To diagnose industry 4.0 by maturity model: the case of Moroccan clothing industry
DOI:
https://doi.org/10.56294/dm2023137Keywords:
Industry 4.0, Maturity Model, Self-Assessment Model, Clothing IndustryAbstract
In 2011, the German government launched the visionary initiative known as Industry 4.0, with the goal of positioning itself at the forefront of cutting-edge manufacturing and the shift towards digital transformation. In the wake of this transformative wave, numerous manufacturers are continuously exploring avenues to bolster their capabilities and remain competitive in the market. This empirical study adopts a maturity model inspired by the Economic Development Board's Singapore Smart Industry Readiness Index. The model empowers companies to perform self-assessments, facilitating a systematic and comprehensive alignment with the principles of Industry 4.0. The research delves into the assessment of Industry 4.0 maturity within the Moroccan clothing industry, examining clustering index factors and the influence of key factors on companies' self-assessment. The results classify 252 Moroccan Clothing enterprises into three distinct categories, highlighting a strong positive correlation among process, technology, and organization. Significantly, a majority of the 252 companies evaluated using the maturity model still appear to be in early stages or partially mature, necessitating significant improvements and a reevaluation of their Industry 4.0 transformation strategies. Conclusively, the Singapore Smart Industry Readiness Index proves to be a valuable tool for conducting self-assessments within Moroccan-based enterprises. These findings offer practical guidance for both industry practitioners and researchers seeking to navigate the complexities of Industry 4.0 maturity and grouping
References
1. Kang, H. S., J. Y. Lee, S. S. Choi, H. Kim, J. H. Park, J. Y. Son, B. H. Kim, and S. D. Noh. 2016. “Smart Manufacturing: Past Research, Present Findings, and Future Directions.” International Journal of Precision Engineering and Manufacturing-Green Technology 3 (1): 111–128. doi:10.1007/ s40684-016-0015-5.
2. Pereira, A. C., & Romero, F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia manufacturing, 13, 1206-1214.
3. Rahamaddulla, S. R. B., Leman, Z., Baharudin, B. H. T. B., & Ahmad, S. A. (2021). Conceptualizing smart manufacturing readiness-maturity model for small and medium enterprise (sme) in malaysia. Sustainability, 13(17), 9793.
4. Kassa, M.K., Wube, H.D. (2023). Benefits and Challenges of Industry 4.0 in African Emerging Economies. In: Girma Debelee, T., Ibenthal, A., Schwenker, F. (eds) Pan-African Conference on Artificial Intelligence. PanAfriCon AI 2022. Communications in Computer and Information Science, vol 1800. Springer, Cham. https://doi.org/10.1007/978-3-031-31327-1_15.
5. Auktor, Georgeta Vidican (2022): The opportunities and challenges of Industry 4.0 for industrial development: A case study of Morocco's automotive and garment sectors, Discussion Paper, No. 2/2022, ISBN 978-3-96021-179-2, Deutsches Institut für Entwicklungspolitik (DIE), Bonn, https://doi.org/10.23661/dp2.2022
6. Wirawan, M. V., & Melinda, T. THE INFLUENCE OF SERVICE QUALITY AND PRICE TOWARDS PURCHASING DECISION FOR ANUGERAH SOUND SYSTEM SERVICE EMPLOYMENT. HELD BY CIPUTRA UNIVERSITY COLLABORATED WITH, 84.)
7. He, X., Wu, X., Croasdell, D., & Zhao, Y. (2022). Dynamic capability, ambidexterity and social network—empirical evidence from SMEs in China. Journal of Small Business and Enterprise Development, 29(6), 958-974.
8. Singapore Economic Development Board. 2017. "Singapore Smart Industry Readiness Index: Catalyzing the Transformation of Manufacturing". 1–46. Accessed 20 October 2023. https://www.edb.gov.sg/en/news-andresources/news/advanced-manufacturing-release.html
9. Shukla, M., & Shankar, R. (2023). Readiness assessment for smart manufacturing system implementation: multiple case of Indian small and medium enterprises. International Journal of Computer Integrated Manufacturing, 1-19.
