otif-analysis

Audits order-level delivery data to compute the OTIF metric ladder and locate lateness concentration.

30.5k|3.5k|Updated Jul 4, 2025
One-click install
npx skills add https://github.com/davila7/claude-code-templates --skill otif-analysis
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: otif-analysis
Source: https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/operations/otif-analysis
Command: npx skills add https://github.com/davila7/claude-code-templates --skill otif-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about otif-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calculate OTIF (on-time in-full) from order data?

OTIF requires order-level rows with requested delivery date, actual delivery date, and completeness information. An order counts as OTIF only if it arrives on or before the requested date with all lines or full quantity delivered, judged at order level rather than averaged across lines.

Why is my on-time delivery score high but customers still complain?

High scores usually reflect measurement choices: tolerance windows, promised-date anchors that hide sales padding, cancelled orders leaving the denominator, and line-level averaging. Computing the metric ladder from tolerant to strict definitions exposes how much each choice inflates the reported KPI.

What data is required for an OTIF delivery analysis?

You need order_id, requested_delivery_date, promised_delivery_date, actual_delivery_date, completeness info (lines or quantities ordered vs delivered), and a status or cancelled flag. Useful segmentation cuts include carrier, region, customer, and product family.

What if my data has no requested delivery date?

Only the promised-date rungs of the ladder are computable, and the analysis cannot detect sales padding between requested and promised dates. The Skill flags this explicitly and recommends capturing the requested date going forward.

How do cancelled orders affect OTIF metrics?

Quietly removing cancelled orders from the denominator flatters the metric. The analysis counts cancelled orders during validation, states how they are treated, and includes a ladder rung with cancellations kept in the denominator to show the effect.