Carbon-intelligent planning THEME 2 Evolutions of themes Planning evolves to balance lead time, cost, emissions and circularity, embedding energy and carbon optimization into networks by design. Energy cost Carbon footprintCarbon visibility and accounting — AI models predict process- level energy use and CO₂ intensity. — Carbon KPIs are piloted on select production lines using IoT and edge data to identify optimization opportunities.Carbon optimization — Prescriptive AI optimizes plant- level operations for cost- carbon trade-offs. — Systems dynamically schedule production around availability of renewables and local energy prices, shifting energy-intensive tasks into low-carbon windows. — Optimization remains enterprise-centric, guided by business priorities and known grid conditions.Planet-aware systems — Optimization expands beyond the enterprise to the energy ecosystem. — Agentic and quantum solvers connect factories, suppliers and power networks into grid-interactive, carbon-intelligent systems. — These networks autonomously balance carbon, cost and capacity in real time.NOW (0-2 years) NEAR (3-5 years) NEXT (5+ years) Objectives Intelligent production planning with digital twin and machine learningPLANNING | CASE STUDY ACG Packaging Materials Challenge Solution Impact The company’s Satara site in Maharashtra, India produces over 5,000 SKUs* across 40+ product types. Production involves complex routings and frequent changeovers. Around 30% of orders are rush orders. Planning was manual and siloed, resulting in low-capacity utilization, long production lead times and poor OTDIF.*Chronos, a digital twin-driven production planning and optimization solution, was implemented combining reinforcement learning (RL), optimization algorithms and simulation. This RL-based planning model creates a demand-driven seed plan, which is expanded into 1,000+ optimized plan variants. Each variant is simulated using a plant digital twin to evaluate KPIs such as lead time, capacity utilization and changeovers, enabling recommendation of the optimal production plan.The transformation strengthened operational efficiency, accelerated lead times and enhanced overall delivery reliability, including: –42% reduction in change over time. – 23% reduction in production lead time. – Increase in capacity utilization from 62% to 78%. Note: *SKU = stock keeping unit; OTDIF = on-time delivery in full. PLANNING Intelligent Industrial Operations Outlook 2026 15