How to Solve the PLM Trap in Demand Forecasting

📊 Planners often master the 𝘮𝘦𝘤𝘩𝘢𝘯𝘪𝘤𝘴 of the system – any system but rarely invest enough time in learning the 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗻𝘂𝗮𝗻𝗰𝗲𝘀 behind it.

💡 Planning isn’t just clicking buttons. It’s about uncovering the organizational and market events that shape demand.

⚙️ 𝗧𝗵𝗲 𝗦𝗲𝘁𝘂𝗽

When configuring like-modeled forecasting — whether it’s PLM in SAP IBP or any other forecasting tool — you create an analog pair between products. But here’s the catch: you must define all the required parameters 👇

✅ Phase-in
✅ Phase-out
✅ Launch date
✅ Volumetrics
✅ Weighting

⚠️ 𝗧𝗿𝗮𝗽 #𝟭: 𝗧𝗵𝗲 𝗗𝗲𝗳𝗮𝘂𝗹𝘁 𝗪𝗲𝗶𝗴𝗵𝘁𝗶𝗻𝗴

Planners link the new SKU to a predecessor with a similar pattern… but nobody touches the predecessor’s weighting. It stays at the default 𝟭𝟬𝟬% — and that’s where the trouble begins. 📈💥

⚠️ 𝗧𝗿𝗮𝗽 #𝟮: 𝗧𝗵𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗦𝗲𝗹𝗳-𝗠𝗮𝗽𝗽𝗶𝗻𝗴

Many planners forget the self-mapping pair needed to sustain an ongoing forecast once the new product has its own history.

🧩 𝗘𝘅𝗮𝗺𝗽𝗹𝗲

Product 𝗡𝗫 is modeled after Product 𝗢𝗫, but is only expected to sell 𝟮𝟬% of the older product’s volume.

Here’s how the mapping should look:

• NX ➡️ OX → Weight 𝟮𝟬% (𝘞𝘩𝘦𝘳𝘦 𝘥𝘰𝘦𝘴 𝘵𝘩𝘦 20% 𝘤𝘰𝘮𝘦 𝘧𝘳𝘰𝘮? 𝘔𝘢𝘳𝘬𝘦𝘵 𝘪𝘯𝘵𝘦𝘭, 𝘭𝘢𝘶𝘯𝘤𝘩 𝘱𝘭𝘢𝘯𝘴, 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘺 𝘣𝘦𝘯𝘤𝘩𝘮𝘢𝘳𝘬𝘴 — 𝘯𝘰𝘵 𝘨𝘶𝘦𝘴𝘴𝘸𝘰𝘳𝘬!)
• NX ➡️ NX → Weight 𝟭𝟬𝟬% (𝘴𝘰 𝘵𝘩𝘦 𝘯𝘦𝘸 𝘚𝘒𝘜 𝘭𝘦𝘢𝘳𝘯𝘴 𝘧𝘳𝘰𝘮 𝘪𝘵𝘴 𝘰𝘸𝘯 𝘢𝘤𝘵𝘶𝘢𝘭𝘴 𝘰𝘷𝘦𝘳 𝘵𝘪𝘮𝘦)

🎯 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲

The PLM link is only as smart as the planner behind it. Defaults are the enemy of accuracy. Challenge every 100% before you trust the forecast.

👉 How does your team avoid the PLM trap?

Read our related posts:

• When Your Algorithm Forecasts a Ghost – The Hidden Cost of Ignoring PLM Flags
• SAP IBP Best Fit Forecasting – How Poor Model Pool Design Breaks Your Demand Plan
• SAP IBP PLM Trap – How Default 100% Weighting Destroys Forecast Accuracy