Modeling and Analysis of Lean Manufacturing Strategies Using ISM-Fuzzy MICMAC Approach

  • Mohit Tyagi Department of Industrial and Production Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India
  • Dilbagh Panchal 1Department of Industrial and Production Engineering, Dr B R Ambedkar National Institute of Technology Jalandhar, Punjab, India
  • Deepak Kumar Department of Mechanical Engineering, Delhi Technological University, Delhi, India
  • R. S. Walia Department of Production and Industrial Engineering, PEC University, Chandigarh, India
Keywords: Lean Manufacturing System (LMS); Lean Strategies; Factor Analysis; SPSS 21; ISM Methodology; Fuzzy MICMAC


The current research work deals with an identification of different lean strategies and extraction to relevant strategies after discussion with experts and gives the answer of a question “how lean manufacturing strategies can help the organization to enhance the efficiency of the organization with great effectiveness?”  In this research work, thirty-six lean strategies have been identified and out of which thirteen lean strategies were filtered in respect of highly importance value by factor analysis using software SPSS 21. Further, to identify and analyze the inter-relationship among filtered strategies, an Interpretive Structural Modeling (ISM) with Fuzzy Matriced’ Impacts Croise´s Multiplication Applique´e a UN Classement (MICMAC) approach has been used. Fuzzy MICMAC help to understand the dependence and driver’s power of the lean strategies. The mutual importance of extracted strategies has been discussed through developing the ISM model and the individual assessment of each strategy with each of the other strategies has been derived using the Fuzzy MICMAC approach.


Ahlstrom, P. (2004). Lean service operations: translating lean Pro principles to service operations. International Journal of Services Technology and Management, 5(5-6): 545-564.

Al-Tit, A. A. (2017). Factors affecting the organizational performance of manufacturing firms. International Journal of Engineering Business Management, 9: 1-9.

Anand, G., & Ward, P. T. (2004). Fit, flexibility and performance in manufacturing: coping with dynamic environments. Production and Operations Management, 13(4): 369-385.

Arslankaya S & Atay H. (2015). Maintenance Manage and Lean Manufacturing Practices in a Firm Which Produces Dairy Products. Procedia-Social and Behavioral Sciences, 207: 214-224.

Ballard, G., & Howell, G. (1998). Shielding production: essential step in production control. Journal of Construction Engineering and management, 124(1): 11-17.

Berlin, C., Neumann, W. P., Theberge, N., &Örtengren, R. (2014). Avenues of entry: how industrial engineers and ergonomists access and influence human factors and ergonomics issues. European Journal of Industrial Engineering, 8(3): 325-348.

Brown, S., & Cousins, P. D. (2004). Supply and operations: parallel paths and integrated strategies. British Journal of Management, 15(4): 303-320.

Chai, S. F., Luo, S. J., & Zhang, L. J. (2012). Study on simulation of the main shaft production line. In Advanced Materials Research, 472: 2076-2079.

Charan, P., Shankar, R., & Baisya, R. K. (2008). Analysis of interactions among the variables of supply chain performance measurement system implementation. Business Process Management Journal, 14(4): 512-529.

Chatterjee, P., & Stević, Ž. (2019). A two-phase fuzzy AHP-fuzzy TOPSIS model for supplier evaluation in manufacturing environment. Operational Research in Engineering Sciences: Theory and Applications, 2(1): 72-90.

Chinprateep, S., & Boondiskulchok, R. (2011). Heuristic for integrated purchasing and production planning. European Journal of Industrial Engineering, 5(1): 64-80.

Đalić, I., Ateljević, J., Stević, Ž., & Terzić, S. (2020). An integrated swot–fuzzy piprecia model for analysis of competitiveness in order to improve logistics performances. FactaUniversitatis, Series: Mechanical Engineering, 18(3): 439-451.

Dewangan, D. K., Agrawal, R., & Sharma, V. (2015). Enablers for competitiveness of Indian manufacturing sector: An ISM-fuzzy MICMAC analysis. Procedia-Social and Behavioral Sciences, 189: 416-432.

Diabat, A., & Govindan, K. (2011). An analysis of the drivers affecting the implementation of green supply chain management. Resources, conservation and recycling, 55(6): 659-667.

Dos Santos, Z. G., Vieira, L., & Balbinotti, G. (2015). Lean Manufacturing and ergonomic working conditions in the automotive industry. Procedia Manufacturing, 3: 5947-5954.

Dul, J., & Neumann, W. P. (2009). Ergonomics contributions to company strategies. Applied ergonomics, 40(4): 745-752.

