A pharmaceutical manufacturer exposes why its On-Time-In-Full numbers can’t be trusted

The manufacturer could not accurately or consistently calculate On-Time-In-Full delivery performance across its customer orders. OTIF was a stated corporate objective, but the data to measure it wasn't being captured, and the process couldn't handle the complexity of a dynamic allocation environment.

Under the surface, the order-to-ship process had real problems. Allocation calculations were run in Microsoft Access and re-imported to SAP manually. About 80% of orders were immediately blocked on entry and handled with white-glove manual intervention. Ship-date logic was driven by tribal knowledge rather than consistent rules. And the reports that showed OTIF results were misleading: deliveries were only created at the time of physical availability, so the system showed OTIF as structurally complete at Post Goods Issue by construction, regardless of whether the customer received the order on time.

The company ran roughly 1,500 orders per month across 230 SKUs, with more than 50% of orders on allocation at any given time. Every manual workaround had become load-bearing.

Mindset ran a structured Design Thinking engagement focused on the full order-to-ship flow, using the Stanford d.school methodology. The work brought roughly 20 cross-functional stakeholders into the room together, spanning Commercial Analytics, Marketing, Logistics, Distribution and Warehouse Operations, Order Management, National Accounts, Business Analysis, Materials Management, Data Engineering, Operational Excellence, and the SAP and Life Science Platform teams.

The team worked through the Understand, Observe, and Point of View phases, facilitating direct observation of how the people who actually ran order management, logistics, and retail accounts did their work day to day. The team mapped the real SAP allocation flow in detail: the custom tables, the ZSD_ALLOC report, storage location behavior, and ATP logic. That mapping exposed where the data gaps were and where the process had been patched with manual steps.

From there, the team reframed each problem into a user-centered point of view statement and moved toward a prototype that could be tested with real users.

The workshop produced a validated, documented problem statement that the company could act on, along with a clear quantification of the operating environment: 1,500 orders per month, 230 total SKUs, 17 SKUs allocated for all customers and 40 for select customers, more than 50% of orders on allocation, and 80% of orders blocked immediately on entry.

The engagement identified three concrete directions: re-enabling or repairing standard SAP allocations to reduce manual dependency, automating delivery creation for allocated items to remove the timing distortion in OTIF measurement, and correcting the OTIF metric itself to measure against Requested Delivery Date or Customer Must-Arrive Date rather than a Post Goods Issue timestamp.

By the end of the day, the company had a shared picture of why its OTIF numbers were wrong, what SAP changes could fix it, and a user-centered prototype direction ready for the next phase.