Agent skill · data analytics · davila7
otif-analysis
Audit delivery performance from order-level data - compute the OTIF metric ladder (tolerant to strict), find where lateness concentrates, and quantify the gap between the reported KPI and what customers experience. Use when the user mentions OTIF, on-time delivery, delivery performance, late orders, teslimat performansı, zamanında teslimat, or asks why customers complain despite a high on-time score. Differentiator - exposes measurement choices before optimizing operations.
Why this skill is useful
Provides a detailed methodology for calculating and analyzing the OTIF metric, which includes specific data requirements and analytical steps that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
OTIF Analysis Most "delivery problems" are measurement problems first. Before recommending any operational fix, establish what the honest number is and which definition choices inflate the reported one. Required data Order-level rows with: orderid, requesteddeliverydate (what the customer asked for), promiseddeliverydate (what was confirmed), actualdeliverydate, completeness info (lines ordered vs delivered, or qty ordered vs delivered), and a status/cancelled flag. Useful cuts: carrier, region, customer, product family. If requesteddeliverydate is missing, say so explicitly: only the promised-date rungs are computable, and the analysis cannot see sales-padding. Recommend capturing the requested date going forward. Workflow 1. Validate before computing. Count duplicated order rows; flag impossible dates (actual before order date); count cancelled orders and state how they will be treated. Report these counts in the output - an audit that silently cleans data is not an audit. 2. Compute the metric ladder on the same population, strictest last: L1: on-time vs promised date, with the tolerance window currently in use (ask what it is; if unknown, show +3 days and label it) L2: on-time vs promised, zero tolerance L3: on-time vs requested, zero tolerance L4: OTIF = on-time vs requested AND order complete (all lines / full qty). In-full is judged at order level - a 9-of-10-lines delivery is not 90% on-time, it is one incomplete order L5: OTIF with cancelled orders kept in the denominator Present the ladder as a table with the delta and the cause of each drop (tolerance, padding, partials, cancellations). 3. Decompose the gap. For the dimension with the largest spread (try carrier, region, month, customer, product family), show OTIF per segment. Name the concentrated driver, not just the average. 4. Analyze the tail, not the mean. Average lateness hides the distribution. Report the share of orders 4+ days late and the worst decile - those are the orders customers remember. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/otif-analysis