How can you use machine learning to optimize your Supply Chain?

Most teams start where the data is richest and the payback is quickest: demand forecasting. A model turns messy sales history into a forward view with a confidence range, which then feeds inventory and replenishment. From there it extends to setting dynamic safety stock that flexes with real volatility, generating replenishment and purchase proposals a planner reviews and approves, and sharing forecasts with suppliers so they can plan around you. The sequence matters more than the ambition: clean the core data first, agree on a few clear service targets, prove value on one scope, then widen it. Used this way, machine learning removes the manual recalculation and frees planners for the judgment calls a model cannot make.