Sameer Kumar, Yogesh Marawar, G. Soni, V. Jain, A. Gurumurthy, R. Kodali
{"title":"通过价值流映射工具的优化排序来提高制造组织绩效的混合方法","authors":"Sameer Kumar, Yogesh Marawar, G. Soni, V. Jain, A. Gurumurthy, R. Kodali","doi":"10.1108/ijlss-03-2022-0069","DOIUrl":null,"url":null,"abstract":"\nPurpose\nLean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.\n\n\nDesign/methodology/approach\nA proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.\n\n\nFindings\nConsidering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.\n\n\nOriginality/value\nThe design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.\n","PeriodicalId":48601,"journal":{"name":"International Journal of Lean Six Sigma","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A hybrid approach to enhancing the performance of manufacturing organizations by optimal sequencing of value stream mapping tools\",\"authors\":\"Sameer Kumar, Yogesh Marawar, G. Soni, V. Jain, A. Gurumurthy, R. Kodali\",\"doi\":\"10.1108/ijlss-03-2022-0069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nLean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.\\n\\n\\nDesign/methodology/approach\\nA proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.\\n\\n\\nFindings\\nConsidering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.\\n\\n\\nOriginality/value\\nThe design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. 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A hybrid approach to enhancing the performance of manufacturing organizations by optimal sequencing of value stream mapping tools
Purpose
Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.
Design/methodology/approach
A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.
Findings
Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.
Originality/value
The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.
期刊介绍:
Launched in 2010, International Journal of Lean Six Sigma publishes original, empirical and review papers, case studies and theoretical frameworks or models related to Lean and Six Sigma methodologies. High quality submissions are sought from academics, researchers, practitioners and leading management consultants from around the world. Research, case studies and examples can be cited from manufacturing, service and public sectors. This includes manufacturing, health, financial services, local government, education, professional services, IT Services, transport, etc.