Why is data quality so important for Machine Learning in Supply Chain?

Machine learning models learn from historical data. If the data is inaccurate, inconsistent, or biased, the model will reproduce those issues at scale. Clean, well-structured data is essential to build trust in forecasts and recommendations. In Supply Chain specifically, the data that matters most, sales history, master data, lead times and stock movements, often sits across several systems and accumulates inconsistencies over time. Investing in data quality upstream tends to deliver more KPI movement than tuning the model itself, because a well-prepared dataset lets even standard probabilistic methods produce reliable forecasts and buffer recommendations. Trust in the output is what drives adoption, and adoption is what turns models into operational value.