A BUSINESS INTELLIGENCE SIMULATOR FOR SALES OPTIMIZATION USING A MACHINE LEARNING DDS FRAMEWORK

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Published: 2026-04-28
DOI: https://doi.org/10.46281/bjmsr.v11i2.2860
Keywords:
Smart Business Intelligence, Sales Optimisation, and Explainable Machine Learning, Decision Support Systems, Random Forest, Retail Analytics, Predictive Analytics, Diagnostic Analytics

Main Article Content

Nabil El Kadhi
Associate Professor, VPAA and Computer Science Department, Applied Science University, Manama, Bahrain
https://orcid.org/0000-0002-8139-320X
Maruf Fatima Sadriwala
Assistant Professor, Pacific University, Faculty of Commerce and Management, India
https://orcid.org/0000-0002-1383-291X
Kaneez Fatima Sadriwala
Associate Professor, College of Economics, Management and Information Systems, University of Nizwa, Sultanate of Oman
https://orcid.org/0000-0002-5769-7967
Rifat O. Shannak
Professor, Business School, Jordan University, Amman, Jordan
https://orcid.org/0000-0003-1058-679X
Marwan Alshar’e
Associate Professor, Faculty of Computing and IT, Sohar University, Sohar, Sultanate of Oman
https://orcid.org/0000-0003-1058-679X

Abstract

Existing business intelligence (BI) systems are predominantly descriptive and offer limited support for actionable decision-making in sales optimization, leading to a disconnect between data analytics and operational management. This study examines the development of a unified BI simulator that integrates machine learning, Pareto-based diagnostics, and what-if simulation to support data-driven sales optimization. The research employs an empirical pizza sales dataset from Kaggle, consisting of transactional, temporal, and operational variables. It applies multiple machine learning algorithms for comparative evaluation, along with Pareto analysis to identify key product contributors and a simulation engine to assess the sensitivity of low-performing products to operational changes. The results reveal that the Random Forest model outperforms other models, achieving 97.5% accuracy, an F1-score of 0.941, and an AUC of 0.996. Pareto analysis shows that approximately 30% of product categories account for nearly 80% of total sales, while a small proportion of products consistently exhibit low demand. Additionally, simulation analysis indicates that variations in operational factors, such as delivery efficiency, delivery distance, and product complexity, result in sales variability of up to 12.8%. The findings of this study suggest that the integration of predictive, diagnostic, and simulation-based analytics within a unified BI framework enables precise identification of key sales drivers and quantitatively measures the responsiveness of low-performing products to operational changes, thereby offering a comprehensive and data-driven evaluation of sales performance.


JEL Classification Codes: C81, C80, C890.

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Article Details

Issue

Vol. 11 No. 2 (2026): Continuous Publication

Section

Research Paper/Theoretical Paper/Review Paper/Short Communication Paper
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Author Biographies

Nabil El Kadhi , Associate Professor, VPAA and Computer Science Department, Applied Science University, Manama, Bahrain

Nabil El Kadhi is an accomplished academic and leader in higher education, currently serving as Associate Professor and Vice President for Academic Affairs (VPAA) at Applied Science University in Manama, Bahrain. With a strong background in Computer Science, he also contributes actively to the university as a member of the Computer Science Department. Dr. El Kadhi is recognized for his commitment to academic excellence, curriculum development, and the advancement of innovative teaching and research practices.

Throughout his career, he has played a key role in shaping academic policies, enhancing program quality, and fostering an environment that supports student success and faculty development. His research interests span areas within computer science, reflecting both theoretical depth and practical relevance. In his leadership capacity, he works closely with faculty and administration to drive institutional growth, accreditation efforts, and international collaboration.

Dr. El Kadhi is widely respected for his strategic vision, dedication to education, and contributions to the academic community in Bahrain and beyond.

Maruf Fatima Sadriwala , Assistant Professor, Pacific University, Faculty of Commerce and Management, India

Maruf Fatima Sadriwala is an Assistant Professor in the Faculty of Commerce and Management at Pacific University, India. She is an emerging academic known for her dedication to teaching, research, and student development in the fields of commerce and management studies. With a strong academic foundation, she contributes to shaping future professionals by integrating theoretical knowledge with practical insights in her classroom.

