Why Bad Demand Data Breaks SAP IBP Forecasts | Data Cleansing & Outlier Handling Explained

“Bad demand data doesn’t just distort forecasts — it silently drives wrong business decisions.”

Welcome to another insightful episode of Bestowal Talks, where we simplify real-world SAP IBP concepts for planners and transformation teams.

In this episode, we dive deep into a critical but often underestimated foundation of Demand Planning in SAP IBP — Data Cleansing Methods.

Through a realistic, meeting-style discussion between a Business Planner and an SAP IBP Consultant, we break down how IBP handles imperfect demand history and how these settings directly influence forecast accuracy and planner confidence.

We cover:
1] Handling missing demand values using Mean, Median, and Given (Fixed) values
2] Detecting abnormal demand spikes using the IQR (Interquartile Range) method
3] Correcting outliers using Mean-based, Median-based, and Tolerance-driven approaches
4] Common planner misunderstandings — and when not to blindly correct data
5] How data cleansing choices impact downstream forecasting and decision-making

If you’re a Demand Planner, SAP IBP Consultant, Forecast Analyst, or Supply Chain Leader, this episode will help you understand how data cleansing in IBP is not just a technical step — but a strategic planning decision.

🎧 Tune in to learn how to build forecasts on data you can actually trust.

For more SAP IBP insights, webinars, and consulting support, visit Bestowal Systems & Services.

Видео Why Bad Demand Data Breaks SAP IBP Forecasts | Data Cleansing & Outlier Handling Explained канала Bestowal Systems and services Private Limited