American Journal of Business and Operations Research

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https://doi.org/10.54216/AJBOR

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2692-2967ISSN (Online) 2770-0216ISSN (Print)

Volume 11 , Issue 1 , PP: 79-88, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Data-Driven Decision Support Systems for Business Process Improvement

Betul Aktas 1 *

  • 1 Higher Vocational School, Cag University, Mersin, Turkey - (betulaktas@cag.edu.tr)
  • Doi: https://doi.org/10.54216/AJBOR.110109

    Received: July 27, 2023 Revised: October 18, 2023 Accepted: December 11, 2023
    Abstract

    The accessibility of data is altering how businesses make decisions at different levels. Scholars and professionals are investigating the ways in which Business Process suppliers can profit from the availability and application of data, particularly in relation to decision-making concerning service provision. Business Process Improvement is one of the applications that is anticipated to gain the most from the accessibility of information. Suppliers of services can avoid failures by making prompt and well-informed decisions based on the evaluation of the resource's health state. Despite this, providing data-driven BPI services is not simple, and providers must set up their systems to correctly gather, process, and utilize past and current data. This study introduces a data-driven business intelligence framework to provide use full insights for improving business process activities. This framework offers a set of visualization tools that help interpret the relation between different factors that can improve the management of different business processes. Moreover, our framework provides successful integration of random forests to allow predictive modeling of sales, profits, and discounts across different regions.

    Keywords :

    Business Intelligence , Decision Making , Data-driven Intelligence , Business Process.

    References

    [1]    Power, D. J. (2008). Understanding data-driven decision support systems. Information Systems Management25(2), 149-154.

    [2]    Fleig, C. (2020). Design of data-driven decision support systems for business process standardization (Doctoral dissertation, Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2020).

    [3]    Rejikumar, G., Aswathy Asokan, A., & Sreedharan, V. R. (2020). Impact of data-driven decision-making in Lean Six Sigma: an empirical analysis. Total Quality Management & Business Excellence31(3-4), 279-296.

    [4]    Hedgebeth, D. (2007). Data‐driven decision making for the enterprise: an overview of business intelligence applications. Vine37(4), 414-420.

    [5]    Mandinach, E. B., Honey, M., & Light, D. (2006, April). A theoretical framework for data-driven decision making. In annual meeting of the American Educational Research Association, San Francisco, CA.

    [6]    Abd Rahman, M. S. B., Mohamad, E., & Abdul Rahman, A. A. B. (2021). Development of IoT—enabled data analytics enhance decision support system for lean manufacturing process improvement. Concurrent Engineering29(3), 208-220.

    [7]    Antomarioni, S., Lucantoni, L., Ciarapica, F. E., & Bevilacqua, M. (2021). Data-driven decision support system for managing item allocation in an ASRS: A framework development and a case study. Expert Systems with Applications185, 115622.

    [8]    Diván, M. J. (2017, December). Data-driven decision making. In 2017 international conference on Infocom technologies and unmanned systems (trends and future directions)(ICTUS) (pp. 50-56). IEEE.

    [9]    Tripathi, V., Chattopadhyaya, S., Mukhopadhyay, A. K., Saraswat, S., Sharma, S., Li, C., & Rajkumar, S. (2022). Development of a data-driven decision-making system using lean and smart manufacturing concept in industry 4.0: A case study. Mathematical Problems in Engineering2022.

    [10] Polenghi, A., Roda, I., Macchi, M., & Pozzetti, A. (2023). A methodology to boost data-driven decision-making process for a modern maintenance practice. Production Planning & Control34(14), 1333-1349.

    [11] Hannila, H., Kuula, S., Harkonen, J., & Haapasalo, H. (2022). Digitalisation of a company decision-making system: a concept for data-driven and fact-based product portfolio management. Journal of Decision Systems31(3), 258-279.

    [12] Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics10(7), 828.

    [13] Hamoud, A. K., Marwah, K. H., Alhilfi, Z., & Sabr, R. H. (2021). Implementing data-driven decision support system based on independent educational data mart. International Journal of Electrical and Computer Engineering11(6), 5301.

    [14] Clark, A., Zhuravleva, N. A., Siekelova, A., & Michalikova, K. F. (2020). Industrial artificial intelligence, business process optimization, and big data-driven decision-making processes in cyber-physical system-based smart factories. Journal of Self-Governance and Management Economics8(2), 28-34.

    [15] Troisi, O., Maione, G., Grimaldi, M., & Loia, F. (2020). Growth hacking: Insights on data-driven decision-making from three firms. Industrial Marketing Management90, 538-557.

    [16] Power, D. J. (2002). Decision support systems: concepts and resources for managers. Quorum Books.

    [17] Kratsch, W., Manderscheid, J., Reißner, D., & Röglinger, M. (2017). Data-driven process prioritization in process networks. Decision Support Systems100, 27-40.

    [18] Zhu, Y. (2018). A data driven educational decision support system. International Journal of Emerging Technologies in Learning (Online)13(11), 4.

    [19] Burstein, F., W Holsapple, C., & Power, D. J. (2008). Decision support systems: a historical overview. Handbook on decision support systems 1: Basic themes, 121-140.

    [20] Wu, L., Li, Z., & AbouRizk, S. (2022). Automating Common Data Integration for Improved Data-Driven Decision-Support System in Industrial Construction. Journal of Computing in Civil Engineering36(2), 04021037.

    Cite This Article As :
    Aktas, Betul. Data-Driven Decision Support Systems for Business Process Improvement. American Journal of Business and Operations Research, vol. , no. , 2024, pp. 79-88. DOI: https://doi.org/10.54216/AJBOR.110109
    Aktas, B. (2024). Data-Driven Decision Support Systems for Business Process Improvement. American Journal of Business and Operations Research, (), 79-88. DOI: https://doi.org/10.54216/AJBOR.110109
    Aktas, Betul. Data-Driven Decision Support Systems for Business Process Improvement. American Journal of Business and Operations Research , no. (2024): 79-88. DOI: https://doi.org/10.54216/AJBOR.110109
    Aktas, B. (2024) . Data-Driven Decision Support Systems for Business Process Improvement. American Journal of Business and Operations Research , () , 79-88 . DOI: https://doi.org/10.54216/AJBOR.110109
    Aktas B. [2024]. Data-Driven Decision Support Systems for Business Process Improvement. American Journal of Business and Operations Research. (): 79-88. DOI: https://doi.org/10.54216/AJBOR.110109
    Aktas, B. "Data-Driven Decision Support Systems for Business Process Improvement," American Journal of Business and Operations Research, vol. , no. , pp. 79-88, 2024. DOI: https://doi.org/10.54216/AJBOR.110109