Improving sustainable management through an AI-based strategic transition: A policy that promotes firms to use AI advancements in the Industry 4.0 era

Amélioration de la gestion durable par une transition stratégique basée sur l'IA : Une politique qui encourage les entreprises à utiliser les avancées de l'IA à l'ère de l'industrie 4.0

Authors

Keywords:

Artificial intelligence, Strategy, Transition, Industry 4.0, Maturity Model, Management

Abstract

Due to the severe effects of the Fourth Industrial Revolution and environmental disasters or subsequent pandemics, the digital infrastructures of businesses need drastic and constant changes. As a result, many companies are actively implementing innovative digital strategies to accelerate digital adjustments throughout the scale of their organizational structures. Since artificial intelligence (AI) has demonstrated its success in a wide range of fields, including both business processes and daily activities, it has gained extreme interest in corporate ecosystems. Business management techniques could be transformed by the integration of AI for more productivity, cost-effectiveness, and overall efficiency. AI is strategically incorporated into businesses to help them engage with their target consumers more effectively, giving them an edge over their digital competitors. Additionally, AI has the potential to revolutionize corporate operations, enabling the creation of novel ideas, the completion of complex tasks, and the acceleration of significant economic growth. To achieve thorough optimization, it is necessary to carefully adjust AI integration tactics to the unique requirements at each stage of development. In this study, we present a strong strategy to hasten the conception, alignment, and prioritizing of developing activities, supporting a smooth transition to more effective and sustainable management methods.

Downloads

Download data is not yet available.

References

Bokolo, A. Jnr (2023) Distributed Ledger and Decentralised Technology Adoption for Smart Digital Transition in Collaborative Enterprise, Enterprise Information Systems, 17:4, DOI: 10.1080/17517575.2021.1989494

Zhang, H. Pathways to carbon neutrality in major exporting countries: the threshold effect of the digital transition. Environ Sci Pollut Res 30, 7522–7542 (2023). https://doi.org/10.1007/s11356-022-22592-x

Hongxia Peng. La transition numérique des attributs managériaux : espace, temps et cognition. ISTE Group. , 2023, Collection TC. ⟨hal-03979636⟩

Allioui, H., Allioui, A. (2022). The Financial Sphere in the Era of Covid-19: Trends and Perspectives of Artificial Intelligence. In: Mansour, N., M. Bujosa Vadell, L. (eds) Finance, Law, and the Crisis of COVID-19. Contributions to Management Science. Springer, Cham. https://doi.org/10.1007/978-3-030-89416-0_3

Martin, C., Towill, D.R.: Supply chain migration from lean and functional to agile and customized. Supply Chain Manag. 5, 206–213 (2000)

Brown, S., Bessant, J.: The manufacturing strategy capabilities links in mass customization and agile manufacturing–an exploratory study. Int. J. Oper. Prod. Manag. 23, 707–730 (2003)

Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., Kuhlenkötter, B.: Learning factory modules for smart factories in Industrie 4.0. Procedia CIRP 54, 113–118 (2016)

Barreto, L., Amaral, A., Pereira, T.: Industry 4.0 implications in logistics: an overview. Procedia Manuf. 13, 1245–1252 (2017)

Elkington, J.: Cannibals with Forks: The Triple Bottom (1998)

Womack, D.T., Jones, J.P.: Lean thinking—banish waste and create wealth in your corporation. J. Oper. Res. Soc. 48(11), 1148 (1997)

O’Reilly, M.L., Tushman, C.A.: Organizational ambidexterity: past, present, and future. Acad. Manag. Perspect. 27(4), 324–338 (2013)

Alavi, S., Abd, D., Wahab, N.M., Shirani, B.A.: Organic structure and organizational learning as the main antecedents of workforce agility. Int. J. Prod. Res. 52(21), 6273–6295 (2014)

Kagermann, H., Wahlster, W., Helbig, J.: Recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industrie 4.0 Working Group (2013). https://www.din.de

Lee, J., Bagheri, B., Kao, H.A.: A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23 (2015)

