Retail supply chains have grown more and more complex, and companies must have end-to-end supply chain visibility to prepare for unexpected changes in demand. As such, retailers should explore practical, relevant ways to predict demand, while continuing to deliver quality, omni-channel consumer experiences.
In 1956, considerable fluctuations in production, inventories and profit baffled managers in General Electric’s household appliance division. Despite supervisory efforts, the variations endured. Traditionally, managers blamed these types of fluctuations on external causes, like business cycles.
Over the last three decades we’ve moved from maximizing resource utilization to operating demand-driven supply chains. So you can imagine my surprise when I heard a leading process manufacturer in a supply constrained environment, one that sells to large businesses, was considering returning to a fixed production schedule. The justification for the change – high demand variability and poor forecast accuracy – only furthered my disbelief.
Innovative companies are cutting supply chain complexity and accelerating responsiveness using artificial intelligence. By applying AI and machine learning against vast sets of supply chain data to unearth insights into problems and performance, enterprises are augmenting knowledge-intensive areas such as supply chain planning to be more dynamic, flexible, and efficient.