Dr. Chockalingam

Implementing SAP IBP Inventory Optimization in CPG — what actually works Most implementations take longer than they should — not because the technology is hard, but because teams try to boil the ocean before proving value. 💡 Secondly, the IO methods leveraged are quite basic NOT doing justification to the tool capabilities. After working with CPG companies across snacks, [...]

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 [...]

🚨 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 [...]

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_suppports_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 [...]

Worst Planning Practices in SAP IBP — WP #2: A "Best Fit" Model Pool with No Design Thinking Continuing our Worst Planning Practices in SAP IBP series — The Best Fit configuration checked all the right boxes during implementation, but its real-world performance introduced considerable forecasting challenges. The Scenario A consultant configured Best Fit statistical forecasting in SAP IBP [...]

Worst Planning Practices in SAP IBP — WP #1: Applying an Aggressive Forecast Model to a New SKU Most SAP IBP problems don't announce themselves. They show up months later — as excess inventory, a write-off, or a board asking why forecast accuracy keeps sliding. Trace it back, and it's almost always a planning decision nobody challenged. This is [...]

SAP IBP Heuristic vs Optimizer: When to Use Each Engine (and Why the Wrong Choice Costs You Service Levels) Most SAP IBP teams default to the Heuristic engine because it's fast. In constrained, multi-node supply chains, that habit quietly drives stockouts, excess inventory, and lost margin. Here's how to choose deliberately. Fast planning isn't smart planning. It's one of [...]

#Demand_Modeling_in_SAP_IBP Three steps involved in setting up a Demand Model in SAP IBP are described in this modeling primer videos for Demand Planners. Step 0 — General Settings surfaces the key configuration parameters: planning horizon, time profile, aggregation level, and key figure mapping — the foundation everything else depends on. Step 1 — Pre-Processing now explicitly calls out the [...]

In an era of relentless market shifts, a "good guess" is no longer a viable strategy for supply chain leaders. At Valtitude, we recognize that moving from reactive firefighting to proactive planning requires more than just a software license—it requires the precise engineering and implementation of a robust digital core. As specialists in SAP Integrated Business Planning (IBP), we [...]