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@article{205743,
        author = {Charoo Ranjan and Harsh Agrawal and Kotra Sasank and Dr. Vivekanand S Gogi},
        title = {AI-Driven Retail Decision Optimization Through Forecasting, Segmentation, and Risk Analytics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {7343-7350},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205743},
        abstract = {The levels of turbulence, competition and data complexity keep on rising in the contemporary organizations, which requires a shift in the paradigm of managerial decision making that has traditionally been the result of intuition. Even as transactional and operational data are being generated at a very high rate, the nature of managerial decisions remains reactive since there is no foreseeable as well as prescriptive analytics. The proposed paper suggests a comprehensive AI-based data analytics system that assists enhancing the managerial decision-making at the strategic level and integrating machine learning solutions with the conventional Principles of Management and Managerial Eco-nomics. The offered framework applies the time series forecasting to facilitate the planning-level decision-making, clustering algorithms to facilitate the customer segmentation and enhance the organizational alignment, and supervised classification models to facilitate the proactive risk and loss analysis and enhance managerial control. These analytics modules are combined under a formal decision-support logic which transforms predictive analytics findings into managerial actionable information. The framework can be used to reduce uncertainty, the influence of cognitive bias, and allow rational and evidence-based decision-making by directing the managerial decision-making process with the help of analytics. The approach proposed would highly promote human-AI cooperation in which the artificial intelligence will support and complement the judgment of managers as opposed to replacing it. Experimental results show that the proposed framework is effective in improving planning accuracy, resource allocation, and risk management, thus filling the gap between traditional management theory and contemporary data analytics-based practices. Index Terms—Data Analytics, Artificial Intelligence, Strategic Decision-Making, Managerial Economics, Principles of Management, Machine Learning, Decision Support Systems, Predictive Analytics},
        keywords = {Data Analytics, Artificial Intelligence, Strategic Decision-Making, Managerial Economics, Principles of Management, Machine Learning, Decision Support Systems, Predictive Analytics},
        month = {June},
        }