Duraccio, V., Forcina, A., Silvestri, A., & Bona, G. D. (2014). Assessment of the Effectiveness of Maintenance-oriented Design. International Journal of Engineering Business Management, 6: 6-19.

Faisal, M. N. (2010). Sustainable supply chains: a study of interaction among the enablers. Business Process Management Journal, 16(3): 508–529.

Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education.

Gorane, S. J., & Kant, R. (2013). Supply chain management: modeling the enablers using ISM and fuzzy MICMAC approach. International Journal of Logistics Systems and Management, 16(2): 147-166.

Greinacher, S., Moser, E., Hermann, H., & Lanza, G. (2015). Simulation based assessment of lean and green strategies in manufacturing systems. Procedia CIRP, 29: 86-91.

Guo, Z., Ngai, E., Yang, C., & Liang, X. (2015). An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. International journal of production economics, 159: 16-28.

Guillen, D., Gomez, D., Hernandez, I., Charris, D., Gonzalez, J., Leon, D., & Sanjuan, M. (2020). Integrated methodology for industrial facilities management and design based on FCA and lean manufacturing principles. Facilities, 38 (7/8): 523-538.

Hackman, J. R., & Wageman, R. (1995). Total quality management: Empirical, conceptual, and practical issues. Administrative science quarterly, 40(2): 309–342.

Harland, C. M. (1996). Supply chain management: relationships, chains and networks. British Journal of management, 7(s1): S63-S80.

Hartini, S., & Ciptomulyono, U. (2015). The relationship between lean and sustainable manufacturing on performance: literature review. Procedia Manufacturing, 4: 38-45.

Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational research methods, 7(2): 191-205.

Heimerl, C., & Kolisch, R. (2010). Work assignment to and qualification of multi-skilled human resources under knowledge depreciation and company skill level targets. International Journal of Production Research, 48(13): 3759-3781.

Hugo, A., & Pistikopoulos, E. N. (2005). Environmentally conscious long-range planning and design of supply chain networks. Journal of Cleaner Production, 13(15): 1471-1491.

Jain, V., & Raj, T. (2015). Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC. International Journal of System Assurance Engineering and Management, 6(3): 350-371.

Jain, V., & Raj, T. (2016). Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach. International journal of production economics, 171: 84-96.

Jasti, N. V. K., & Kodali, R. (2016). Validity and reliability of lean enterprise frameworks in Indian manufacturing industry. Proceedings of the institution of mechanical engineers, Part B: Journal of engineering manufacture, 230(2): 354-363.

Kandasamy, W. V., Smarandache, F., & Ilanthenral, K. (2007). Elementary fuzzy matrix theory and fuzzy models for social scientists. Infinite Study, Published by Automaton, Los Angeles, USA.

Kannan, G., & Haq, A. N. (2007). Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research, 45(17): 3831-3852.

Kannan, G., Pokharel, S., & Kumar, P. S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, conservation and recycling, 54(1): 28-36.

Kenné, J. P., Gharbi, A., & Beit, M. (2007). Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale. European Journal of Operational Research, 178(2): 408-420.

Kushwaha, D. K., Panchal, D., & Sachdeva, A. (2020). Risk analysis of cutting system under intuitionistic fuzzy environment. Reports in Mechanical Engineering, 1(1): 162-173.

Kusrini, E., Subagyo, & Masruroh, N. A. (2014). Good criteria for supply chain performance measurement. International Journal of Engineering Business Management, 6(9): 1-19.

Le Dain, M. A., Calvi, R., & Cheriti, S. (2011). Measuring supplier performance in collaborative design: proposition of a framework. R&d Management, 41(1): 61-79.

Lee, A. H., Kang, H. Y., & Chang, C. C. (2011). An integrated interpretive structural modeling–fuzzy analytic network process–benefits, opportunities, costs and risks model for selecting technologies. International Journal of Information Technology & Decision Making, 10(05): 843-871.

Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: from constructs to theory. Journal of operations management, 16(4): 407-425.

Mandal, A., & Deshmukh, S. G. (1994). Vendor selection using interpretive structural modeling (ISM). International journal of operations & production management, 14(6): 52-59.

McKone, K. E., Schroeder, R. G., & Cua, K. O. (2001). The impact of total productive maintenance practices on manufacturing performance. Journal of operations management, 19(1): 39-58.

Mohammaddust, F., Rezapour, S., Farahani, R. Z., Mofidfar, M., & Hill, A. (2017). Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs. International Journal of Production Economics, 183: 632-653.

Mudgal, R. K., Shankar, R., Talib, P., & Raj, T. (2010). Modeling the barriers of green supply chain practices: An Indian perspective. International Journal of Logistics Systems and Management, 7(1): 81-107.

