Abstract
To deal with dynamic customer preferences and global competition, Medium, Small and Micro Enterprises (MSMEs) are striving to improve customer satisfaction by enhancing their process capability, optimising resource utilization and achieving cost effectiveness. Manufacturing line in MSMEs involves a number of complex processes and process variations lead to rejections of poor quality products resulting in monetary losses and customer dissatisfaction. Delivery of high quality product within constraints of manpower, machinery and other limited resources stipulates the need to improve the process performance of manufacturing line through quality management. With this perspective, the present work proposes a framework to identify and prioritize defects by integrating multicriteria decision making techniques- Fuzzy Decision Making Trial and Evaluation Laboratory and Fuzzy Analytic Network Process with Quality Management Practices. The integration filters out most influential defects prior to data collection and prioritize them to reach out to critical defects of manufacturing process. Additionally, it addresses challenges faced by management in terms of large number of defects, insufficient data on defects and dependency among selected criteria. The proposed framework is exhibited with the help of a real case study. It is practically relevant in deriving decision support solutions for improving performance of manufacturing line in MSME firms. By virtue of the results, key areas are identified to augment responsiveness to government policies and MSME’s proficiency to overcome resource constraints.
Similar content being viewed by others
Abbreviations
- MSME:
-
Medium, small and micro enterprises
- FDEMATEL:
-
Fuzzy decision making trial and evaluation laboratory
- FANP:
-
Fuzzy analytic network process
- QMP:
-
Quality management practices
- GoI:
-
Government of India
- GDP:
-
Gross domestic product
- FICCI:
-
Federation of Indian chambers of commerce and industry
- PwC:
-
Pricewaterhousecoopers
- MoMSME:
-
Ministry of medium, small and micro enterprises
- IBEF:
-
India brand equity foundation
- M/o Textile:
-
Ministry of textile
- NMCP:
-
National manufacturing competitiveness programme
- DCMSME:
-
Development commissioner ministry of medium, small and micro enterprises
- USA:
-
United States of America
- UK:
-
United Kingdom
- MCDM:
-
Multi-criteria decision making
- ZED:
-
Zero defect and zero effect
- C&E:
-
Cause and effect
- FMEA:
-
Failure mode of effect and analysis
- DMAIC:
-
Define-measure-analyse-improve-control
- SIPOC:
-
Supply input process output customer
- CRT:
-
Current reality tree
- TMED:
-
Taguchi method of experimental design
- CFCS:
-
Converting fuzzy data into crisp scores
- BNP:
-
Best non-fuzzy performance
- CGD:
-
Cause group defects
- KPI:
-
Key performance indicators
- TFN:
-
Triangular fuzzy numbers
- OA:
-
Orthogonal arrays
- S/N :
-
Signal to noise
- ANOVA:
-
Analysis of variance
- SOP:
-
Standard operating procedures
References
Ablanedo-Rosas, J. H., Alidaee, B., Moreno, J. C., & Urbina, J. (2010). Quality improvement supported by the 5S, an empirical case study of Mexican organisations. International Journal of Production Research, 48(23), 7063–7087.
Bagri, G. P., Garg, D., & Agarwal, A. (2021). Examining green practices and firm performances. International Journal of Business Performance and Supply Chain Modelling, 12(4), 329–361.
Ben, F. A., & Jaouachi, B. (2022). Study of the effect of enzymatic washing parameters on the bagging properties of denim fabric with Taguchi method. Journal of Surfactants and Detergents, 25(4), 505–519.
Bhat, S., Gijo, E. V., Rego, A. M., & Bhat, V. S. (2020). Lean Six Sigma competitiveness for micro, small and medium enterprises (MSME): An action research in the Indian context. The TQM Journal, 33(2), 379–406.
Brooke, J. (1996). SUS A quick and dirty usability scale. In P. W. Jordan, B. Thomas, B. A. Weerdmeester, & A. L. McClelland (Eds.), Usability Evaluation in Industry (pp. 189–194). London: Taylor and Francis.
Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011.
Chang, B., Chang, C. W., & Wu, C. H. (2011). Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38(3), 1850–1858.
