Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 하비브 무하마드 살만 | - |
dc.date.accessioned | 2024-05-09T01:26:24Z | - |
dc.date.available | 2024-05-09T01:26:24Z | - |
dc.date.issued | 2023-06-26 | - |
dc.identifier.citation | SUSTAINABILITY, v. 15, NO 13, page. 1-24 | en_US |
dc.identifier.issn | 2071-1050 | en_US |
dc.identifier.uri | https://information.hanyang.ac.kr/#/eds/detail?an=edsdoj.0f28fdc0105a42c79ff3087a4863e967&dbId=edsdoj | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/190228 | - |
dc.description.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 preand 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. | en_US |
dc.language | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.relation.ispartofseries | v. 15, NO 13;1-24 | - |
dc.subject | ergo-green-lean framework | en_US |
dc.subject | productivity | en_US |
dc.subject | KPIs | en_US |
dc.subject | REBA | en_US |
dc.subject | standard Nordic questionnaire (SNQ) | en_US |
dc.subject | carbon footprint analysis (CFA) | en_US |
dc.subject | value stream mapping (VSM) | en_US |
dc.subject | Python regression model | en_US |
dc.title | An Empirical Study of the Implementation of an Integrated Ergo-Green-Lean Framework: A Case Study | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/su151310138 | en_US |
dc.relation.journal | SUSTAINABILITY | - |
dc.contributor.googleauthor | Kanan, Mohammad | - |
dc.contributor.googleauthor | Dilshad, Ansa Rida | - |
dc.contributor.googleauthor | Zahoor, Sadaf | - |
dc.contributor.googleauthor | Hussain, Amjad | - |
dc.contributor.googleauthor | Habib, Muhammad Salman | - |
dc.contributor.googleauthor | Mehmood, Amjad | - |
dc.contributor.googleauthor | Abusaq, Zaher | - |
dc.contributor.googleauthor | Hamdan, Allam | - |
dc.contributor.googleauthor | Asad, Jihad | - |
dc.relation.code | 2023036095 | - |
dc.sector.campus | E | - |
dc.sector.daehak | EXECUTIVE VICE PRESIDENT FOR ERICA[E] | - |
dc.identifier.pid | salmanhabib | - |
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