This is like a GPS for your consumer-goods supply chain: it constantly looks at demand, production, inventory, and transport data and then tells you the cheapest, fastest way to move products from factories to shelves—while updating the plan whenever reality changes.
Traditional CPG supply chains rely on static forecasts and manual planning, leading to excess inventory, stockouts, high logistics costs, and slow reaction to demand or disruption. AI-driven optimization reduces waste and cost while maintaining service levels by continuously recalculating optimal plans across the network.
Proprietary historical demand, promotion, and execution data across SKUs and channels; embedded workflows with planners and logistics teams; integration to ERP/TMS/WMS making switching costly over time.
Hybrid
Time-Series DB
High (Custom Models/Infra)
Complexity and runtime of large-scale optimization (network-wide routing and inventory decisions) as SKU, location, and constraint counts grow; plus data integration quality from multiple enterprise systems.
Early Majority
Uses AI not only for demand forecasting but for end-to-end supply-chain optimization (inventory, production, and logistics), targeting measurable cost savings rather than just analytics dashboards.