Abstract
To be productive and to remain competitive in the current business environment, industries need to embrace technological advancements and latest concepts. One such latest and widely preferred concept is cellular manufacturing systems (CMS). CMS, a developed version of lean manufacturing concept, aims at eliminating the unproductive works and also in reducing the number of activities. Though, CMS offers many benefits, the industries are facing many difficulties in the implementation of CMS. This research work aims at identifying and evaluating the barriers in the implementation of CMS from a real industrial setting. For this, initially, 11 barriers to the implementation of CMS were identified through comprehensive literature survey. Then, these barriers were analysed using interpretive structural modelling, a multi-criteria decision making technique. Outcome of the study indicate inventory build-up, machine utilization, control and supervision as the top three barriers in the implementation of CMS. Based on the outcome, this study provides some implications for the industry practitioners to overcome these barriers in implementing effective CMS. The implications of this study may act as a guide for the industries in increasing the production capacity and better outputs.
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References
Ansari MF, Kharb RK, Luthra S, Shimmi SL, Chatterji S (2013) Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique. Renew Sustain Energy Rev 27:163–174
Bathrinath S, Koshy RA, Bhalaji RKA, Koppiahraj K (2021) Identification of the critical activity in heat treatment process using TISM. Mater Today Proc 39:60–65
Bhalaji RKA, Bathrinath S, Saravanasankar S (2019) Analysis of risk factors related to patients in healthcare industry using ISM method. In: AIP conference proceedings, vol 2128, no 1. AIP Publishing LLC, p 050003
Biresselioglu ME, Kaplan MD, Yilmaz BK (2018) Electric mobility in Europe: a comprehensive review of motivators and barriers in decision making processes. Transp Res Part A Policy Pract 109:1–13
Birgün S, Kulaklı A (2022) Eliminating the barriers of green lean practices with thinking processes. Digitizing production systems. Springer, Cham, pp 372–383
Doukas H, Patlitzianas KD, Psarras J (2006) Supporting sustainable electricity technologies in Greece using MCDM. Resour Policy 31(2):129–136
Duperrin JC, Godet M (1973) Methode de hierarchisation des elements d’un systeme, Rapport economique du CEA. R-45-41, Paris
Hübel C, Schaltegger S (2022) Barriers to a sustainability transformation of meat production practices-An industry actor perspective. Sustain Prod Consum 29:128–140
Kahraman C, Ertay T, Büyüközkan G (2006) A fuzzy optimization model for QFD planning process using analytic network approach. Eur J Oper Res 171(2):390–411
Karuppiah K, Sankaranarayanan B, Ali SM, Kabir G (2020) Role of ergonomic factors affecting production of leather garment-based SMEs of India: implications for social sustainability. Symmetry 12(9):1414
Karuppiah K, Sankaranarayanan B, Ali SM (2021) On sustainable predictive maintenance: exploration of key barriers using an integrated approach. Sustain Prod Consum 27:1537–1553
Kumar N, Mathiyazhagan K, Mathivathanan D (2020) Modelling the interrelationship between factors for adoption of sustainable lean manufacturing: a business case from the Indian automobile industry. Int J Sustain Eng 13(2):93–107
Kumar P, Singh RK, Kumar V (2021) Managing supply chains for sustainable operations in the era of industry 4.0 and circular economy: analysis of barriers. Resour Conserv Recycl 164:105215
Lee K-C, Tsai W-H, Yang C-H, Lin Y-Z (2018) An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations. J Air Transp Manag 68:76–85
Mandal A, Deshmukh SG (1994) Vendor selection using interpretive structural modelling (ISM). Int J Oper Prod Manag 14(6):52–59
Marimuthu R, Sankaranarayanan B, Ali SM, de Sousa Jabbour ABL, Karuppiah K (2021) Assessment of key socio-economic and environmental challenges in the mining industry: implications for resource policies in emerging economies. Sustain Prod Consum 27:814–830
Miltenburg J (2001) U-shaped production lines: a review of theory and practice. Int J Prod Econ 70(3):201–214
Olabi AG, Wilberforce T, Abdelkareem MA (2021) Fuel cell application in the automotive industry and future perspective. Energy 214:118955
Önüt S, Kara SS, Efendigil T (2008) A hybrid fuzzy MCDM approach to machine tool selection. J Intell Manuf 19(4):443–453
Pérez ATE, Rossit DA, Tohmé F, Vásquez ÓC (2022) Mass customized/personalized manufacturing in Industry 4.0 and blockchain: research challenges, main problems, and the design of an information architecture. Inf Fusion 79:44–57
Perno M, Hvam L, Haug A (2022) Implementation of digital twins in the process industry: a systematic literature review of enablers and barriers. Comput Ind 134:103558
Prakash R, Singhal S, Agarwal A (2018) An integrated fuzzy-based multi-criteria decision-making approach for the selection of an effective manufacturing system. Benchmarking Int J 25:280–296
Qarnain SS, Muthuvel S, Sankaranarayanan B (2021) Analysis of energy conservation factors in buildings using interpretive structural modeling methodology: an Indian perspective. J Inst Eng India Ser A 102(1):43–61
Ramaganesh M, Bathrinath S (2020) Analysing environmental factors for corporate social responsibility in mining industry using ISM methodology. In: Soft computing for problem solving. Springer, Singapore, pp 349–360
Ridha HM, Gomes C, Hizam H, Ahmadipour M, Heidari AA, Chen H (2021) Multi-objective optimization and multi-criteria decision-making methods for optimal design of standalone photovoltaic system: a comprehensive review. Renew Sustain Energy Rev 135:110202
Sundar R, Balaji AN, Satheesh Kumar RM (2014) A review on lean manufacturing implementation techniques. Procedia Eng 97:1875–1885
Turner C, Okorie O, Emmanouilidis C, Oyekan J (2022) Circular production and maintenance of automotive parts: an Internet of Things (IoT) data framework and practice review. Comput Ind 136:103593
Waqas M, Dong Q-l, Ahmad N, Zhu Y, Nadeem M (2018) Critical barriers to implementation of reverse logistics in the manufacturing industry: a case study of a developing country. Sustainability 10(11):4202
Warfield JN (1994) Science of generic design: managing complexity through systems design. Iowa State Press
Xu W, Cui J, Li L, Yao B, Tian S, Zhou Z (2021) Digital twin-based industrial cloud robotics: framework, control approach and implementation. J Manuf Syst 58:196–209
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Arunprasath, K., Bathrinath, S., Bhalaji, R.K.A. et al. An integrated approach to modelling of barriers in implementation of cellular manufacturing systems in production industries. Int J Syst Assur Eng Manag 14, 1370–1378 (2023). https://doi.org/10.1007/s13198-023-01941-0
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DOI: https://doi.org/10.1007/s13198-023-01941-0