Improvement of Manufacturing Process Based on Value Stream Mapping: A Case Study

Abstract

AbstractValue Stream Mapping (VSM) is a key tool in Lean Manufacturing (LM) that helps identify opportunities for process improvement. This research aims to propose an integrated method using LM tools (such as Kanban, VSM, Pareto chart, and Supplier Input Process Output Customer) and Arena simulation to improve the productivity of the production line through reducing lead time, inventory time, including Raw Material (RM) time and Work In Progress (WIP) time and enhancing Process Cycle Efficiency (PCE) ratio. To begin the process, a Pareto chart is employed to identify the main product of the organization. Subsequently, a current VSM is constructed to identify any waste in the production line. The Kanban tool is then used to propose appropriate remedies to improve the manufacturing process. To validate the efficiency of the production capacity in future VSM, Arena simulation is executed by determining Takt Time. Lastly, a future VSM is formulated based on the suggested improvements, and an appraisal is conducted to provide recommendations for prospective enhancements. A real case study of a furniture company in Vietnam is performed to demonstrate the effectiveness of the proposed method. The results reveal impressive benefits, including a 92% reduction in lead time and a corresponding 92% increase in PCE. Additionally, there is a substantial reduction of around 90% in WIP time, while RM time is completely eliminated, resulting in a 100% reduction. These findings highlight the efficacy of the proposed approach and the importance of VSM and other Lean tools in achieving process optimization.Keywords: Current Value Stream MappingFuture Value Stream MappingLean ManufacturingKanban Toolinventory TimeEMJ Focus Areas: Continuous ImprovementOrganization & Work System DesignSystems EngineeringTechnology Management Disclosure StatementNo potential conflict of interest was reported by the author(s).Data Availability StatementAll data, models, and code generated or used during the study appear in the submitted articleAdditional informationNotes on contributorsChia-Nan WangChia-Nan Wang received the Ph.D. degree in industrial engineering and management from the Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, in 2004. He is currently a Professor with the Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan. His research interests include production & operation research, decision analysis, management of technology, systematic innovation, smart manufacturing and business management.Tran Thi Bich Chau VoTran Thi Bich Chau Vo is a Ph.D. student from National Kaohsiung University of Science and Technology at the Department of Industrial Engineering and Management, Taiwan. She graduated an M.Sc in Industrial System Engineering at Ho Chi Minh City University of Technology (HCMUT), Vietnam, in 2015. Her research interests include conducting experiments on lean manufacturing, demand forecasting, inventory prediction, business process re-engineering, and especially intelligent manufacturing.Yu-Chi ChungYu-Chi Chung received his Ph.D. degree in the Department of Computer Science and Information Engineering at the National Cheng Kung University, Tai wan, in 2007. Currently, he is an Associate Professor of the Department of Industrial Engineering and Management National Kaohsiung University of Science and Technology, Taiwan. His research interests include mobile/wireless data management, sensor networks, skyline query processing, spatio-temporal databases, web information retrieval, and machine learning.Yousef AmerYousef Amer is a Program Director at the School of Engineering, University of South Australia. His research interests include simulation-based Lean Six-Sigma and Design for Six-Sigma, Artificial Intelligence, Manufacturing Strategy and Technology, Sustainability in Product and Service Development, Lean and Green Supply Chain Modeling, Optimization and Simulation, and Sustainable Nano-manufacturing. He has published books and many academic papers, including International Journal of Production Economics, International Journal of Production Research, and Robotics and Computer-Integrated Manufacturing.Linh Thi Truc DoanLinh Thi Truc Doan got her Bachelor’s Degree in Chemical Engineering in 2006 and then received an M.Sc in Industrial Management from Taiwan Tech University, Taiwan, in 2010. She completed her Ph.D. program in System Engineering at UniSA STEM, The University of South Australia, in 2020. Her interests include optimization, reverse supply chain management, project management, and Lean Manufacturing.