OTIF Dashboard (On Time In Full)

Deliveries that were BOTH on time and in full, divided by total deliveries — the strictest common form. Some programs compute case-fill × on-time separately or measure at order-line level; Walmart-style programs set thresholds (e.g., 90%+) with fines per percentage shortfall. Match your calculation to each customer's routing guide; this dashboard's both-conditions logic mirrors the strict form.

Programs have run from 87% to 98% targets depending on retailer and era — Walmart's OTIF program moved targets repeatedly (70%→87%→98% across its evolution), with fines around 3% of the cost of goods for non-compliant cases. Grocery and mass retail are strictest. Below-target performance compounds: fines, plus worse slot allocations, plus buyer conversations you don't want.

Under strict OTIF both fail equally — which feels absurd until you see the retailer's side (a short truck and a late truck both break the replenishment plan). Where programs differ (separate fill-rate and timeliness penalties), the math depends on the fine structure. The honest answer: fix the root cause your reason-ranking surfaces, because gaming the lesser penalty is a treadmill.

Attack your top logged reason, not OTIF itself. Appointment misses → book at ETA-firm and track slots; transit delays → re-base promise dates on actual (not quoted) transit from your KPI log; stock shortages → the problem is upstream of logistics entirely. Suppliers who improve 5+ points in a quarter almost always did one specific thing — and they knew which thing because they logged reasons.