What are the main use cases of machine learning in Supply Chain?

Four recur across industries. Demand forecasting is the foundation, weighing many signals at once to predict demand as a range rather than a single number. Inventory optimization turns that forecast into the right stock in the right place, protecting service without tying up cash. Replenishment and procurement automation generate order proposals so planners stop recalculating by hand. Supplier collaboration shares demand signals upstream so partners can anticipate rather than react. A fifth is growing fast: scenario simulation, testing how a plan holds under a demand spike or a supply disruption before committing to it. Which of these matters most varies by business, easiest to see across Flowlity's customer stories. For Plum, a fast-scaling design brand, the first win was simply the replenishment visibility it never had on spreadsheets.