Chen, K. S., & Chang, T. C. (2020). Construction and fuzzy hypothesis testing of Taguchi Six Sigma quality index. International Journal of Production Research, 58(10), 3110–3125.
Chinta, S. K., & Kumar, R. (2012). Processing problems of polyester and its remedies. International Journal of New Technology Research, 1(7), 1–19.
DCMSME, (2015). http://dcmsme.gov.in/MSME%20ANNUAL%20REPORT%202014-15_English.pdf. Accessed on 18th August 2022.
Doggett, A. M. (2005). Root cause analysis: A framework for tool selection. Quality Management Journal, 12(4), 34–45.
Durakovic, B., & Basic, H. (2013). Continuous quality improvement in textile processing by statistical process control tools: A case study of medium-sized company. Periodicals of Engineering and Natural Sciences, 1(1).
Ferreira, F. A., Kannan, D., Meidutė-Kavaliauskienė, I., & Vale, I. M. (2022). A sociotechnical approach to vaccine manufacturer selection as part of a global immunization strategy against epidemics and pandemics. Annals of Operations Research, 1–30.
FICCI report, (2022). https://ficci.in/Sedocument/20612/manufacturing-survey.pdf. Access on 18–08–2022.
Fouda, Y. M. (2022). Integral images-based approach for fabric defect detection. Optics & Laser Technology, 147, 107608.
Garza-Reyes, J. A., Salomé Valls, A., Peter Nadeem, S., Anosike, A., & Kumar, V. (2018). A circularity measurement toolkit for manufacturing SMEs. International Journal of Production Research, 1–25.
Geršak, J., & Knez, B. (1991). Reduction in thread strength as a cause of loading in the sewing process. International Journal of Clothing Science and Technology, 3(4), 6–12.
Gijo, E. V., Bhat, S., & Jnanesh, N. A. (2014). Application of Six Sigma methodology in a small-scale foundry industry. International Journal of Lean Six Sigma, 5(2), 193–211.
Gijo, E. V., & Sarkar, A. (2013). Application of Six Sigma to improve the quality of the road for wind turbine installation. The TQM Journal, 25(3), 244–258.
Gijo, E. V., & Scaria, J. (2012). Product design by application of Taguchi’s robust engineering using computer simulation. International Journal of Computer Integrated Manufacturing, 25(9), 761–773.
Govindan, K., Nasr, A. K., Karimi, F., & Mina, H. (2022a). Circular economy adoption barriers: An extended fuzzy best–worst method using fuzzy DEMATEL and Supermatrix structure. Business Strategy and the Environment.
Govindan, K., Nasr, A. K., Saeed Heidary, M., Nosrati-Abarghooee, S., & Mina, H. (2022b). Prioritizing adoption barriers of platforms based on blockchain technology from balanced scorecard perspectives in healthcare industry: A structural approach. International Journal of Production Research, 1–15.
Govindan, K. (2022). Theory Building Through Corporate Social Responsibility 4.0 for Achieving SDGs: A Practical Step Toward Integration of Digitalization With Practice-Based View and Social Good Theory. IEEE Transactions on Engineering Management.
Govindan, K., Dhingra Darbari, J., Kaul, A., & Jha, P. C. (2021). Structural model for analysis of key performance indicators for sustainable manufacturer–supplier collaboration: A grey-decision-making trial and evaluation laboratory-based approach. Business Strategy and the Environment, 30(4), 1702–1722.
Gupta, H., & Barua, M. K. (2016). Identifying enablers of technological innovation for Indian MSMEs using best–worst multi criteria decision making method. Technological Forecasting and Social Change, 107, 69–79.
Gupta, H., & Nanda, T. (2015). A quantitative analysis of the relationship between drivers of innovativeness and performance of MSMEs. International Journal of Technology, Policy and Management, 15(2), 128–157.
Huang, C. F., Chen, K. S., Sheu, S. H., & Sheu, T. S. (2010). Enhancement of axle bearing quality in sewing machines using six sigma. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 224(10), 1581–1590.
Huang, C. C., & Yu, W. H. (2001). Fuzzy neural network approach to classifying dyeing defects. Textile Research Journal, 71(2), 100–104.
