Supply chain advisor Stephan de Wit on the planning problem that costs millions and rarely gets diagnosed correctly.
.png)
Most S&OP performance issues trace back to product data governance, not forecast accuracy or tooling.
Poor data governance costs mid-size organisations millions — in lost revenue, excess working capital, and planning capacity — without surfacing on any dashboard.
The fix requires a structural redesign of ownership, governance, and data architecture, not a clean-up project.
When S&OP starts underperforming, organisations examine the forecast, review the tooling, and question the process design. Stephan de Wit, a supply chain advisor with 25 years of experience, has watched this across dozens of organisations. His starting point is different: the product data that sits underneath all of it.
"S&OP assumes that you plan based on reality — based on a certain level of data quality and certainty," he says. "If that data isn't correct, or doesn't move correctly between systems, everything downstream goes wrong. Even if your forecast is solid and your tooling is right."
In organisations where no one owns product master data, a significant share of planning capacity goes to correction work: checking interface errors, fixing parameter inconsistencies, building spreadsheets to reconcile what different systems report.
"Everyone does their job as well as they can," Stephan says. "But if the data in your systems is wrong, you can't do your job properly. You lose your time to repair work."
S&OP meetings absorb the same problem. Discussions that should focus on decisions — demand signals, supply constraints, trade-offs — get consumed by questions about data quality. Is this the latest version? Why did this number change? The agenda fills with data questions rather than planning decisions.
Stephan points to two organisations he worked with recently.
At a large retailer, parts of the orderable assortment couldn't be ordered. Interface rules had been configured incorrectly. Business logic connecting planning and ordering systems was wrong. Products appeared available in the system but couldn't be committed in practice.
At a consumer packaged goods manufacturer, incomplete product master data blocked new product introductions. Lifecycle parameters were missing. Suppliers couldn't place orders. Customers couldn't buy. The financial damage ran into millions.
In both cases, no one held end-to-end ownership of the data.
The costs accumulate across three dimensions.
Planning capacity: In mid-size and large organisations, 4 to 10 people are often occupied daily with data correction and recovery — not improving planning performance. That's a full team's capacity, consumed by a problem that shouldn't exist.
Assortment exposure: Around 2 to 4% of the orderable assortment is often not executable in practice, due to lifecycle errors or data gaps. For an organisation with €100M in turnover, that's a €2 to 4M exposure sitting in the product catalogue.
Downstream effects: Teams set safety stock targets on assumptions that don't hold. Customer service levels drop. Employees carry a growing workload of fixes. Unexpected stock differences force urgent, expensive decisions.
"It doesn't stand out," Stephan says. "There's a department set up to deal with it, so people assume it belongs there. But if you convert it properly, it's a serious amount of money."
Product master data lives between functions. IT manages the systems. Category manages the attributes. Supply chain uses the planning parameters. Finance reads the output. No single function owns the lifecycle of a product from introduction to phase-out, and S&OP sits at the end of all of it.
The planning cycle's pace makes this worse. Teams move fast and assume data is correct because stopping to verify isn't possible within the rhythm. Teams patch errors for this cycle. The same errors return in the next one. The firefighting normalises. Teams stop questioning it.
"There is no end-to-end ownership on data," Stephan says. "And that is a fundamental problem in organisations."
Organisations that have fixed this don't do it with a one-time clean-up. They redesign ownership and architecture.
Stephan describes a retail transformation he led where the solution centred on a central data hub — the single point through which all data flows passed before entering operational systems. An MDM (Master Data Management) layer ensured no product data reached planning, ordering, or reporting tools without validation. Every product lifecycle change had one accountable owner, a controlled entry point, validated parameter logic, and a reconciliation step before activation.
Four changes made the difference:
Assign single ownership. One function holds accountability for product lifecycle integrity across all systems. Distributing that responsibility across IT, supply chain, and category is how the governance gap opens.
Govern the interfaces. Treat data connections between systems as controlled flows. Build validation into the architecture before data enters operational tools.
Embed lifecycle validation in the S&OP rhythm. Review orderability, parameter completeness, and lifecycle status before planning decisions are made — not after errors have surfaced.
Treat MDM as a cross-functional capability. Master data management spans commercial planning, procurement, and finance. It doesn't belong to supply chain alone. "It is a hugely underexposed subject," Stephan says.
Stephan closed the webinar with these:
If those questions don't have clear answers, that's where the problem lives.
When data governance has a clear owner, planners steer instead of repair. Teams execute reliably. Working capital drops. Customer promises hold. Managers spend meetings on decisions rather than diagnosing errors.
"If you organise things well upfront," Stephan says, "you create time. Time to simulate. Time to look at the S&OP process differently. Time to make the planning stable and reliable."
Most teams blame forecast accuracy, tooling limitations, or process design. The structural issue is more often missing or mismanaged product master data, with no clear owner of lifecycle integrity across systems.
In mid-size to large organisations, 4 to 10 people spend the majority of their working time on data correction rather than planning improvement. On top of that, 2 to 4% of the orderable assortment is often not executable due to lifecycle or data errors — a multi-million euro exposure for organisations with revenues above €50M.
A data hub centralises validation: all product data passes through it before reaching operational systems such as ERP, planning tools, or reporting platforms. An MDM layer behind it confirms data is validated, parameter logic is correct, and lifecycle status is confirmed before any system acts on it.
Data governance needs a single owner with end-to-end accountability for product lifecycle integrity. Shared responsibility across IT, supply chain, and category is the primary reason governance breaks down. The accountable function varies by organisation, but the accountability itself cannot be shared.
Discover how you can fix your data flow.
Timo can answer all your questions!