10. Bruna Felippes, Isaac da Silva, Sanderson Barbalho, Tobias Adam, Ina Heine & Robert Schmitt (2022) 3D-CUBE readiness model for industry 4.0: technological, organizational, and process maturity enablers, Production & Manufacturing Research, 10:1, 875-937, DOI: 10.1080/21693277.2022.2135628.
11. Singapure, E. (2020). THE SMART INDUSTRY READINESS INDEX Catalysing the transformation of manufacturing. EDB Singapure. Recuperado de: https://www. edb. gov. sg/content/dam/edb-en/aboutedb/media-releases/news/the-smart-industry-readiness-index/the-sgsmart-industry-readiness-index-whitepaper, 20(1).
12. Chrissis, M. B., Wemyss, G., Goldenson, D., Konrad, M., Smith, K., & CARNEGIE-MELLON UNIV PITTSBURGH PA SOFTWARE ENGINEERING INST. (2003). CMMI (Trademark) Interpretive Guidance Project: Preliminary Report.
13. Schuh, G., Anderl, R., Gausemeier, J., Ten Hompel, M., & Wahlster, W. (Eds.). (2017). Industrie 4.0 maturity index: die digitale transformation von unternehmen gestalten. Herbert Utz Verlag.
14. De Carolis, A., Macchi, M., Negri, E., & Terzi, S. (2017). A maturity model for assessing the digital readiness of manufacturing companies. In Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing: IFIP WG 5.7 International Conference, APMS 2017, Hamburg, Germany, September 3-7, 2017, Proceedings, Part I (pp. 13-20). Springer International Publishing.
15. Gamache, S., Abdul-Nour, G., & Baril, C. (2020). Evaluation of the influence parameters of Industry 4.0 and their impact on the Quebec manufacturing SMEs: The first findings. Cogent Engineering, 7(1), 1771818.
16. Reisenhofer, E., Picciotto, A., & Li, D. (1995). A factor analysis approach to the study of the eutrophication of a shallow, temperate lake (San Daniele, North Eastern Italy). Analytica Chimica Acta, 306(1), 99-106.
17. Jamieson, J. (2004). Analysis of covariance (ANCOVA) with difference scores. International Journal of Psychophysiology, 52(3), 277-283.
18. PwC. 2018. “Global Digital Operations Survey.” Accessed 22 October 2023. https://www.pwc.com/gx/en/industries/industry-4-0.html
19. Cronbach, L. 1951. “Coefficient Alpha and the Internal Structure of Tests.” Psychomerika 16: 297–334. doi:10.1007/ BF02310555.
20. Rahman, S.M.T.; Kabir, A. Potential cluster regions for manufacturing small and medium enterprises in khulna city of bangladesh: A spatial examination. Int. J. Recent Technol. Eng. 2019, 8, 980–986.
21. Rodríguez, A.J.G.; Barón, N.J.; Martínez, J.M.G. Validity of dynamic capabilities in the operation based on new sustainability narratives on nature tourism SMEs and clusters. Sustainability 2020, 12, 1004. / Nestle, V.; Täube, F.A.; Heidenreich, S.; Bogers, M. Establishing open innovation culture in cluster initiatives: The role of trust and information asymmetry. Technol. Forecast. Soc. Chang. 2019, 146, 563–572.
22. Pelletier, C.; Cloutier, L.M. Conceptualising digital transformation in SMEs: An ecosystemic perspective. J. Small Bus. Enterp. Dev. 2019, 26, 855–876.
23. Jami Pour, M.; Asarian, M. Strategic orientations, knowledge management (KM) and business performance: An exploratory study in SMEs using clustering analysis. Kybernetes 2019, 48, 1942–1964.
