Think of your supply chain planning as flying a modern plane: the AI is the autopilot doing millions of calculations per second, and your planners are the pilots deciding the destination, watching for storms, and overriding when needed. This setup makes planning faster, safer, and more precise than humans or software alone.
Traditional supply chain planning relies heavily on manual spreadsheets and experience, which struggle with volatile demand, complex global networks, and frequent disruptions. This use of AI aims to improve forecast accuracy, inventory levels, and responsiveness while keeping human judgment in control for exceptions and strategic decisions.
Moat is likely to come from proprietary demand, supply, and logistics data combined with embedded AI models tuned to a customer’s specific products, seasonality, and network, plus workflow integration into existing planning processes that make the tool sticky for planners.
Hybrid
Time-Series DB
Medium (Integration logic)
Quality and granularity of historical demand and supply data, plus integration with multiple ERP/WMS/TMS systems across the supply chain.
Early Majority
Emphasis on human-in-the-loop collaboration—using AI to augment, not replace, planners—along with domain-specific models for supply chain planning and scenario analysis rather than a generic AI platform.