{"title":"多层多变量价值流映射:跨操作层、环境层和社会层的综合框架,具有集成的kpi相互关系","authors":"Ayoub Heydarzade , Niloofar Rezaei , Seyed Alireza Vaezi , Jaime A. Camelio","doi":"10.1016/j.mfglet.2025.06.023","DOIUrl":null,"url":null,"abstract":"<div><div>Industry 4.0 technologies have increased the complexity and interconnectivity of manufacturing systems, challenging the conventional scope of Value Stream Mapping (VSM). In response, this paper proposes a Multi-Layer Multi-Variable Value Stream Mapping (MLMV-VSM) framework that integrates operational, environmental, and social layers within a single methodology. The approach captures Key Performance Indicators (KPIs) and their interdependencies, enabling more balanced system optimization. Unlike traditional VSM, MLMV-VSM explicitly incorporates human-centric metrics, such as stress and fatigue, along with operational and environmental factors. An illustrative example demonstrates how operator skill development can influence production speed, energy consumption, and ergonomic outcomes, highlighting cross-layer trade-offs and synergies. The paper also addresses practical challenges, including the measurement of social metrics, the prioritization of competing KPIs, and the need for real-time adaptability. Finally, avenues for future work are identified, emphasizing the integration of Industry 4.0 technologies such as the Internet of Things (IoT) and data analytics to support dynamic decision-making and foster sustainable manufacturing practices.</div></div>","PeriodicalId":38186,"journal":{"name":"Manufacturing Letters","volume":"44 ","pages":"Pages 184-194"},"PeriodicalIF":2.0000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-layer multi-variable value stream mapping: A comprehensive framework across operational, environmental, and social layers with integrated KPIs interrelationships\",\"authors\":\"Ayoub Heydarzade , Niloofar Rezaei , Seyed Alireza Vaezi , Jaime A. Camelio\",\"doi\":\"10.1016/j.mfglet.2025.06.023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Industry 4.0 technologies have increased the complexity and interconnectivity of manufacturing systems, challenging the conventional scope of Value Stream Mapping (VSM). In response, this paper proposes a Multi-Layer Multi-Variable Value Stream Mapping (MLMV-VSM) framework that integrates operational, environmental, and social layers within a single methodology. The approach captures Key Performance Indicators (KPIs) and their interdependencies, enabling more balanced system optimization. Unlike traditional VSM, MLMV-VSM explicitly incorporates human-centric metrics, such as stress and fatigue, along with operational and environmental factors. An illustrative example demonstrates how operator skill development can influence production speed, energy consumption, and ergonomic outcomes, highlighting cross-layer trade-offs and synergies. The paper also addresses practical challenges, including the measurement of social metrics, the prioritization of competing KPIs, and the need for real-time adaptability. Finally, avenues for future work are identified, emphasizing the integration of Industry 4.0 technologies such as the Internet of Things (IoT) and data analytics to support dynamic decision-making and foster sustainable manufacturing practices.</div></div>\",\"PeriodicalId\":38186,\"journal\":{\"name\":\"Manufacturing Letters\",\"volume\":\"44 \",\"pages\":\"Pages 184-194\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2025-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Manufacturing Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213846325000495\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Manufacturing Letters","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213846325000495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
Multi-layer multi-variable value stream mapping: A comprehensive framework across operational, environmental, and social layers with integrated KPIs interrelationships
Industry 4.0 technologies have increased the complexity and interconnectivity of manufacturing systems, challenging the conventional scope of Value Stream Mapping (VSM). In response, this paper proposes a Multi-Layer Multi-Variable Value Stream Mapping (MLMV-VSM) framework that integrates operational, environmental, and social layers within a single methodology. The approach captures Key Performance Indicators (KPIs) and their interdependencies, enabling more balanced system optimization. Unlike traditional VSM, MLMV-VSM explicitly incorporates human-centric metrics, such as stress and fatigue, along with operational and environmental factors. An illustrative example demonstrates how operator skill development can influence production speed, energy consumption, and ergonomic outcomes, highlighting cross-layer trade-offs and synergies. The paper also addresses practical challenges, including the measurement of social metrics, the prioritization of competing KPIs, and the need for real-time adaptability. Finally, avenues for future work are identified, emphasizing the integration of Industry 4.0 technologies such as the Internet of Things (IoT) and data analytics to support dynamic decision-making and foster sustainable manufacturing practices.