Optimizing the material distribution method for automotive electronic switch production: A rigid and flexible approach

Author:

Chen Yujin1ORCID,Cheng Mengmeng2ORCID,Wu Yaoguang1,Zhu Jihong13,Meng Yanmei1

Affiliation:

1. School of Mechanical Engineering, Guangxi University, Nanning, China

2. School of Computer and Electronic Information, Guangxi University, Nanning, China

3. Department of Precision Instrument, Tsinghua University, Beijing, China

Abstract

The lifecycle of automotive electronic switches is relatively short. The products exhibit a high level of customization. The assembly production demonstrates characteristics of high variety in small batch sizes and frequent production changeovers. Traditional delivery methods often result in issues such as excessive line-side inventory, residual material accumulation, and material management disorder. In this article, a reverse planning approach is adopted to logically analyze from the material demand side to the supply side. By integrating the concepts of push and pull production control, a just-in-time delivery sequencing model is designed. Furthermore, the model was validated based on production data from Company A. The aim is to reduce line-side inventory, improve delivery accuracy, shift from ensuring material supply to efficient supply, and enhance inventory turnover. The purpose of improvement is to achieve a 20% reduction in floor space occupied by the assembly workshop at the same production level, decrease line-side inventory (including raw materials, work-in-progress, and finished goods) by 30%, increase inventory turnover by 20%, reduce the working hours required for logistics personnel by 30%, and improve the accuracy of material deployment (including quantity and part number) from 92% to 99%.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Trajectory tracking of binocular vision system for picking robot based on fast non-singular terminal sliding mode control;Transactions of the Institute of Measurement and Control;2024-04-13

2. Optimizing production logistics through advanced machine learning techniques: A study on resource allocation for small-batch and multi-variety challenges;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2024-03-11

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