AI-Driven S&OP Digital Transformation for a Global Petrochemical Leader

Situation

A global petrochemical manufacturer ran a complex network of 8+ ethylene crackers, 28+ derivative plants, and over 100 products across regions. Its Sales & Operations Planning (S&OP) processes relied heavily on Excel models, ERP systems, two decades of institutional heuristics, and expert dependencies.

Planning decisions across upstream (feedstock) and downstream (production, demand) functions were made in silos, limiting visibility and slowing response times in an increasingly volatile market.

With rising operational disruptions, leadership sought to transform S&OP from a static planning process into a dynamic, intelligence-led decision system.

Challenges

• Siloed upstream and downstream planning led to chronic supply-demand misalignment with no shared source of truth
• Manual, Excel-driven workflows, heavy reliance on heuristics reduced scalability and speed
• Lack of Dynamic Replanning; cracker outages or feedstock curtailments required days of manual recalculation
• Limited forecasting accuracy, inability to capture complex demand patterns (lumpy, intermittent, erratic demand)
• No end-to-end visibility across feedstock, production, inventory, and pricing decisions

Solution

We designed and implemented an AI-powered S&OP Decision Intelligence Platform, integrating demand, supply, pricing, and inventory decisions into a single, connected ecosystem.

1. Market Intelligence & Price Forecasting
   • Built AI/ML models for price forecasting across products and regions
   • Integrated macroeconomic indicators, feedstock prices, and real-time news sentiment via a GenAI intelligence hub
2. AI-Driven Demand Planning
   • Implemented demand profiling (smooth, erratic, intermittent, lumpy) and customer segmentation
   • Transitioned from heuristic forecasting to machine learning models
3. Feedstock & Olefins Grid Optimizer
   • Built a real-time optimization engine that dynamically ranks plants by margin and automates olefins supply redistribution during cracker outages, curtailments, and demand shocks
4. • Simulated real-world disruptions: cracker outages, feedstock curtailments, and demand shocks
   • Enabled real-time, scenario-based decision-making
5. Enterprise-Scale Change Management
   • Transformed 20-year-old planning processes
   • Enabled adoption across ~1000 stakeholders
   • Integrated decision forums at Pre-S&OP, Regional, and Global S&OP levels

Impact

• 90% + price forecast accuracy across 22 sales offices and 3 product lines
• 20%+ demand forecast accuracy
• 2 weeks reduction in prediction cycles, accelerating decision-making
• Real-time scenario replanning for outages and dynamic replanning during unforeseen events (e.g., wars, disruptions)
• Shifted contribution margin optimization from product-group to SKU level, unlocking hidden margin within the existing portfolio
• Shift from reactive planning → proactive, scenario-led decision-making