Hermann, M., Pentek, T., Otto, B.: Design principles for Industrie 4.0 scenarios. In: IEEE 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928–3937 (2016)

Hofmann, E., Rüsch, M.: Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind. 89, 23–34 (2017)

Herberer, S., Lau, L.K., Behrendt, F.: Development of an Industrie 4.0 maturity index for small and medium-sized enterprises. In: IESM Conference, Saarbrucken (2017)

Chrissis, M.B., Konrad, M., Shrum, S.: CMMI 2e Guide des bonnes pratiques pour l’amélioration des processus. CMMI (r) pour le développement, version 1.2. SEI, Pearson Education France (2008)

Costantino, F., Gravio, G.D., Shaban, A.: Multi-criteria logistics distribution network design for mass customization. Int. J. Appl. Decis. Sci. 7(2), 151–167 (2014)

Windt, K., Felix, B., Thorsten, P.: Autonomy in production logistics: identification, characterization, and application. Int. J. Robot. Comput. Integr. Manuf. 24(4), 572–578 (2008)

Vaidya, S., Ambad, P., Bhosle, S.: Industry 4.0–a glimpse. Procedia Manuf. 20(1), 233–238 (2018)

Tjahjono, B., et al.: What does Industry 4.0 mean to supply chain? Procedia Manuf. 13, 1175–1182 (2017)

Strandhagen, J.O., Vallandingham, L.R., Fragapane, G., Strandhagen, J.W., Stangeland, A.B.H., Sharma, N.: Logistics 4.0 and emerging sustainable business models. Advances in Manufacturing 5(4), 359–369 (2017)

Timm, J.I., Fabian, L.: Logistics 4.0 - a challenge for simulation. In: Proceedings of the 2015 Winter Simulation Conference, pp. 3118–3119 (2015)

Blanchet, M., Bergerried, R.: Industrie 4.0-les leviers de la transformation. Gimélec (2014)

Hankel, M., Rexroth, B.: The Reference Architectural Model Industrie 4.0 (Rami 4.0). ZVEI, 2, 2 (2015)

Schumacher, A., Erol, S., Sihn, W.: A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP 52, 161–166 (2016)

Johnson, C.N.: The benefits of PDCA. Qual. Prog. 35(5), 120 (2002)

Tobias, M., Joachim, M., Eberhard, A.: Value stream mapping 4.0: a holistic examination of the value stream and information logistics in production. CIRP Ann. 66(1), 413–416 (2017)

Fernández, J., Puerto, F.R.: Pareto-optimality in classical inventory problems. NRI J. 45(1), 83–98 (1998)

Wang, L.-J., Guo, M., Sawada, K., Lin, J., Zhang, J.: A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci. J. 20(1), 117–136 (2016). https://doi.org/10.1007/s12303-015-0026-1

Chen, W., Xie, X., Peng, J., Wang, J., Duan, Z., Hong, H.: GIS-based landslide susceptibility modeling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomatics Nat. Hazards Risk 8(2), 950–973 (2017)

Habba, B., Allioui, A. and Farhane, F. (2022), "Moroccan family businesses professionalization: benefits and challenges", Journal of Family Business Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFBM-10-2022-0125

Allioui, A., Habba, B. and Berrada El Azizi, T. (2022), "Investment policy of Moroccan family businesses in times of crisis: the role of cultural logics, family reputation, and imitation effect", Journal of Family Business Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JFBM-04-2022-0057.

Downloads

Published

2024-11-18

How to Cite

ALLIOUI, H., & Mourdi, Y. (2024). Improving sustainable management through an AI-based strategic transition: A policy that promotes firms to use AI advancements in the Industry 4.0 era: Amélioration de la gestion durable par une transition stratégique basée sur l’IA : Une politique qui encourage les entreprises à utiliser les avancées de l’IA à l’ère de l’industrie 4.0. International Journal of Computer Engineering and Data Science (IJCEDS), 3(4), 1–20. Retrieved from https://ijceds.com/ijceds/article/view/67