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 be faulted? Or is it the planner?

What needs to be fixed here?

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.

Read: SAP IBP Worst Practices WP1 — Applying an Aggressive Forecast Model to a New SKU