American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/2253 2018 2018 Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt Ahmed M. Ali Faculty of Information Systems and Computer Science, October 6th University, Cairo, Egypt Ahmed Abdelhafeez Faculty of Computers and Informatics, Zagazig University, Sharqiyah, Egypt Shimaa S. Mohamed The advent of Industry 4.0 has propelled a transformative shift in business paradigms, prompting the strategic integration of business intelligence (BI) for sustainable portfolio management. This study addresses the need to discern optimal strategies in clustering investor portfolios within this dynamic landscape. Leveraging the Gap Statistic Algorithm and Silhouette Coefficient, a systematic methodology was employed to cluster investors based on diverse portfolio attributes, including asset allocation, risk profiles, and historical performance metrics. A feature correlation map elucidated attribute interdependencies, while summary statistics provided a comprehensive snapshot of the investor dataset. Results from the Gap Statistic Algorithm revealed an optimal cluster count, guiding the segmentation of investors into distinct clusters. Subsequent validation using the Silhouette Coefficient affirmed the coherence and quality of the clusters derived. The findings underscore the efficacy of BI-driven approaches in effectively clustering investors based on portfolio characteristics within Industry 4.0, facilitating nuanced insights into investor behaviors and preferences. Conclusively, this research illuminates pathways for informed decision-making in sustainable portfolio management, emphasizing the pivotal role of BI tools in optimizing investor segmentation strategies for contemporary industrial landscapes. 2021 2021 124 24 10.54216/AJBOR.030205 https://www.americaspg.com/articleinfo/1/show/2253