otif-analysis

原文涵盖的主题

• OTIF Analysis
• Required data
• Workflow
• Pitfalls to check explicitly
• Output format

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. 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. 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-ti

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