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Framework for sustainable maintenance system: ISM–fuzzy MICMAC and TOPSIS approach

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Abstract

Due to the increasing complexity of manufacturing processes and automation, maintenance of all machines and equipments has become challenging task for production managers today. Due to lack of sensitivity for maintenance, share of maintenance cost in total product cost is also increasing along with decreased productivity. Organizations are either quite slow or getting failed in updating their maintenance systems with time. Keeping in view the importance of maintenance in today’s context, this study has tried to develop a framework for a sustainable maintenance system for manufacturing organizations. Usually organizations are not able to identify critical factors for effective maintenance. Therefore, in this context, the study has identified fourteen factors for the effective maintenance management from the literature review. Some of these factors are process oriented and some are result oriented. Interpretive structural modeling approach is applied for the development of structural relationship among the factors from a strategic perspective. Fuzzy MICMAC analysis is then carried out to categorize these factors based on their driving and dependence value. Further to prioritise major driving factors, Technique for order preferences by similarity of an ideal solution approach has been also applied. It is observed that top management support and commitment, strategic planning and implementation, continuous upgradation of maintenance system to reduce manufacturing lead time and cost are major factors to ensure the sustainable competitive advantage.

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References

  • Abreu, J., Martins, P. V., Fernandes, S., & Zacarias, M. (2013). Business processes improvement on maintenance management: A case study. Procedia Technology,9, 320–330.

    Google Scholar 

  • Bartz, T., Cezar Mairesse Siluk, J., & Paula Barth Bartz, A. (2014). Improvement of industrial performance with TPM implementation. Journal of Quality in Maintenance Engineering,20(1), 2–19. https://doi.org/10.1108/jqme-07-2012-0025.

    Article  Google Scholar 

  • Block, J., Ahmadi, A., Tyrberg, T., & Söderholm, P. (2014). Part-out-based spares provisioning management: A military aviation maintenance case study. Journal of Quality in Maintenance Engineering,20(1), 76–95.

    Google Scholar 

  • Bottani, E., Ferretti, G., Montanari, R., & Vignali, G. (2014). An empirical study on the relationships between maintenance policies and approaches among Italian companies. Journal of Quality in Maintenance Engineering,20(2), 135–162.

    Google Scholar 

  • Bouslah, B., Gharbi, A., & Pellerin, R. (2016). Integrated production, sampling quality control and maintenance of deteriorating production systems with AOQL constraints. Omega,61, 110–126.

    Google Scholar 

  • Casey, D., & Murphy, K. (2009). Issues in using methodological triangulation in research. Nurse Researcher,16(4), 40–55.

    Google Scholar 

  • El-Akruti, K., Zhang, T., & Dwight, R. (2016). Developing an optimum maintenance policy by life cycle cost analysis—A case study. International Journal of Production Research,54, 1–17.

    Google Scholar 

  • Everett, C. (2002). Penn states commitment to quality improvement. Quality Progress, 35(1), 44–49.

    Google Scholar 

  • Foss, C., & Ellefsen, B. (2002). The value of combining qualitative and quantitative approaches in nursing research by means of method triangulation. Journal of Advanced Nursing,40(2), 242–248.

    Google Scholar 

  • Gosavi, A., Murray, S. L., Tirumalasetty, V. M., & Shewade, S. (2011). A budget- sensitive approach to scheduling maintenance in a total productive maintenance (TPM) program. Engineering Management Journal,23(3), 46–56.

    Google Scholar 

  • Graham, N. K., Arthur, Y. D., & Mensah, D. P. (2014). Managerial role in ensuring successful total quality management programme in Ghanaian printing firms. The TQM Journal,26(5), 398–410.

    Google Scholar 

  • Gustafson, A., Schunnesson, H., Galar, D., & Kumar, U. (2013). Production and maintenance performance analysis: manual versus semi-automatic LHDs. Journal of Quality in Maintenance Engineering,19(1), 74–88.

    Google Scholar 

  • Haavisto, I., & Goentzel, J. (2015). Measuring humanitarian supply chain performance in a multi-goal context. Journal of Humanitarian Logistics and Supply Chain Management,5(3), 300–332.

    Google Scholar 

  • Halcomb, E., & Andrew, S. (2005). Triangulation as a method for contemporary nursing research. Nurse Researcher,13(2), 71–82.

    Google Scholar 

  • Harrington, H. J. (1987). The improvement process: How America’s leading companies improve quality. New York, NY: McGraw-Hill Book Company.

    Google Scholar 

  • Hassini, S. B., Tayeb, F. B., Marmier, F., & Rabahi, M. (2015). Considering human resource constraints for real joint production and maintenance schedules. Computers & Industrial Engineering,90, 197–210.

    Google Scholar 

  • Hwang, C. L., & Yoon, K. (1981). Multiple attributes decision making methods and applications. Berlin: Springer.

