What is the first step toward a data-mature Supply Chain?

The first step is to assess your current data maturity: data sources, quality, governance, and usage. From there, organizations can define a realistic roadmap to improve data foundations and progressively introduce machine learning where it delivers the most value. The diagnostic stage is more important than it looks. It exposes which data is actually trustworthy, which decisions still depend on spreadsheets and tribal knowledge, and where the highest-return improvements sit. A clear-eyed view at the start prevents teams from layering advanced analytics on top of unreliable inputs, which is the most common reason data and AI programs fail to translate into measurable Supply Chain KPI improvements.