving supply chain profitability and expanding order volumes for apparel retailers.

Keywords: Clothing industry; Crowdsourcing supply chain; Ordering decision; Positive and negative incentives; Risk sharing (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-256-9_90

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