An Empirical Study of the Implementation of an Integrated Ergo-Green-Lean Framework: A Case Study
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Published:2023-06-26
Issue:13
Volume:15
Page:10138
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ISSN:2071-1050
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Container-title:Sustainability
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language:en
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Short-container-title:Sustainability
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.
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference59 articles.
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