A BUSINESS INTELLIGENCE SIMULATOR FOR SALES OPTIMIZATION USING A MACHINE LEARNING DDS FRAMEWORK - Kadhi , Nabil El; Sadriwala , Maruf Fatima; Sadriwala , Kaneez Fatima; Shannak , Rifat O.; Alshar’e, Marwan (Bangladesh Journal of Multidisciplinary Scientific Research, 2026)
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.