An Empirical Study of the Implementation of an Integrated Ergo-Green-Lean Framework: A Case Study

Author:

Kanan Mohammad1ORCID,Dilshad Ansa Rida2,Zahoor Sadaf2,Hussain Amjad3,Habib Muhammad Salman2,Mehmood Amjad2ORCID,Abusaq Zaher1,Hamdan Allam4,Asad Jihad5ORCID

Affiliation:

1. Industrial Engineering Department, Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Saudi Arabia

2. Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore 54000, Pakistan

3. Department of Mechanical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan

4. Department of Accounting and Economics, College of Business and Finance, Ahlia University, Manama P.O. Box 10878, Bahrain

5. Department of Physics, Faculty of Applied Sciences, Palestine Technical University-Kadoorie, Tulkarm P305, Palestine

Abstract

The implementation of lean manufacturing to increase productivity often neglects the impact on the environment and the well-being of employees. This can result in negative consequences such as environmental harm and poor employee satisfaction. To address this issue, an integrated ergo-green-lean conceptual model was developed in the literature. However, no case study has been conducted to support this model. Therefore, this research aims to investigate the practical outcomes of implementing the integrated framework in an automobile parts industry. Key performance indicators (KPIs) were identified, including ergonomic risk score, job satisfaction, carbon footprint emission both from direct energy consumption and material wastage, cycle time, lead time, die setup time, and rejection rate. Various assessment techniques were employed, such as the rapid entire body assessment (REBA) with the Standard Nordic Questionnaire (SNQ), job stress survey, carbon footprint analysis (CFA), and value stream mapping (VSM) to evaluate the KPIs at the pre- and post-intervention phases. The results demonstrate significant improvements in job satisfaction (49%), improved REBA score of 10 postures with very high risk numbers by 100%, a 30.3% and 19.2% decrease in carbon emissions from energy consumption and material wastage, respectively, a 45% decrease in rejection rate at the customer end, a 32.5% decrease in in-house rejection rate, a 15.5% decrease in cycle time, a 34.9% decrease in lead time, and a 21% decrease in die setup time. A Python regression model utilizing sklearn, pandas, and numpy was created to assess the relationship between process improvement and the chosen KPIs.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference59 articles.

Cited by 4 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

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3