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dc.contributor.author하비브 무하마드 살만-
dc.date.accessioned2024-05-09T01:26:24Z-
dc.date.available2024-05-09T01:26:24Z-
dc.date.issued2023-06-26-
dc.identifier.citationSUSTAINABILITY, v. 15, NO 13, page. 1-24en_US
dc.identifier.issn2071-1050en_US
dc.identifier.urihttps://information.hanyang.ac.kr/#/eds/detail?an=edsdoj.0f28fdc0105a42c79ff3087a4863e967&dbId=edsdojen_US
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/190228-
dc.description.abstractThe 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.languageen_USen_US
dc.publisherMDPIen_US
dc.relation.ispartofseriesv. 15, NO 13;1-24-
dc.subjectergo-green-lean frameworken_US
dc.subjectproductivityen_US
dc.subjectKPIsen_US
dc.subjectREBAen_US
dc.subjectstandard Nordic questionnaire (SNQ)en_US
dc.subjectcarbon footprint analysis (CFA)en_US
dc.subjectvalue stream mapping (VSM)en_US
dc.subjectPython regression modelen_US
dc.titleAn Empirical Study of the Implementation of an Integrated Ergo-Green-Lean Framework: A Case Studyen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/su151310138en_US
dc.relation.journalSUSTAINABILITY-
dc.contributor.googleauthorKanan, Mohammad-
dc.contributor.googleauthorDilshad, Ansa Rida-
dc.contributor.googleauthorZahoor, Sadaf-
dc.contributor.googleauthorHussain, Amjad-
dc.contributor.googleauthorHabib, Muhammad Salman-
dc.contributor.googleauthorMehmood, Amjad-
dc.contributor.googleauthorAbusaq, Zaher-
dc.contributor.googleauthorHamdan, Allam-
dc.contributor.googleauthorAsad, Jihad-
dc.relation.code2023036095-
dc.sector.campusE-
dc.sector.daehakEXECUTIVE VICE PRESIDENT FOR ERICA[E]-
dc.identifier.pidsalmanhabib-
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