What problem does it solve?
Reported on-time delivery scores often look healthy while customers still complain about late or incomplete orders. This Skill audits order-level delivery data to expose how measurement choices (anchor date, tolerance windows, cancelled orders, line-level averaging) inflate the reported KPI, and shows the honest OTIF number customers actually experience.
Core Features & Use Cases
- Metric Ladder Computation: Calculates five rungs from tolerant promised-date on-time through strict OTIF (on-time vs requested date AND in-full), with deltas and causes for each drop.
- Gap Decomposition: Segments OTIF by carrier, region, customer, month, or product family to name the concentrated driver of lateness rather than just the average.
- Tail Analysis & Reconciliation: Reports the share of orders 4+ days late and the worst decile, then recomputes the headline OTIF from raw rows to verify consistency.
- Use Case: A supply chain analyst has a 94% reported on-time score but rising customer complaints. The Skill reveals that a 3-day tolerance window and sales padding of promised dates hide a true OTIF of 78%, concentrated in one carrier and region.
Quick Start
Analyze my order delivery data and compute the OTIF metric ladder to explain why customers complain despite our high on-time score.