Towards developing a decision support system for the industry 4.0

Authors

  • Fudhah Ateeq AlSelami University of Jeddah

DOI:

https://doi.org/10.23925/2179-3565.2025v16i3p41-53

Keywords:

Industry, Fourth Revolution, Decision Support System, Microsoft Power BI

Abstract

The ability to swiftly modify and respond to developments in the business environment is essential for firms to be capable of functioning in the extremely competitive surroundings of the contemporary socio-economic context. The development of Industry 4.0 has given businesses the opportunity to become more competitive. The process of digital transformation of enterprises is referred to as Industry 4.0. It transforms the nature of business of a company drastically. The purpose of the study is to determine whether the data visualization techniques promote the decision support systems. The major aim of the research will be to investigate the features of the microsoft power BI. The research proposed the ideology of implementing the data visualization concept to the decision support system. A dataset of an enterprise was taken to implement the statistical analysis and to find the correlation among the variables. The research found the correlation of the variables along with the data visualization charts where the complex data is generated as bar graphs and it helps the stakeholders to make decisions. This research will cover the building of a business intelligence system utilizing Microsoft Power BI and the outcomes that were attained.

Author Biography

Fudhah Ateeq AlSelami, University of Jeddah

Department of Management Information Systems, College of Business, Al Kamel Branch, Governorate of Jeddah

References

Abualigah, L., Abd Elaziz, M., Sumari, P., Geem, Z. W., & Gandomi, A. H. (2022). Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Systems with Applications, 191, 116158. https://doi.org/10.1016/j.eswa.2021.116158

Antony, J., Sony, M., & McDermott, O. (2023). Conceptualizing Industry 4.0 readiness model dimensions: An exploratory sequential mixed-method study. The TQM Journal, 35(2), 577-596. https://doi.org/10.1108/TQM-06-2021-0180

Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics, 10(7), 828. https://doi.org/10.3390/electronics10070828

Doltsinis, S., Ferreira, P., Mabkhot, M. M., & Lohse, N. (2020). A Decision Support System for rapid ramp-up of industry 4.0 enabled production systems. Computers in Industry, 116, 103190. https://doi.org/10.1016/j.compind.2020.103190

Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of cleaner production, 252, 119869. https://doi.org/10.1016/j.jclepro.2019.119869

Kabadurmus, O., Kayikci, Y., Demir, S., & Koc, B. (2023). A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks. Socio-Economic Planning Sciences, 85, 101417.

Marques, R., Moura, A., & Teixeira, L. (2020, August). Decision support system for the industry 4.0 environment: Design and development of a business intelligence tool. In Proceedings of the International Conference on Industrial Engineering and Operations Management (pp. 1613-1624). https://www.ieomsociety.org/detroit2020/papers/385.pdf

Miller, M. A., & Ellis, J. M. (2016). The NAICS code selection process and small business participation. Naval Postgraduate School Monterey CA Monterey United States.

Oehlert, C., Schulz, E., & Parker, A. (2022). NAICS Code Prediction Using Supervised Methods. Statistics and Public Policy, 9(1), 58-66. https://doi.org/10.1080/2330443X.2022.2033654

Rosati, R., Romeo, L., Cecchini, G., Tonetto, F., Viti, P., Mancini, A., & Frontoni, E. (2023). From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in Industry 4.0. Journal of Intelligent Manufacturing, 34(1), 107-121. https://doi.org/10.1007/s10845-022-01960-x

Shwab, K. (2016, January). The Fourth Industrial Revolution: what it means, how to respond. In World Economic Forum: Cologny, Switzerland.

Sony, M., & Naik, S. (2020). Key ingredients for evaluating Industry 4.0 readiness for organizations: a literature review. Benchmarking: An International Journal, 27(7), 2213-2232.

Trstenjak, M., Opetuk, T., Cajner, H., & Hegedić, M. (2022). Industry 4.0 readiness calculation—transitional strategy definition by decision support systems. Sensors, 22(3), 1185.

Zhang, M., Li, W., Zhang, L., Jin, H., Mu, Y., & Wang, L. (2023). A Pearson correlation-based adaptive variable grouping method for large-scale multi-objective optimization. Information Sciences, 639, 118737. https://doi.org/10.1016/j.ins.2023.02.055

Published

2025-10-22