By Dr. Chockalingam|2026-06-19T09:35:47+00:00June 19th, 2026|Categories: Blog|Tags: Causal Event Forecasting, Demand Signal, Event Modeling, Forecast Accuracy, SAP IBP|
Forecasting the Pattern, Not the Cause: Why Smoothing Over Events Distorts Your Demand Signal Actual Worst Practice in SAP implementations: Removing all events from historical data and making it flat to produce a smoothed forecast!! Ask yourself: is your forecast capturing demand — or just smoothing over it? 📊In SAP IBP, too many planners still default to exponential smoothing [...]
By Dr. Chockalingam|2026-06-19T16:09:55+00:00June 12th, 2026|Categories: Blog|Tags: IBP Governance, Planning Model Configuration, SAP IBP Consulting, SAPIBP, Supply Chain Planning|
Turning Supply Chain Blueprints into Reality: SAP IBP Consulting from the Ground Up At Valtitude, we help organizations unlock the full power of SAP IBP — from the ground up. Brownfield or Greenfield, we level the playing field for users, super-users, techies, middle managers and c-Suite. With advances in Fiori, information can be accessed seamlessly at any level... Whether [...]
By Dr. Chockalingam|2026-06-19T16:21:17+00:00June 12th, 2026|Categories: Blog|Tags: CPG, Demand Planning, IBP, Inventory Optimization, S&OP, SAP Consulting, SAP IBP, SCM, Supply Chain, Valtitude|
Worst Practices Supply Chain Planning: Equal-Split Weekly Disaggregation That Erases Real Demand Patterns 📉 Equal-split forecasts in SAP IBP: convenient, but is it costing you? 🤔 A pattern I see often in IBP implementations: the monthly forecast gets disaggregated to weeks using an equal split. 400 units in the month? That becomes 100 / 100 / 100 / 100 [...]
By Dr. Chockalingam|2026-06-11T14:16:09+00:00June 11th, 2026|Categories: Blog|
Forecasting Worst Practice #8: Ghost Demand from Discontinued Products That Never Leave the System This worst practice is not just in SAP IBP. This is common across many systems. Forecast accuracy gets scrutiny. Master data accuracy deserves the same. One missed field in the Product Master. Months of planning noise. Here's how ghost demand haunts your supply chain. One [...]
By Dr. Chockalingam|2026-06-11T13:53:24+00:00June 11th, 2026|Categories: Blog|
SAP IBP Worst Practice #7: Planning with Average Lead Times That Never Get Updated 🚨 STOP planning supply with average lead times! You're lying to your own planning system. And it's costing you. Here's the scenario I have seen in number of implementations. A supplier delivers in 5 days. Then 7. Then 6. Then suddenly… 💥 15 days. But [...]
By Dr. Chockalingam|2026-06-11T13:47:30+00:00June 11th, 2026|Categories: Blog|
SAP IBP Worst Practice #6: The PLM Weighting Trap That Wrecks Your Forecast Product Lifecycle Management (PLM) in SAP IBP (and APO) is supposed to be your secret weapon for phase-in / phase-out modeling and #analog forecasting. Instead, it's quietly killing forecast accuracy👇 📌 The Setup Looks Perfect A new SKU launches. A planner maps it to a predecessor [...]
By Dr. Chockalingam|2026-06-10T14:48:24+00:00June 10th, 2026|Categories: Blog|
SAP IBP Worst Practice #5: Heuristics on high gear ⚡ Fast planning ≠ smart planning. A lesson every supply chain team eventually learns the hard way. A typical worst practice in SAP IBP implementations: using the Heuristic engine in highly constrained, multi-node scenarios — because it's fast and familiar. Heuristic plans sequentially — first priority gets served first. It's fast, it produces [...]
By Dr. Chockalingam|2026-06-10T14:50:05+00:00June 10th, 2026|Categories: Blog|
🚨 SAP IBP Worst Practices... continuing on to Supply Planning and IO ⚠️ WP #4: One lead time. Every product. Every location. What could go wrong? We keep seeing this in SAP IBP Inventory Optimization implementations — and it could quietly kill the usability and the credibility of the tool even before go-live. 💀 🛠️ The integration consultant loads [...]
By Dr. Chockalingam|2026-06-12T14:26:40+00:00June 10th, 2026|Categories: Blog|
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 [...]
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