International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-Driven Sales & Operations Planning (S&OP): A Framework for Intelligent Demand-Capacity Balancing

Author(s) Mr. Sahil Bansal
Country United States
Abstract Traditional Sales and Operations Planning (S&OP) processes, long anchored in periodic review cycles and manual consensus-building, face mounting pressure from supply chain volatility, compressed planning horizons, and exponentially growing data volumes. This paper proposes a conceptual framework for AI-driven S&OP that transforms the planning process from a static, calendar-bound activity into a continuous, adaptive intelligence system. The proposed three-layer architecture — comprising a Data Integration Layer, an Intelligence Engine, and a Decision Orchestration Layer — provides a structured pathway for embedding machine learning, predictive analytics, and automated decision support into the S&OP cycle. A single quantitative construct, the Demand-Supply Dispersion (DSD) metric, is introduced as a measure of planning effectiveness. Evidence drawn from industry implementations and emerging academic literature suggests that AI-augmented S&OP can reduce planning cycle times by up to 75 percent, improve forecast accuracy by 15–30 percent, and materially reduce working capital requirements. The paper concludes with practical guidance for practitioners and a research agenda for scholars exploring the intersection of AI and operations management.
Keywords AI-Driven S&OP, Demand-Capacity Balancing, Intelligent Planning, Machine Learning, Supply Chain Planning, Demand Sensing, Decision Orchestration.
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-06-08
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.86601
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