    Google Scholar 

  • Khatwani, G., Singh, S. P., Trivedi, A., & Chauhan, A. (2015). Fuzzy-TISM: A fuzzy extension of TISM for group decision making. Global Journal of Flexible System Management,16(1), 97–112.

    Google Scholar 

  • Kigsirisin, S., Pussawiro, S., & Noohawm, O. (2016). Approach for total productive maintenance evaluation in water productivity: A case study at Mahasawat water treatment plant. Procedia Engineering,154, 260–267.

    Google Scholar 

  • Komonen, K. (2002). A cost model of industrial maintenance for profitability analysis and benchmarking. International Journal of Production Economics,79, 15–31.

    Google Scholar 

  • Korpela, J., & Tuominen, M. (1996). A decision support system for strategic issues management of logistics. International Journal of Production Economics, 46, 605–620.

    Google Scholar 

  • Kumar, U., Galar, D., Parida, A., Stenström, C., & Berges, L. (2013). Maintenance performance metrics: a state-of-the-art review. Journal of Quality in Maintenance Engineering,19(3), 233–277.

    Google Scholar 

  • Kumar, R., Singh, R. K., & Shankar, R. (2015). Critical success factors for implementation of supply chain management in Indian small and medium enterprises and their impact on performance. IIMB Management Review,27(2), 92–104.

    Google Scholar 

  • Kumar, J., Soni, V. K., & Agnihotri, G. (2014). Impact of TPM implementation on Indian manufacturing industry. International Journal of Productivity and Performance Management,63(1), 44–56.

    Google Scholar 

  • Lin, X. J., Lin, Q., & Zhang, G. N. (2015). Effectivity of total productive maintenance (TPM) in large size organizations—A case study in Shandong Lingong. Applied Mechanics and Materials,701, 1249–1252.

    Google Scholar 

  • Lorén, S., & Maré, J. D. (2015). Maintenance for reliability—A case study. Annals of Operations Research,224(1), 111–119.

    Google Scholar 

  • Lotfi, F. H., Marbini, A. H., Agrell, P. J., Aghayi, N., & Gholami, K. (2013). Allocating fixed resources and setting targets using a common-weights DEA approach. Computers & Industrial Engineering,64, 631–640.

    Google Scholar 

  • Lu, Z., Cui, W., & Han, X. (2015). Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty. Computers & Industrial Engineering,80, 236–244.

    Google Scholar 

  • Macchi, M., & Fumagalli, L. (2013). A maintenance maturity assessment method for the manufacturing industry. Journal of Quality in Maintenance Engineering,19(3), 295–315.

    Google Scholar 

  • Maletič, D., Maletič, M., & Gomišček, B. (2012). The relationship between continuous improvement and maintenance performance. Journal of Quality in Maintenance Engineering,18(1), 30–41.

    Google Scholar 

  • Mangano, G., & Marco, A. D. (2014). The role of maintenance and facility management in logistics: A literature review. Facilities,32(5/6), 241–255.

    Google Scholar 

  • Mosadeghrad, A. M. (2014). Why TQM programmes fail? A pathology approach. The TQM Journal,26(2), 160–187.

    Google Scholar 

  • Mwanza, B. G., & Mbohwa, C. (2015). Design of a total productive maintenance model for effective implementation: Case study of a chemical manufacturing company. Procedia Manufacturing,4, 461–470.

    Google Scholar 

  • Narayan, V. (2012). Business performance and maintenance. Journal of Quality in Maintenance Engineering,18(2), 183–195.

    Google Scholar 

  • Noroozi, A., Khakzad, N., Khan, F., MacKinnon, S., & Abbassi, R. (2013). The role of human error in risk analysis: Application to pre-and post-maintenance procedures of process facilities. Reliability Engineering & System Safety,119, 251–258.

    Google Scholar 

  • Parida, A., Kumar, U., Galar, D., & Stenström, C. (2015). Performance measurement and management for maintenance: A literature review. Journal of Quality in Maintenance Engineering,21(1), 2–33.

    Google Scholar 

  • Pettit, S., & Beresford, A. (2009). Critical success factors in the context of humanitarian aid supply chains. International Journal of Physical Distribution & Logistics Management,39(6), 450–468.

    Google Scholar 

  • Poduval, P. S., Pramod, V. R., & Jagathy Raj, V. P. (2015). Interpretive structural modeling (ISM) and its application in analyzing factors inhibiting implementation of total productive maintenance (TPM). International Journal of Quality & Reliability Management,32(3), 308–331.

    Google Scholar 

  • Rafiq, M. (2015). Structural health monitoring for maintenance management of deteriorating structures: Current practice and state of the art. In 2nd International and 6th national conference on earthquake structures, Kerman, Iran.

  • Shaaban, M. S., & Awni, A. H. (2014). Critical success factors for total productive manufacturing (TPM) deployment at Egyptian FMCG companies. Journal of Manufacturing Technology Management,25(3), 393–414.

