How do you implement machine learning in your Supply Chain?

Start smaller than most roadmaps suggest, and begin with data rather than algorithms. Consolidate clean demand and inventory history and connect the source systems, usually the enterprise resource planning (ERP), so the model has something reliable to learn from. Then pick one scope where the pain is clear, a category or a site, agree on the service targets that define success, and run the machine learning forecast alongside the current process before switching over. Modern platforms shorten this from the six-month projects of traditional tools to weeks, sometimes days for lighter deployments. Widen the scope once planners trust the output and the first results hold, rather than trying to automate everything at once.