Enhancing Organizational Performance and Strategic Forecasting Through Business Intelligence Technique

Sonam Srivastava

doi:10.70454/JRICST.2025.20401

Authors

Sonam Srivastava Sikkim Manipal University, Sikkim Author

DOI:

https://doi.org/10.70454/JRICST.2025.20401

Keywords:

Business Intelligence, Sales Forecasting, Data Visualization, Predictive Analytics, Organizational Performance

Abstract

This study examines how Business Intelligence (BI) techniques can enhance organizational performance and improve sales forecasting, using data from a retail store. To find trends, performance problems, and actionable insights, a structured business intelligence approach that combined descriptive, diagnostic, and predictive analytics was used. While diagnostic analysis demonstrated that excessive discounting lowers profitability, descriptive analytics exposed regional variations in sales. Comparative analysis demonstrated that, in contrast to conventional approaches, BI-driven decisions result in increased profits, better discount control, and enhanced overall performance. Power BI, Tableau, Qlik Sense, Looker, and SAP Business Objects are the five top BI tools evaluated in the study, with an emphasis on their scalability, data integration, and visualization capabilities. Furthermore, future sales were predicted using predictive models like ARIMA and Prophet, which aided in inventory management and strategic alignment. Findings emphasize that BI tools are essential for enabling data-driven decisions, improving operational efficiency, and fostering continuous growth. When integrated with cloud and AI technologies, BI supports timely analytics, allowing organizations to remain competitive, agile, and responsive in a dynamic business environment.

Downloads

Download data is not yet available.

References

[1] C. P. Gupta and V. V. R. Kumar, "Predictive Analytics: An AI Tool Enabling Organizations to Take Well-Informed Decisions," in Proc. 2024 Multimedia University Engineering Conference (MECON), Cyberjaya, Malaysia, 2024, pp. 1–5, doi: 10.1109/MECON62796.2024.10776357.

[2] A. Pradeep, "Enhancing Business Intelligence through Business Analytics and Data Mining Techniques using Python," in Proc. 2023 3rd Int. Conf. Intelligent Technologies (CONIT), Hubli, India, 2023, pp. 1–6, doi: 10.1109/CONIT59222.2023.10205708.

[3] M. Semwal, K. Akila, M. Manasa, P. S. Raj, Y. Motukuru and P. Karthik, "Machine Learning-Enabled Business Intelligence For Dynamic Pricing Strategies In E-Commerce," in Proc. 2024 2nd Int. Conf. Disruptive Technologies (ICDT), Greater Noida, India, 2024, pp. 116–120, doi: 10.1109/ICDT61202.2024.10489724.

[4] T. Maaitah, "The role of business intelligence tools in the decision making process and performance," J. Intell. Stud. Bus., vol. 13, no. 1, pp. 43–52, 2023.

[5] A. Gupta and P. Agarwal, "Enhancing Sales Forecasting Accuracy through Integrated Enterprise Resource Planning and Customer Relationship Management using Artificial Intelligence," in Proc. 2024 3rd Int. Conf. Artificial Intelligence for Internet of Things (AIIoT), Vellore, India, 2024, pp. 1–6, doi: 10.1109/AIIoT58432.2024.10574785.

[6] Z. Sun, L. Sun and K. Strang, "Big data analytics services for enhancing business intelligence," J. Comput. Inf. Syst., vol. 58, no. 2, pp. 162–169, 2018.

[7] Y. Niu, L. Ying, J. Yang, M. Bao and C. B. Sivaparthipan, "Organizational business intelligence and decision making using big data analytics," Inf. Process. Manag., vol. 58, no. 6, Art. no. 102725, 2021, doi: 10.1016/j.ipm.2021.102725.

[8] L. T. Becker and E. M. Gould, "Microsoft power BI: Extending Excel to manipulate, analyze, and visualize diverse data," Serials Rev., vol. 45, no. 3, pp. 184–188, 2019, doi: 10.1080/24751448.2018.1497381.

[9] S. Carlisle, "Software: Tableau and Microsoft Power BI," Technol. Archit. Des., vol. 2, no. 2, pp. 256–259, 2018.

[10] P. Labbe, C. Anjos, K. Solanki and J. DiMaso, Hands-On Business Intelligence with Qlik Sense: Implement Self-Service Data Analytics with Insights and Guidance from Qlik Sense Experts. Birmingham, UK: Packt Publ., 2019.

[11] "Evaluation of Top Cloud Service Providers’ BI Tools: A Comparison of Amazon Quicksight, Microsoft Power BI, and Google Looker," Int. J. Comput. Eng. Technol. (IJCET), vol. 15, no. 3, pp. 150–156, May–Jun. 2024, doi: 10.17605/OSF.IO/RKZG4.

[12] V. Singh, SAP Business Intelligence Quick Start Guide: Actionable Business Insights from the SAP BusinessObjects BI Platform. Birmingham, UK: Packt Publ., 2019.

[13] T. Boone, R. Ganeshan, A. Jain and N. R. Sanders, "Forecasting sales in the supply chain: Consumer analytics in the big data era," Int. J. Forecast., vol. 35, no. 1, pp. 170–180, 2019, doi: 10.1016/j.ijforecast.2018.09.003.

[14] C. M. Olszak, "Business intelligence systems for innovative development of organizations," Procedia Comput. Sci., vol. 207, pp. 1754–1762, 2022.

[15] E. Khanmohammadi, H. Safari, M. Zandieh, B. Malmir and E. B. Tirkolaee, "Development of Dynamic Balanced Scorecard Using Case-Based Reasoning Method and Adaptive Neuro-Fuzzy Inference System," IEEE Trans. Eng. Manag., vol. 71, pp. 899–912, 2024, doi: 10.1109/TEM.2022.3140291.

[16] "Store Sales Forecasting Dataset," Kaggle, Apr. 12, 2024. [Online]. Available: https://www.kaggle.com/datasets/tanayatipre/store-sales-forecasting-dataset?resource=download