Why I audit the assumptions before I trust the forecast

Building the reorder logic before buying software is the right instinct, but logic is only as good as the data feeding it. Bad inputs wreck a forecast just as fast as having no forecast at all.

I saw this directly on one engagement. A demand planning tool recommended 44,000 units on a SKU. Actual need was under 6,000. The tool wasn't broken, it was doing exactly what it was built to do, projecting forward off transaction history. The problem was upstream. Pre-order transactions were feeding into that history as confirmed demand, and nobody had checked that assumption since onboarding, months earlier. One buy put roughly two million dollars into overstock on a single SKU.

That's not a forecasting failure. The forecast ran clean on dirty inputs. The real failure happened earlier and quieter, nobody had told the system the difference between a pre-order and a sale.

This is why every engagement I run starts with the assumptions feeding the model, not the model itself. Software can run the calculation. It can't tell you whether the number going in means what everyone assumes it means, that's the part that needs an owner, not just a formula.