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It took us about 2 hours to build a working demand-forecasting tool. The hard part is not the model. It’s still the data. My co-founder built the first version in one afternoon. Simple time-series logic, basic interface, usable forecasts. Ten years ago, this would have taken weeks. Today, building tools is cheap and fast. That will change how most supply-chain teams work in the near future. But once we connected real manufacturing data, the same old problem appeared. Sales data in one system. Inventory in another. Production plans in spreadsheets. Half the history missing or inconsistent. This isn’t new. Research keeps pointing to the same root cause: data silos and limited data availability are the main reason forecasts fail. Many teams still rely on spreadsheets or simple averages, which leads to biased and inconsistent results. And even modern AI tools struggle when the underlying data is fragmented. So the technology changed. The core problem didn’t. You don’t start with AI. You start with clean, connected data.