Narasimhan, R., Swink, M., & Kim, S. W. (2006). Disentangling leanness and agility: an empirical investigation. Journal of operations management, 24(5): 440-457.

Nenni, M. E., Giustiniano, L., & Pirolo, L. (2014). Improvement of manufacturing operations through a lean management approach: a case study in the pharmaceutical industry. International Journal of Engineering Business Management, 6(1): 24.

Nordin, N., Deros, B. M., & AbdWahab, D. (2010). A survey on lean manufacturing implementation in Malaysian automotive industry. International Journal of Innovation, Management and Technology, 1(4): 374.

Onyeocha, C. E., Khoury, J., & Geraghty, J. (2015). Evaluation of multi-product lean manufacturing systems with setup and erratic demand. Computers & Industrial Engineering, 87: 465-480.

Pająk, M. (2020). Fuzzy model of the operational potential consumption process of a complex technical system. Facta Universitatis, Series: Mechanical Engineering, 18(3): 453-472.

Panchal, D., & Kumar, D. (2014). Reliability analysis of CHU system of coal fired thermal power plant using fuzzy λ-τ approach. Procedia Engineering, 97: 2323-2332.

Panchal, D., Jamwal, U., Srivastava, P., Kamboj, K., & Sharma, R. (2018). Fuzzy methodology application for failure analysis of transmission system. International Journal of Mathematics in Operational Research, 12(2): 220-237.

Prasad, M. M., Dhiyaneswari, J. M., Jamaan, J. R., Mythreyan, S., & Sutharsan, S. M. (2020). A framework for lean manufacturing implementation in Indian textile industry. Materials Today: Proceedings, 33: 2986-2995.

Palange, A., & Dhatrak, P. (2021). Lean manufacturing a vital tool to enhance productivity in manufacturing. Materials Today:

Panchal, D., Singh, A. K., Chatterjee, P., Zavadskas, E. K., & Keshavarz-Ghorabaee, M. (2019). A new fuzzy methodology-based structured framework for RAM and risk analysis. Applied Soft Computing, 74: 242-254.

Petrović, G., Mihajlović, J., Ćojbašić, Ž., Madić, M., & Marinković, D. (2019). Comparison of three fuzzy MCDM methods for solving the supplier selection problem. Facta Universitatis, Series: Mechanical Engineering, 17(3): 455-469.

Qureshi, M. N., Kumar, D., & Kumar, P. (2008). An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pacific Journal of Marketing and Logistics, 20(2): 227-249.

Rahman, N. A. A., Sharif, S. M., & Esa, M. M. (2013). Lean manufacturing case study with Kanban system implementation. Procedia Economics and Finance, 7: 174-180.

Riezebos, J., Klingenberg, W., & Hicks, C. (2009). Lean production and information technology: connection or contradiction? Computers in industry, 60(4): 237-247.

Rohani, J. M., & Zahraee, S. M. (2015). Production line analysis via value stream mapping: a lean manufacturing process of color industry. Procedia Manufacturing, 2: 6-10.

Salleh, N. A. M., Kasolang, S., & Jaffar, A. (2012). Simulation of integrated total quality management (TQM) with lean manufacturing (LM) practices in forming process using Delmia Quest. Procedia Engineering, 41: 1702-1707.

Schiele, J. J., & McCue, C. P. (2010). A framework for the adoption of lean thinking within public procurement. International Journal of Procurement Management, 3(4): 379-396.

Seifert, D. (2003). Collaborative planning, forecasting, and replenishment: How to create a supply chain advantage. AMACOM Div American Mgmt Assn.

Shah, R., & Ward, P. T. (2003). Lean manufacturing: context, practice bundles, and performance. Journal of operations management, 21(2): 129-149.

Sharma, R., & Garg, S. (2010). Interpretive structural modeling of enablers for improving the performance of automobile service centre. International Journal of Services Operations and Informatics, 5(4): 351-372.

Shuaib, M., Khan, U., & Haleem, A. (2016). Modeling knowledge sharing factors and understanding its linkage to competitiveness. International Journal of Global Business and Competitiveness, 11(1): 23-36.

Singh, B., Garg, S. K., Sharma, S. K., & Grewal, C. (2010). Lean implementation and its benefits to production industry. International journal of lean six sigma, 1(2): 157-168.

Singh, R. K., Sharma, H. O., & Garg, S. K. (2010). Interpretive structural modeling for selection of best supply chain practices. International Journal of Business Performance and Supply Chain Modelling, 2(3): 237-257.

Soroush, H. M. (2015). Scheduling with job-dependent past-sequence-dependent setup times and job-dependent position-based learning effects on a single processor. European Journal of Industrial Engineering, 9(3): 277-307.

