The purpose of this research paper is to identify the enablers for Lean implementation in the manufacturing sector, to establish a relationship among them using interpretive structural modeling (ISM) and to rank them using interpretive ranking process (IRP).
The research paper presents a blend of theoretical framework and practical applications. In the paper, eight enablers of Lean production are identified from literature survey and experts’ opinion. These include 5S, value stream mapping (VSM), just in time, single minute exchange of die, computer-integrated manufacturing, concurrent engineering, training and enterprise resource planning. ISM is used to obtain a structural relationship among these enablers of Lean. MICMAC analysis is used to identify the driving power and dependence of the variables. Further, IRP is used to rank the lean enablers with respect to key performance areas.
The ISM- and IRP-based models indicate that “training” is the most significant factor for the Lean implementation process in manufacturing sector. The MICMAC analysis also shows that “training” has the maximum driving power and the least dependence and hence has strong managerial significance. The management should place high priority on tackling this criterion. VSM occupies the top level in the ISM hierarchy, indicating that all other Lean enablers should act in unison to make VSM implementation a success.
Enablers are the building blocks for deployment of the Lean concept. To know the key enablers and relationship among them can help many organizations to develop Lean competencies. This study is perhaps among the first few that focuses on two modeling procedures based on interpretive logic, i.e. ISM and IRP. The paper provides useful insights to the Lean production implementers, consultants and researchers.
The authors express gratitude to the experts whose valuable inputs helped in understanding the contextual relationship between the Lean criteria used in this study.
Sharma, V., Dixit, A. and Qadri, M. (2016), "Modeling Lean implementation for manufacturing sector", Journal of Modelling in Management, Vol. 11 No. 2, pp. 405-426. https://doi.org/10.1108/JM2-05-2014-0040Download as .RIS
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