Her teaching interests include core areas of commerce, business management, and related interdisciplinary subjects. She is committed to fostering critical thinking, analytical skills, and ethical awareness among her students. Alongside her teaching responsibilities, Ms. Sadriwala actively engages in academic research and scholarly activities, contributing to the advancement of knowledge in her field.

At Pacific University, she plays an important role in supporting academic programs, mentoring students, and participating in institutional initiatives aimed at enhancing educational quality and professional excellence.

Kaneez Fatima Sadriwala , Associate Professor, College of Economics, Management and Information Systems, University of Nizwa, Sultanate of Oman

Kaneez Fatima Sadriwala is an Associate Professor at the College of Economics, Management and Information Systems at the University of Nizwa, Sultanate of Oman. She is a dedicated academic with extensive experience in higher education, specializing in the fields of economics, management, and information systems. Her work reflects a strong commitment to teaching excellence, research development, and academic leadership.

Dr. Sadriwala has contributed significantly to curriculum design and program development, ensuring alignment with international academic standards and industry needs. She is actively involved in mentoring students, guiding research projects, and fostering a learning environment that encourages critical thinking and innovation.

Her research interests span various aspects of management and information systems, with a focus on addressing contemporary challenges in business and technology. In addition to her academic responsibilities, she participates in institutional initiatives, quality assurance processes, and collaborative academic activities, contributing to the overall growth and reputation of the University of Nizwa.

Widely respected by her peers and students, Dr. Sadriwala continues to play a vital role in advancing education and research within her institution and the broader academic community.

Rifat O. Shannak , Professor, Business School, Jordan University, Amman, Jordan

Rifat O. Shannak is a Professor at the Business School of the University of Jordan in Amman, Jordan. He is a distinguished academic with extensive experience in business and management education, known for his contributions to teaching, research, and academic leadership.

Professor Shannak’s expertise lies in key areas of business studies, where he has been actively involved in developing curricula, supervising graduate research, and advancing scholarly work. His research interests address contemporary issues in management and organizational practices, contributing to both academic knowledge and practical applications in the business field.

Throughout his career, he has played an important role in mentoring students and supporting faculty development, fostering an environment of academic rigor and innovation. In addition to his teaching and research responsibilities, he has been engaged in various institutional and professional activities, helping to strengthen academic programs and promote collaboration within the university and beyond.

Respected for his dedication and professionalism, Professor Shannak continues to make valuable contributions to the academic community in Jordan and the wider region.

Marwan Alshar’e, Associate Professor, Faculty of Computing and IT, Sohar University, Sohar, Sultanate of Oman

Marwan Alshar’e is an Associate Professor in the Faculty of Computing and Information Technology at Sohar University, Sultanate of Oman. He is a dedicated academic with a strong background in computing and IT, contributing significantly to teaching, research, and academic development within his institution.

Dr. Alshar’e’s expertise spans key areas of computer science and information technology, where he is actively involved in delivering high-quality education and mentoring students at both undergraduate and postgraduate levels. He is committed to fostering innovation, critical thinking, and practical skills that prepare students for the evolving demands of the technology sector.

In addition to his teaching responsibilities, he engages in research activities addressing contemporary challenges in computing and digital transformation. He also contributes to curriculum development, quality assurance processes, and academic initiatives aimed at enhancing program effectiveness and institutional excellence.

Recognized for his professionalism and academic commitment, Dr. Alshar’e continues to play an important role in advancing computing education and research at Sohar University and in the wider academic community.

How to Cite

Kadhi , N. E. ., Sadriwala , M. F. ., Sadriwala , K. F. ., Shannak , R. O. ., & Alshar’e, M. . (2026). A BUSINESS INTELLIGENCE SIMULATOR FOR SALES OPTIMIZATION USING A MACHINE LEARNING DDS FRAMEWORK. Bangladesh Journal of Multidisciplinary Scientific Research, 11(2), 128-136. https://doi.org/10.46281/bjmsr.v11i2.2860
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References

Ab Talib, M. S., Abdul Hamid, A. B., & Thoo, A. C. (2015). Critical success factors of supply chain management: A literature survey and Pareto analysis. EuroMed Journal of Business, 10(2), 234–263. https://doi.org/10.1108/EMJB-09-2014-0028

Alasiri, M. M., & Salameh, A. A. (2020). The impact of business intelligence (BI) and decision support systems (DSS): Exploratory study. International Journal of Management, 11(5),1001–1016. https://ssrn.com/abstract=3631747

