Sustainable supply chain distribution model of fashion market based on improved ant colony algorithm

Yue, Linhong and Zhong, Zoe Ziqi ORCID: 0000-0002-3919-9999 (2023) Sustainable supply chain distribution model of fashion market based on improved ant colony algorithm. In: Proceedings - 2023 International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023. Proceedings - 2023 International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023. IEEE, 289 - 293. ISBN 9798350341706

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Abstract

The fashion clothing brand marketing cycle is notably brief, and its supply chain system is comprehensive, significantly reducing the time from clothing design to production. This agility enables swift responses to consumer demands, aligning with the fast-paced consumer market. This paper delves into the intricacies of marketing and supply chain management within the fashion clothing brand sector. It introduces an optimized management model for fashion marketing supply chains, built upon the foundation of Particle Swarm Optimization (PSO). Simulation results demonstrate distinct characteristics among different decision methods. Decision method 1 exhibits notably higher inventory levels, which largely contribute to its increased cost when compared to decision methods 2 and 3. Decision method 2 presents relatively stable inventory levels for IMI (Item-Managed Inventory) and IPI (Item-Produced Inventory), with average inventory levels of 4.56 and 4.86, respectively. In contrast, decision method 3 achieves even lower inventory levels for IMI and IPI, with averages of 1.52 and 1.42, respectively. This slight increase in IMI and IPI inventory under decision method 2 underscores the effectiveness of decision method 3 in maintaining lower inventory levels. The model proposed in this paper embodies sound supply chain management principles and exhibits practicality and operability. To realize swift fund withdrawal, mature enterprises must efficiently manage logistics, information flow, and capital flow within this model.

Item Type:	Book Section
Additional Information:	© 2023 IEEE.
Divisions:	Management
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management H Social Sciences > HF Commerce Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	27 Aug 2024 15:06
Last Modified:	20 Dec 2024 00:20
URI:	http://eprints.lse.ac.uk/id/eprint/124684

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