24. Cicea, C.; Popa, I.; Marinescu, C.; S, tefan, S.C. Determinants of SMEs’ performance: Evidence from European countries. Econ. Res. Ekon. Istraz. 2019, 32, 1602–1620.
25. Dubravska, M.; Sira, E. The analysis of the factors influencing the international trade of the slovak republic. In Proceedings of the 2nd Global Conference on Business, Economics, Management and Tourism, Prague, Czech Republic, 30–31 October 2014; Iacob, A.I., Ed.; Elsevier: Amsterdam, The Netherlands, 2015; Volume 23, pp. 1210–1216.
26. Woods, J.; Galbraith, B.; Hewitt-Dundas, N. Network centrality and open innovation: A social network analysis of an SME manufacturing cluster. IEEE Trans. Eng. Manag. 2019, 1–14.
27. Viloria, A.; Lezama, O.B.P. Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 2019, 151, 1201–1206.
28. Pessl, E.; Sorko, S.R.; Mayer, B. Roadmap industry 4.0—Implementation guideline for enterprises. Int. J. Sci. Technol. Soc. 2020, 5, 1728–1743.
29. Turkes, M.C.; Oncioiu, I.; Aslam, H.D.; Marin-Pantelescu, A.; Topor, D.I.; Capusneanu, S. Drivers and barriers in using industry 4.0: A perspective of SMEs in Romania. Processes 2019, 7, 153.
30. Fox, S. Moveable production systems for sustainable development and trade: Limitations, opportunities and barriers. Sustainability 2019, 11, 5154.
31. Brozzi, R.; D’Amico, R.D.; Pasetti Monizza, G.; Marcher, C.; Riedl, M.; Matt, D. Design of Self-Assessment Tools to Measure Industry 4.0 Readiness. A Methodological Approach for Craftsmanship SMEs; Springer: New York, NY, USA, 2018; Volume 540, ISBN 9783030016135, ISSN 18684238.
32. Dubrova, T.A.; Ermolina, A.A.; Esenin, M.A. Innovative activities of SMEs in Russia: Constraints and growth factors. Int. J. Econ. Bus. Adm. 2019, 7, 26–40.
33. Statista Worldwide Semiannual Small and Medium Business Spending Guide. Available online: https://www.statista.com/statistics/760799/worldwide-small-medium-business-it-spending/ (accessed on 10 November 2023).
34. IDC Worldwide Semiannual Small and Medium Business Spending Guide. Available online: https://www.statista.com/statistics/800684/worldwide-small-medium-business-it-spending-growth/ (accessed on 20 November 2023).
35. Gallab, M., Bouloiz, H., Kebe, S.A. et al. Opportunities and challenges of the industry 4.0 in industrial companies: a survey on Moroccan firms. J. Ind. Bus. Econ. 48, 413–439 (2021). https://doi.org/10.1007/s40812-021-00190-1.
36. Computer Economics Robotic Process Automation Adoption Trends and Customer Experience. Available online: https://www.statista.com/statistics/1017027/worldwide-robotic-process-automation-adoption-investment-rates-organization-size/ (accessed on 10 November 2023)
37. El Hamdi, S., Oudani, M., Abouabdellah, A. (2020). Morocco’s Readiness to Industry 4.0. In: Bouhlel, M., Rovetta, S. (eds) Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.1. SETIT 2018. Smart Innovation, Systems and Technologies, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-030-21005-2_44.
38. KHOURIBA, K., & YAOUHI, M. (2021). Le degré d’intégration des sous-traitants marocains dans le secteur du textile-habillement: est-il vraiment une sous-traitance de capacité?. International Journal of Financial Accountability, Economics, Management, and Auditing (IJFAEMA), 3(3), 302-321.
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Copyright (c) 2023 Younes JAMOULI , Samir TETOUANI, Omar CHERKAOUI , Aziz SOULHI (Author)
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