Explore the role of Decision Support Systems (DSS) in enhancing decision-making processes across different business functions finance, marketing, and supply chain
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Abstract
The paper examines decision support systems (DSS) from both theoretical and practical perspectives, showing how modern DSS especially those enhanced by AI and ML significantly improve decision-making and operational performance. Case studies from major companies and three key sectors (retail, healthcare, manufacturing) demonstrate gains in efficiency, accuracy, and cost reduction. The study concludes that DSS are powerful tools that enhance strategic decisions and organizational competitiveness, despite challenges like data quality and user adoption
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References
Albahri, A. S., Khaleel, Y. L., Habeeb, M. A., Ismael, R. D., Hameed, Q. A., Deveci, M., Homod, R. Z., Albahri, O. S., Alamoodi, A. H., & Alzubaidi, L. (2024). A systematic review of trustworthy artificial intelligence applications in natural disasters. Computers and Electrical Engineering, 118, 109409. https://doi.org/10.1016/j.compeleceng.2024.109409
Alzoubi, S., Amayreh, K. T., Farea, M. M., Baker El-Ebiary, Y. A., Ahmad Saany, S. I., & Bisht, N. (2023). A Review of Effectiveness and Efficiency Methodology of Decision Support System for Selecting Suppliers. 2023 International Conference on Computer Science and Emerging Technologies (CSET), 1–7. https://doi.org/10.1109/CSET58993.2023.10346850
Berkhout, M., Smit, K., & Versendaal, J. (2024). Decision discovery using clinical decision support system decision log data for supporting the nurse decision-making process. BMC Medical Informatics and Decision Making, 24(1), 100. https://doi.org/10.1186/s12911-024-02486-3
Berman, A., de Fine Licht, K., & Carlsson, V. (2024). Trustworthy AI in the public sector: An empirical analysis of a Swedish labor market decision-support system. Technology in Society, 76, 102471. https://doi.org/10.1016/j.techsoc.2024.102471
Chen, X., & Geyer, P. (2022). Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty. Applied Energy, 307, 118240. https://doi.org/10.1016/j.apenergy.2021.118240
Chukuigwe, D. N. (2022). Decision Support Tool and Human Resource Practices in Deposit Money Banks in Rivers State. British Journal of Accounting, Management and Information, 9, 8. https://doi.org/www.bwjournal.org
Crisan, A., Juravle, A., & Bancila, R. (2024). A BIM Enabled Workflow for Rehabilitation of Heritage Steel Bridges. https://doi.org/10.20944/preprints202412.1120.v1
Fuentes-Peñailillo, F., Gutter, K., Vega, R., & Silva, G. C. (2024). Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management. Journal of Sensor and Actuator Networks, 13(4). https://doi.org/10.3390/jsan13040039
Giannakopoulos, N. T., Terzi, M. C., Sakas, D. P., Kanellos, N., Toudas, K. S., & Migkos, S. P. (2024). Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling. Information, 15(2), 67. https://doi.org/10.3390/info15020067
Gil, Y., Garijo, D., Khider, D., Knoblock, C. A., Ratnakar, V., Osorio, M., Vargas, H., Pham, M., Pujara, J., Shbita, B., Vu, B., Chiang, Y.-Y., Feldman, D., Lin, Y., Song, H., Kumar, V., Khandelwal, A., Steinbach, M., Tayal, K., … Shu, L. (2021). Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making. ACM Trans. Interact. Intell. Syst., 11(2), 11:1–11:49. https://doi.org/10.1145/3453172
Hamrouni, B., Bourouis, A., Korichi, A., & Brahmi, M. (2021). Explainable Ontology-Based Intelligent Decision Support System for Business Model Design and Sustainability. Sustainability, 13(17), 9819. https://doi.org/10.3390/su13179819
Hossain, M. A., Tiwari, A., Saha, S., Ghimire, A., Imran, M. A. U., & Khatoon, R. (2024). Applying the Technology Acceptance Model (TAM) in Information Technology System to Evaluate the Adoption of Decision Support System. Journal of Computer and Communications, 12(8). https://doi.org/10.4236/jcc.2024.128015
Maaitah, T. (2023). The Role of Business Intelligence Tools in the Decision Making Process and Performance. Journal of Intelligence Studies in Business, 13(1). https://doi.org/10.37380/jisib.v13i1.990
Pillai, A. S. (2023). AI-enabled Hospital Management Systems for Modern Healthcare: An Analysis of System Components and Interdependencies. Journal of Advanced Analytics in Healthcare Management, 7(1).
Psarommatis, F., & Kiritsis, D. (2022). A hybrid Decision Support System for automating decision making in the event of defects in the era of Zero Defect Manufacturing. Journal of Industrial Information Integration, 26, 100263. https://doi.org/10.1016/j.jii.2021.100263
Qiu, K., Chen, J., Ashraf, S., & Shahid, T. (2024). Strategic Decision Support System with Probabilistic Linguistic Term Sets: Extended CRADIS Approach for Supply Chain Risk Management in Sports Industry. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3416391
Ruiz, M., Orta, E., & Sánchez, J. (2024). A simulation-based approach for decision-support in healthcare processes. Simulation Modelling Practice and Theory, 136, 102983. https://doi.org/10.1016/j.simpat.2024.102983
Sadeghi, R. K., Ojha, D., Kaur, P., Mahto, R. V., & Dhir, A. (2024). Explainable artificial intelligence and agile decision-making in supply chain cyber resilience. Decision Support Systems, 180, 114194. https://doi.org/10.1016/j.dss.2024.114194
Sarker, I. H. (2021). Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective. SN Computer Science, 2(5), 377. https://doi.org/10.1007/s42979-021-00765-8
Shahcheraghian, A., Ilinca, A., & Sommerfeldt, N. (2025). K-means and agglomerative clustering for source-load mapping in distributed district heating planning. Energy Conversion and Management: X, 25, 100860. https://doi.org/10.1016/j.ecmx.2024.100860
Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126. https://doi.org/10.1016/S0167-9236(01)00139-7
Spoladore, D., Tosi, M., & Lorenzini, E. C. (2024). Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artificial Intelligence in Medicine, 151, 102859. https://doi.org/10.1016/j.artmed.2024.102859