SAP IBP forecast output showing seasonal spikes for a dormant product with zero demand over 18 months — illustrating the risk of ignoring PLM flags in demand planning

SAP IBP Worst Practices WP3: When Your Algorithm Forecasts a Ghost — The Hidden Cost of Ignoring PLM Flags

It is not about the model but everything that supports it.

WP3: Not considering PLM or other flags that reflect business realities.

Here is a forecast output from a live IBP instance. The product had zero demand for 18+ months.

No shipments. No orders. Silence.

And yet… the algorithm confidently generated a seasonal forecast, projecting big January spikes year after year. 📈

This is a textbook case of an algorithm finding a pattern where none exists in business reality. Do you agree with the forecast?

❌ What went wrong?

💀Product was dormant — no demand for 18+ months

🎲Spikes ≠ seasonality

🤖Did IBP’s MLR algorithm aggressively found “seasonality” in noise?

Is the algorithm to blame? Or is it the planner?

Not entirely. MLR did what MLR does – it found a mathematical pattern in the data it was given. The failure is upstream:

  • No PLM flag to mark the product as inactive or dormant
  • No governance rule to suppress forecasting on zero-demand SKUs
  • No planner review layer to catch the absurd output before it flowed downstream

The fix isn’t a better algorithm. It’s connecting your planning system to business reality – through lifecycle flags, segmentation rules, and exception-driven review.

If your IBP instance doesn’t know a product is dead, it will keep planning for it.

If your IBP environment is forecasting products that the business has already moved on from, the fix isn’t in the algorithm — it’s in the governance layer around it. Click here to connect with Valtitude’s SAP IBP consulting team to audit your PLM flag integration, lifecycle-based segmentation, and forecasting eligibility configuration.

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