Al-Okaily, A., Teoh, A. P., & Al-Okaily, M. (2023). Evaluation of data analytics-oriented business intelligence technology effectiveness: An enterprise-level analysis. Business Process Management Journal, 29(3), 777–800. https://doi.org/10.1108/BPMJ-10-2022-0546

Ben Rabia, M. A., & Bellabdaoui, A. (2022). Simulation-based analytics: A systematic literature review. Simulation Modelling Practice and Theory, 117, 102511. https://doi.org/10.1016/j.simpat.2022.102511

Ferdinand, A. T., & Killa, M. F. (2018). The Pareto sales network asset: A networked power perspective. Business: Theory and Practice, 19, 103–113. https://doi.org/10.3846/btp.2018.11

Gathani, S., Li, K., Thind, R., Zeng, S., Xu, M., Haas, P. J., Demiralp, Ç., & Liu, Z. (2025). PRAXA: A framework for what-if analysis [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2510.09791

Gathani, S., Liu, Z., Haas, P. J., & Demiralp, Ç. (2025). What-if analysis for business professionals: Current practices and future opportunities. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (Article 973, pp. 1–17). Association for Computing Machinery. https://doi.org/10.1145/3706598.3713672

Gorgilli, S., Mittal, S., Ramavath, S. K., Patel, P., Makhmudov, S., & Abdullaeva, P. (2025). Scalable retail intelligence: BI dashboards, analytics, and cloud data. In 2025 International Conference on Sustainability, Innovation & Technology (ICSIT) (pp. 1–5). IEEE. https://doi.org/10.1109/ICSIT65336.2025.11295817

Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: Review and future scope of research. Annals of Operations Research, 308(1), 215–274. https://doi.org/10.1007/s10479-020-03856-6

Hoang, T. G., & Bui, M. L. (2023). Business intelligence and analytic (BIA) stage-of-practice in micro-, small- and medium-sized enterprises (MSMEs). Journal of Enterprise Information Management, 36(4), 1080–1104. https://doi.org/10.1108/JEIM-01-2022-0037

Hou, R., Ye, X., Zaki, H. B. O., & Omar, N. A. B. (2023). Marketing decision support system based on data mining technology. Applied Sciences, 13(7), 4315. https://doi.org/10.3390/app13074315

Islam, M. M., & Hasan, M. M. (2023). Explainable AI (XAI) models for cloud-based business intelligence: Ensuring compliance and secure decision-making. American Journal of Interdisciplinary Studies, 4(3), 208–249. https://doi.org/10.63125/5etfhh77

Islam, M. R., & Ikbal, M. Z. (2022). Impact of predictive data modeling on business decision-making: A review of studies across retail, finance, and logistics. American Journal of Advanced Technology and Engineering Solutions, 2(2), 33–62. https://doi.org/10.63125/8hfbkt70

Jewel, R. M., Linkon, A. A., Shaima, M., Sarker, M. S. U., Shahid, R., Nabi, N., ... & Hossain, M. J. (2024). Comparative analysis of machine learning models for accurate retail sales demand forecasting. Journal of Computer Science and Technology Studies, 6(1), 204–210. https://doi.org/10.32996/jcsts.2024.6.1.23

Jum’a, L., & Basheer, M. E. (2023). Analysis of warehouse value-added services using Pareto as a quality tool: A case study of third-party logistics service provider. Administrative Sciences, 13(2), 51. https://doi.org/10.3390/admsci13020051

Khatuwal, V. S., & Puri, D. (2022). Business intelligence tools for dashboard development. In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) (pp. 128–131). IEEE. https://doi.org/10.1109/ICIEM54221.2022.9853086

Koner, J., Deore, S. S., Roja, S., Sahastrabuddhe, K. N., Kalshetti, P., & Rose, A. (2024). Predictive analysis for sales forecasting using machine learning: Leveraging data-driven insights for enhanced business planning and decision-making. In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1–5). IEEE. https://doi.org/10.1109/ICSES63760.2024.10910656

Liu, S., Duffy, A. H. B., Whitfield, R. I., & Boyle, I. M. (2010). Integration of decision support systems to improve decision support performance. Knowledge and Information Systems, 22(3), 261–286. https://doi.org/10.1007/s10115-009-0192-4