Hussain, T., Jamshaid, H., & Sohail, A. (2014). Reducing defects in textile weaving by applying Six Sigma methodology: A case study. International Journal of Six Sigma and Competitive Advantage, 8(2), 95–104.
IBEF (2022) Indian Brand Equity Foundation, https://www.ibef.org/research/case-study/promoting-indian-textile-and-apparel-exports, accessed on 18–08–2022.
Kannan, D. (2021). Sustainable procurement drivers for extended multi-tier context: A multi-theoretical perspective in the Danish supply chain. Transportation Research Part e: Logistics and Transportation Review, 146, 102092.
Kannan, D., Solanki, R., Kaul, A., & Jha, P. C. (2022). Barrier analysis for carbon regulatory environmental policies implementation in manufacturing supply chains to achieve zero carbon. Journal of Cleaner Production, 358, 131910.
Kaushik, P., Khanduja, D., Mittal, K., & Jaglan, P. (2012). A case study. The TQM Journal, 24(1), 4–16.
Keist, C. N. (2015). Quality control and quality assurance in the apparel industry. Garment Manufacturing Technology (pp. 405–426). Wood head Publishing.
Khandker, S., & Sakib, T. U. (2018). Dmaic approach for process improvement: improving fabric width shrinkage of basic t shirt. In: International Conference on Mechanical, Industrial and Energy Engineering, 23–24 December, Khulna, Bangladesh.
Khanna, S., & Kaur, A. (2022). Innovation and technology of knitted apparels. In Advanced Knitting Technology (pp. 95–140).
Kharub, M., & Sharma, R. (2020). An integrated structural model of QMPs, QMS and firm’s performance for competitive positioning in MSMEs. Total Quality Management & Business Excellence, 31(3–4), 312–341.
Khurana, S., Haleem, A., & Mannan, B. (2019). Determinants for integration of sustainability with innovation for Indian manufacturing enterprises: Empirical evidence in MSMEs. Journal of Cleaner Production, 229, 374–386.
Kim, H., Jung, W. K., Park, Y. C., Lee, J. W., & Ahn, S. H. (2022). Broken stitch detection method for sewing operation using CNN feature map and image-processing techniques. Expert Systems with Applications, 188, 116014.
Krishnan, S., K. Mathiyazhagan, and V. R. Sreedharan, 2020. “Developing a hybrid approach for lean six sigma project management: A case application in the reamer manufacturing industry.” IEEE Transactions on Engineering Management.
Kumar, M., Antony, J., & Tiwari, M. K. (2011). Six Sigma implementation framework for SMEs–a roadmap to manage and sustain the change. International Journal of Production Research, 49(18), 5449–5467.
Kyosev, Y., & Kühn, T. (2022). Joining high thickness materials by sewing–first modelling steps of the stitched place. Applied Composite Materials, 29(1), 83–93.
Lande, M., Shrivastava, R. L., & Seth, D. (2016). Critical success factors for Lean Six Sigma in SMEs (small and medium enterprises). The TQM Journal, 28(4), 613–635.
Lee, C. K. H., Ho, G. T. S., Choy, K. L., & Pang, G. K. H. (2014). A RFID-based recursive process mining system for quality assurance in the garment industry. International Journal of Production Research, 52(14), 4216–4238.
Li, R. J. (1999). Fuzzy method in group decision making. Computers and Mathematics with Applications, 38(1), 91–101.
M/O Textile, (2022). http://texmin.nic.in/sites/default/files/AR_Ministry_of_Textiles_%202021-22_Eng.pdf. Access on 18–08–2022.
Mallet, E., & Du, R. (1999). Finite element analysis of sewing process. International Journal of Clothing Science and Technology, 11(1), 19–36.
Manhas, V. K., Gupta, P., & Gupta, H. (2015). Developing and validating critical success factors of TQM implementation in MSMEs of Punjab in India. International Journal of Indian Culture and Business Management, 11(4), 405–421.
Meric, B., & Durmaz, A. (2005). Effect of thread structure and lubrication ratio on seam properties. Indian Journal of Fibre and Textile Research, 30, 273–277.
Modgil, S., Singh, R. K., & Foropon, C. (2020). Quality management in humanitarian operations and disaster relief management: a review and future research directions. Annals of Operations Research, 1–54.
