Lean manufacturing tools as drivers of social sustainability in the Mexican maquiladora industry

IF 6.7 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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Abstract

Social Sustainability (SOS) is crucial to an industry’s success and depends on various factors. This study presents a structural equation model (SEM) that examines how three lean manufacturing tools, focused on continuous improvement, Kaizen, Gemba, and 5Whys, can enhance SOS. Six hypotheses were proposed and validated using information from 411 responses to a survey on the Mexican maquiladora industry, and the partial least squares approach was used to test them. Subsequently, the system dynamics technique was applied to simulate different scenarios and determine the optimum SOS level based on lean manufacturing tools. The findings indicate that only Kaizen can achieve 100 % implementation within less than five years, whereas the other variables require a more extended period. Specifically, the SOS reached 100 % after 10.25 years, and this period is considered too long. Companies should periodically investigate the new needs of employees to improve their SOS. This study is the first to combine SEM and SD to assess the impact of Kaizen, Gemba, and 5Whys as LM Social Sustainability tools, offering a new perspective to achieve sustainable industrial practices. Companies should periodically investigate new employees’ needs to improve their SOS.

精益生产工具是墨西哥加工业社会可持续性的推动力
社会可持续性(SOS)对一个行业的成功至关重要,并取决于各种因素。本研究提出了一个结构方程模型 (SEM),探讨了以持续改进为重点的三种精益生产工具--Kaizen、Gemba 和 5Whys 如何增强 SOS。通过对墨西哥出口加工业调查的 411 份反馈信息,提出并验证了六个假设,并采用偏最小二乘法对其进行了检验。随后,应用系统动力学技术模拟了不同的情景,并确定了基于精益生产工具的最佳 SOS 水平。研究结果表明,只有 Kaizen 可以在不到五年的时间内实现 100% 的实施率,而其他变量则需要更长的时间。具体来说,SOS 在 10.25 年后才达到 100%,这个时间太长了。企业应定期调查员工的新需求,以改进其 SOS。本研究首次将 SEM 和 SD 结合起来,评估作为 LM 社会可持续发展工具的 Kaizen、Gemba 和 5Whys 的影响,为实现可持续工业实践提供了一个新的视角。企业应定期调查新员工的需求,以改善他们的 SOS。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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