Srinivasaraghavan, J., & Allada, V. (2006). Application of mahalanobis distance as a lean assessment metric. The International Journal of Advanced Manufacturing Technology, 29(11-12): 1159-1168.

Stecke, K. E., & Kim, I. (1988). A study of FMS part type selection approaches for short-term production planning. International Journal of Flexible Manufacturing Systems, 1(1): 7-29.

Stojić, G., Sremac, S., & Vasiljković, I. (2018). A fuzzy model for determining the justifiability of investing in a road freight vehicle fleet. Operational Research in Engineering Sciences: Theory and Applications, 1(1): 62-75.

Susilawati, A., Tan, J., Bell, D., & Sarwar, M. (2015). Fuzzy logic based method to measure degree of lean activity in manufacturing industry. Journal of Manufacturing Systems, 34: 1-11.

Talib, F., Rahman, Z., & Qureshi, M. N. (2011). An interpretive structural modelling approach for modelling the practices of total quality management in service sector. International Journal of Modelling in Operations Management, 1(3): 223-250.

Thakkar, J., Deshmukh, S. G., Gupta, A. D., & Shankar, R. (2007). Development of a balanced scorecard, An integrated approach of interpretive structural modeling (ISM) and analytic network process (ANP). International Journal of Productivity and Performance Management, 56(1): 25-59.

Thierry, M., Salomon, M., Van Nunen, J., & Van Wassenhove, L. (1995). Strategic issues in product recovery management. California management review, 37(2): 114-136.

Tyagi, M., Kumar, P., & Kumar, D. (2015). Analysis of interactions among the drivers of green supply chain management. International Journal of Business Performance and Supply Chain Modelling, 7(1): 92-108.

Tyagi, M., Kumar, P., & Kumar, D. (2017). Modelling and analysis of barriers for supply chain performance measurement system. International Journal of Operational Research, 28(3): 392-414.

Tortorella, G. L., Narayanamurthy, G., & Thurer, M. (2021). Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry. International Journal of Production Economics, 231: 107918.

Venkatraman, N., & Ramanujam, V. (1987). Measurement of business economic performance: an examination of method convergence. Journal of management, 13(1): 109-122.

Wahab, A. N. A., Mukhtar, M., & Sulaiman, R. (2013). A conceptual model of lean manufacturing dimensions. Procedia Technology, 11: 1292-1298.

Wang, G., Wang, Y., & Zhao, T. (2008). Analysis of interactions among the barriers to energy saving in China. Energy Policy, 36(6): 1879-1889.

Wang, Z., Subramanian, N., Gunasekaran, A., Abdulrahman, M. D., & Liu, C. (2015). Composite sustainable manufacturing practice and performance framework: Chinese auto-parts suppliers׳ perspective. International Journal of Production Economics, 170: 219-233.

Ward, P. T., & Duray, R. (2000). Manufacturing strategy in context: environment, competitive strategy and manufacturing strategy. Journal of operations management, 18(2): 123-138.

Warfield, J. N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, 1: 81-87.

Womack, J.P., Jones D.T. & Roos D. (1990). The machine that changed the World: The triumph of lean Production. New York: Rawson Macmillan.

Youssouf, A., Rachid, C., & Ion, V. (2014). Contribution to the optimization of strategy of maintenance by lean six sigma. Physics procedia, 55: 512-518.

Yusup, M. Z., Mahmood, W. H. W., & Salleh, M. R. (2015). Basic formation in streamlining lean practices in manufacturing operations-a review. International Journal of Advanced Operations Management, 7(4): 255-273.

Yadav, G., Luthra, S., Huisingh, D., Mangla, S. K., Narkhede, B. E., & Liu, Y. (2020). Development of a lean manufacturing framework to enhance its adoption within manufacturing companies in developing economies. Journal of Cleaner Production, 245: 118726.

Zavadskas, E. K., Turskis, Z., Stević, Ž., & Mardani, A. (2020). Modelling procedure for the selection of steel pipes supplier by applying fuzzy AHP method. Operational Research in Engineering Sciences: Theory and Applications, 3(2): 39-53.

Zhao, X., Flynn, B. B., & Roth, A. V. (2006). Decision sciences research in China: a critical review and research agenda—foundations and overview. Decision Sciences, 37(4): 451-496.

How to Cite
Mohit Tyagi, Dilbagh Panchal, Deepak Kumar, & R. S. Walia. (2021). Modeling and Analysis of Lean Manufacturing Strategies Using ISM-Fuzzy MICMAC Approach. Operational Research in Engineering Sciences: Theory and Applications, 4(1), 38-66. Retrieved from