Moin, C. J., Doulah, A. S. U., Ali, M., & Sarwar, F. (2018). Implementation of an operating procedure for quality control at production level in a RMG industry and assessment of quality improvement. The Journal of the Textile Institute, 109(4), 524–535.
MoMSME, (2021). https://msme.gov.in/sites/default/files/MSME-ANNUAL-REPORT-ENGLISH%202020-21.pdf. Access on 14–07–2021.
Nayak, R., & Padhye, R. (2015). Garment Manufacturing Technology. Elsevier.
Nayak, R., & Padhye, R. (2018). Artificial intelligence and its application in the apparel industry. Automation in Garment Manufacturing (pp. 109–138). Woodhead Publishing.
Nethaji, P., Kaliyappan, P., Sathya, R., Hariprakash, S. R., & Prakash, K. (2021). Analysis of six sigma—implementation of DIMAC methodology in foundry industry. Advances in Materials Research (pp. 1213–1222). Springer.
Noor, A., Saeed, M. A., Ullah, T., Uddin, Z., & Ullah Khan, R. M. W. (2022). A review of artificial intelligence applications in apparel industry. The Journal of the Textile Institute, 113(3), 505–514.
Nupur, R., Gandhi, K., Solanki, A., & Jha, P. C. (2018). Six Sigma Implementation in cutting process of apparel industry. Quality IT and Business Operations (pp. 279–295). Springer.
Ortiz-Barrios, M., Cabarcas-Reyes, J., Ishizaka, A., Barbati, M., Jaramillo-Rueda, N., & de Jesús Carrascal-Zambrano, G. (2020). A hybrid fuzzy multi-criteria decision-making model for selecting a sustainable supplier of forklift filters: a case study from the mining industry. Annals of Operations Research, 1–39.
Panwar, A., Jain, R., Rathore, A. P. S., Nepal, B., & Lyons, A. C. (2018). The impact of lean practices on operational performance–an empirical investigation of Indian process industries. Production Planning & Control, 29(2), 158–169.
Parhi, S., Joshi, K., Gunasekaran, A., & Sethuraman, K. (2022). Reflecting on an empirical study of the digitalization initiatives for sustainability on logistics: The concept of Sustainable Logistics 40. Cleaner Logistics and Supply Chain, 100058.
Prashar, A. (2014). Adoption of six sigma DMAIC to reduce cost of poor quality. International Journal of Productivity and Performance Management, 63(1), 103–126.
Prashar, A. (2016). Using shainin DOE for six sigma: An Indian case study. Production Planning & Control, 27(2), 83–101.
Prashar, A. (2018). Toward cycle time reduction in manufacturing SMEs: Proposal and evaluation. Quality Engineering, 30(3), 469–484.
Raj, A., Mukherjee, A. A., de Sousa Jabbour, A. B. L., & Srivastava, S. K. (2022). Supply chain management during and post-COVID-19 pandemic: Mitigation strategies and practical lessons learned. Journal of Business Research, 142, 1125–1139.
Rathinamoorthy, R. (2018). “Sustainable apparel production from recycled fabric waste. Sustainable Innovations in Recycled Textiles (pp. 19–52). Singapore: Springer.
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process (Vol. 4922). RWS Publ.
Shah, P. P., & Shrivastava, R. L. (2013). Identification of performance measures of Lean Six Sigma in small-and medium-sized enterprises: A pilot study. International Journal of Six Sigma and Competitive Advantage, 8(1), 1–21.
Shamsi, H. S. (2014). 5S Conditions and improvement methodology in apparel industry in Pakistan. Journal of Polymer and Textile, 1(2), 15–21.
Sharma, P., Malik, S. C., Gupta, A., & Jha, P. C. (2018). A DMAIC six sigma approach to quality improvement in the anodising stage of the amplifier production process. International Journal of Quality & Reliability Management, 35(9), 1868–1880.
Sharma, R. K., & Kharub, M. (2015). Qualitative and quantitative evaluation of barriers hindering the growth of MSMEs. International Journal of Business Excellence, 8(6), 724–747.
