Supply chains by their nature are complex, interconnected systems where the source of a disruption is rarely obvious at first glance. What might take a team of analysts days to unravel can be surfaced and contextualized far more efficiently through GenAI, freeing up planners to focus on response efforts rather than diagnosing the core issue.
Anyone who’s ever watched a weather forecast knows that predicting tomorrow’s conditions is far simpler than predicting conditions six months out. The same logic applies to demand planning, which is where ensemble forecasting comes in, by wrapping in a variety of models that account for a range of possibilities.
It can be easy to miss underlying patterns when planning a new product or forecasting demand for an existing line. Causal modeling addresses that by identifying the underlying factors that actually drive demand for a product, while cluster analytics groups items together based on those shared behavioral patterns.
As supply chain networks have grown in complexity and span multiple manufacturing sites, distribution centers and geographies, the limitations of node-by-node inventory optimization have become increasingly apparent. Multi-Echelon Inventory Optimization, or MEIO, offers a different approach, by managing inventory levels across every tier of the supply chain as one connected system rather than a series of isolated nodes.
A demand signal is only useful if an organization has the capacity to act on it, and connecting the dots between what customers want and what operations can actually deliver has historically been one of supply chain management’s most persistent blind spots. With capacity optimization, a business can ensure that the right resources, space, equipment, labor and materials are available at the right time to meet demand.
John Galt Solutions provides insight into current development of a composite AI framework that provides a unified architecture designed to bring together a full range of AI tools and techniques, and make them accessible and interpretable for anyone.