    Google Scholar 

  • Sharma, R., & Trikha, V. (2011). TPM implementation in piston manufacturing industry for OEE. Current Trends in Engineering Research,1(1), 122–129.

    Google Scholar 

  • Simões, J. M., Gomes, C. F., & Yasin, M. M. (2016). Changing role of maintenance in business organisations: Measurement versus strategic orientation. International Journal of Production Research,54(11), 3329–3346.

    Google Scholar 

  • Singh, R. K. (2011). Analyzing the interaction of factors for success of total quality management in SMEs. Asian Journal on Quality,12(1), 6–19.

    Google Scholar 

  • Singh, R. K. (2015). Modelling of critical factors for responsiveness in supply chain. Journal of Manufacturing Technology Management,26(6), 868–888.

    Google Scholar 

  • Singh, A. S., Atre, R., Vardia, A., Gupta, V. D., & Sebastian, B. (2013a). Indigenous development amongst challenges: Munjal Showa Limited and the implementation of total productive maintenance. International Journal of Productivity and Performance Management,62(3), 323–338.

    Google Scholar 

  • Singh, R., Gohil, A. M., Shah, D. B., & Desai, S. (2013b). Total productive maintenance (TPM) implementation in a machine shop: A case study. Procedia Engineering,51, 592–599.

    Google Scholar 

  • Singh, R. K., Gunasekaran, A., & Kumar, P. (2017). Third party logistics (3PL) selection for cold chain management: A fuzzy AHP and fuzzy TOPSIS approach. Annals of Operations Research,267, 1–23.

    Google Scholar 

  • Singh, R. K., Gupta, A., & Gunasekaran, A. (2018). Analysing the interaction of factors for resilient humanitarian supply chain. International Journal of Production Research,56, 1–19.

    Google Scholar 

  • Singh, R. K., Gupta, A., Kumar, A., & Khan, T. A. (2016). Ranking of barriers for effective maintenance by using TOPSIS approach. Journal of Quality in Maintenance Engineering,22(1), 18–34.

    Google Scholar 

  • Singh, R. K., & Sharma, M. K. (2014). Prioritizing the alternatives for flexibility in supply chains. Production Planning and Control,25(2), 176–192.

    Google Scholar 

  • Singh, R. K., & Sharma, M. K. (2015). Selecting competitive supply chain using Fuzzy-AHP and Extent analysis. Journal of Industrial and Production Engineering,31(8), 524–538.

    Google Scholar 

  • Tyagi, S., Choudhary, A., Cai, X., & Yang, K. (2015). Value stream mapping to reduce the lead-time of a product development process. International Journal of Production Economics,160, 202–212.

    Google Scholar 

  • Uzun, A., & Ozdogan, A. (2012). Maintenance parameters based production policies optimization. Journal of Quality in Maintenance Engineering,18(3), 295–310.

    Google Scholar 

  • Van Horenbeek, A., Pintelon, L., & Muchiri, P. (2010). Maintenance optimization models and criteria. International Journal of System Assurance Engineering and Management,1(3), 189–200.

    Google Scholar 

  • Vinodh, S., Ramesh, K., & Arun, C. S. (2016). Application of interpretive structural modelling for analysing the factors influencing integrated lean sustainable system. Clean Technologies and Environmental Policy,18(2), 413–428.

    Google Scholar 

  • Wakjira, M. W., & Singh, A. P. (2012). Total productive maintenance: A case study in manufacturing industry. Global Journal of Research in Engineering, 12(1-G), 24–32.

    Google Scholar 

  • World Development Indicators. (2013). Manufacturing, value added (% of gdp). Retrieved from http://data.worldbank.org/products/wdi. Accessed 21 Sept 2016.

  • Wu, B., Tian, Z., & Chen, M. (2013). Condition-based maintenance optimization using neural network-based health condition prediction. Quality and Reliability Engineering International,29(8), 1151–1163.

    Google Scholar 

  • Yan, J. (2015). Machinery prognostics and prognosis oriented maintenance management. Hoboken: Wiley.

    Google Scholar 

  • Yunis, M., Jung, J., & Chen, S. (2013). TQM, strategy, and performance: A firm-level analysis. International Journal of Quality & Reliability Management,30(6), 690–714.

    Google Scholar 

  • Zadeh, L. (1965). Fuzzy sets. Information and Control,8(3), 338–353.

    Google Scholar 

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Acknowledgements

Authors are grateful to the Editor of the journal and reviewers for giving valuable suggestions to improve the quality and content of this research paper.

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  1. Management Development Institute, Gurgaon, India

    Rajesh Kumar Singh

  2. Indian Institute of Management, Ahmedabad, India

    Ayush Gupta

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  1. Rajesh Kumar Singh
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Singh, R.K., Gupta, A. Framework for sustainable maintenance system: ISM–fuzzy MICMAC and TOPSIS approach. Ann Oper Res 290, 643–676 (2020). https://doi.org/10.1007/s10479-019-03162-w

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