Simegnaw Ahmmed, A. and Ayele, M. (2020). In-Depth Analysis and Defect Reduction for Ethiopian Cotton Spinning Industry Based on TQM Approach. Journal of Engineering, 2020
Singh, M., & Rathi, R. (2019). A structured review of Lean Six Sigma in various industrial sectors. International Journal of Lean Six Sigma, 10(2), 622–664.
Singh, M., Rathi, R., Khanduja, D., Phull, G. S., & Kaswan, M. S. (2020). Six sigma methodology and implementation in indian context: A review-based study. Advances in Intelligent Manufacturing (pp. 1–16). Singapore: Springer.
Soti, A., Shankar, R., & Kaushal, O. P. (2012). Six sigma in manufacturing for micro, small and medium enterprises in India. International Journal of Productivity and Quality Management, 9(1), 61–81.
Sushil. (2020). Interpretive multi-criteria ranking of production systems with ordinal weights and transitive dominance relationships. Annals of Operations Research, 290(1–2), 677–695
Blackburn, R. (Ed.). (2015). Sustainable apparel: Production, processing and recycling. Woodhead Publishing.
Syduzzaman, M., & Golder, A. S. (2015). Apparel analysis for layout planning in sewing section. International Journal of Current Engineering and Technology, 5(3), 1736–1742.
Trimarjoko, A., Saroso, D., Purba, H., Hasibuan, S., Jaqin, C., & Aisyah, S. (2019). Integration of nominal group technique, Shainin system and DMAIC methods to reduce defective products: A case study of tire manufacturing industry in Indonesia. Management Science Letters, 9(13), 2421–2432.
Uddin, S. M., & Rahman, C. M. L. (2014). Minimization of defects in the sewing section of a garment factory through DMAIC methodology of six sigma. Research Journal of Engineering Sciences, 3(9), 21–26.
Ukponmwan, J. O., Mukhopadhyay, A., & Chatterjee, K. N. (2000). Sewing thread. The Textile Institute, 91, 168–171.
Vaid, S. K., Vaid, G., Kaur, S., Kumar, R., & Sidhu, M. S. (2022). Application of multi-criteria decision-making theory with VIKOR-WASPAS-Entropy methods: A case study of silent Genset. Materials Today: Proceedings, 50, 2416–2423.
Varun, S. Appaiah, & C.S Chethan Kumar. (2015). Enhancing the operational effectiveness of sewing segment in garment industry by DMAIC approach. International Research Journal of Engineering and Technology 2 (3), 840-847
Vishwakarma, A., Dangayach, G. S., Meena, M. L., & Gupta, S. (2022). Analysing barriers of sustainable supply chain in apparel & textile sector: A hybrid ISM-MICMAC and DEMATEL approach. Cleaner Logistics and Supply Chain, 5, 100073.
Wong, W. K., Yuen, C. W. M., Fan, D. D., Chan, L. K., & Fung, E. H. K. (2009). Stitching defect detection and classification using wavelet transform and BP neural network. Expert Systems with Applications, 36(2), 3845–3856.
World Economic Forum, (2020). The global competitiveness report. World Economic Forum. https://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2020.pdf. Accessed on 04–09–2022.
Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507.
Yıldırım, F. F., Yavas, A., & Avinc, O. (2020). Printing with sustainable natural dyes and pigments. Sustainability in the textile and apparel industries (pp. 1–35). Cham: Springer.
Zarbakhshnia, N., Govindan, K., Kannan, D., & Goh, M. (2022). Outsourcing logistics operations in circular economy towards to sustainable development goals. Business Strategy and the Environment.
Zhou, B. (2016). Lean principles, practices, and impacts: A study on small and medium-sized enterprises (SMEs). Annals of Operations Research, 241(1–2), 457–474.
Funding
This work was sponsored by Shanghai Pujiang Program (Grant No. 2021PJC066) and Shanghai Soft Science Research Program (Grant No. 22692196400). This research was supported by National Natural Science Foundation of China Project (72072021, 71772032).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Xu, S., Nupur, R., Kannan, D. et al. An integrated fuzzy MCDM approach for manufacturing process improvement in MSMEs. Ann Oper Res 322, 1037–1073 (2023). https://doi.org/10.1007/s10479-022-05093-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-022-05093-5