What are the challenges of predictive analytics in supply chain?

Key challenges include data quality, integration with legacy systems, organizational resistance, and over-reliance on tools without proper governance. Of these, data quality is usually the most binding constraint: models inherit the inconsistencies of their inputs, so cleaning master data, sales history and lead times often delivers more impact than tuning algorithms. Integration matters next, because predictive insights have no operational value if they cannot be acted on inside the existing planning processes. Governance closes the loop by defining who owns model outputs, how exceptions are handled, and how performance is reviewed, which is what turns predictive analytics from a one-off project into a sustained capability.