Opara, I. J., Lateef, J., Nii-Okai, E., Saah, B. P., Mensah, E. K., Wiafe, G. F. O., & Olayode, A. (2025). Digital resilience in construction projects: A narrative review of data governance, BIM, and real-time decision support systems. Journal of Management and Development Research, 2(2), 117–124. https://doi.org/10.69739/jmdr.v2i2.1129

Paramesha, M., Rane, N., & Rane, J. (2024). Big data analytics, artificial intelligence, machine learning, internet of things, and blockchain for enhanced business intelligence (SSRN Scholarly Paper No. 4855856). Social Science Research Network. https://doi.org/10.2139/ssrn.4855856

Paulino, E. P. (2022). Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry. Journal of Entrepreneurship, Management and Innovation, 18(2), 69–104. https://doi.org/10.7341/20221823

Prasanth, A., Vadakkan, D. J., Surendran, P., & Thomas, B. (2023). Role of artificial intelligence and business decision making. International Journal of Advanced Computer Science and Applications, 14(6). 965–969. https://doi.org/10.14569/IJACSA.2023.01406103

Punia, S., & Shankar, S. (2022). Predictive analytics for demand forecasting: A deep learning-based decision support system. Knowledge-Based Systems, 258, 109956. https://doi.org/10.1016/j.knosys.2022.109956

Rane, N., Paramesha, M., Choudhary, S., & Rane, J. (2024). Business intelligence and business analytics with artificial intelligence and machine learning: Trends, techniques, and opportunities (SSRN Scholarly Paper No. 4831920). Social Science Research Network. https://doi.org/10.2139/ssrn.4831920

Salarpour, M., & Zeinolabedinzadeh, K. (2025). Modeling competitive semiconductor supply chains under disruptions: A multicountry two-stage stochastic framework. Journal of Modelling in Management, 1–26. https://doi.org/10.1108/JM2-05-2025-0201

Salimparsa, M., Sedig, K., Lizotte, D. J., Abdullah, S. S., Chalabianloo, N., & Muanda, F. T. (2025). Explainable AI for clinical decision support systems: Literature review, key gaps, and research synthesis. Informatics, 12(4), 119. https://doi.org/10.3390/informatics12040119

Shannaq, B., & Alabri, A. (2025). EVALUATING THE IMPACT OF CHATGPT IMPLEMENTATION ON HR PERFORMANCE SKILLS: A CASE STUDY OF ACADEMIC INSTITUTIONS IN OMAN. Bangladesh Journal of Multidisciplinary Scientific Research, 10(1), 11–21. https://doi.org/10.46281/bjmsr.v10i1.2281

Sorour, A., & Atkins, A. S. (2024). Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards. Journal of Electronic Science and Technology, 22(1), 100233. https://doi.org/10.1016/j.jnlest.2024.100233

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y

Syberg, M., West, N., Lenze, D., & Deuse, J. (2023). Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning. Procedia CIRP, 120, 1107–1112. https://doi.org/10.1016/j.procir.2023.09.133

Vankayalapati, R. K. (2020). AI-driven decision support systems: The role of high-speed storage and cloud integration in business insights (SSRN Scholarly Paper No. 5103815). Social Science Research Network. https://doi.org/10.2139/ssrn.5103815

Wang, F., Luan, Y., Badawi, A. N., Ayad, A., Abdallah, A. F., Ali, M., Ahmad, Z., & Jiang, W. (2023). Information analysis for dynamic sale planning by AI decision support process. Information Processing & Management, 60(3), 103319. https://doi.org/10.1016/j.ipm.2023.103319

Zamil, M. H., Mohiuddin, M., & Mamun, M. N. H. (2024). Business intelligence systems in finance and accounting: A review of real-time dashboarding using Power BI and Tableau. American Journal of Scholarly Research and Innovation, 3(2), 52–79. https://doi.org/10.63125/fy4w7w04

Zhang, Y., Kang, Q., Yu, W. Y., Gong, H., Fu, X., Han, X., Zhong, T., & Ma, C. (2025). Decision information meets large language models: The future of explainable operations research [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2502.09994

Zohdi, M., Rafiee, M., Kayvanfar, V., & Salamiraad, A. (2022). Demand forecasting-based machine learning algorithms on customer information: An applied approach. International Journal of Information Technology, 14(4), 1937–1947. https://doi.org/10.1007/s41870-022-00875-3

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