Order Fill Rate by Promised Date

A distributor ships every ordered unit, but several deliveries arrive after the date it promised, so its basic fill rate can look strong while customers miss their own production schedules. Order fill rate by promised date combines completeness with a clearly defined time promise.

APQC publishes separate measures for order fill rate and the percentage of sales orders delivered on time, and McKinsey discusses defining on-time, in-full consistently, including the point where service is measured; this composite should state exactly how it joins quantity and timing. Define a complete order, deciding whether every line and quantity must arrive or whether an allowed substitute counts, and write this before calculating results.

Define the promise as the customer-agreed delivery date, not merely the date a warehouse planned to ship, storing the initial promise and later agreed changes. Choose the time event too, since carrier pickup, arrival at the customer dock and accepted delivery are different milestones, and pick the one that matches the customer commitment.

Set the grain, because whole-order pass rates differ from line-level or unit-level rates and a one-unit shortage can fail an entire order under a strict whole-order definition. Fix the denominator to include eligible orders due in the period, including those still unfulfilled, since excluding open late orders can overstate performance.

Handle partial shipments by stating customer-approved split rules, because several on-time parcels satisfy an order only when the full promised quantity arrives by the date, and handle early deliveries, since some customers reject stock that arrives before a booked slot and on-time may mean a window. Distinguish requested from promised: a customer may request tomorrow but agree to next week, so measure against the commitment actually made and track request-to-promise gaps separately.

Track promise revisions, because repeatedly moving the date can make a metric look good without improving service, and keep both original and final agreed promise measures. Capture evidence from shipment records, carrier proof and customer receipt, since an internal status change is not always proof of delivery, and preserve timestamps and reasons for customer-requested quantity or address edits that reset the agreed scope.

Treat cancellations separately, since a customer cancellation before fulfilment should not become an on-time success and the definition must say whether seller-caused cancellations count as failures. Investigate failures by classifying stockout, picking, transport, promise setting and customer unavailability: link stockouts to replenishment and allocation decisions, warehouse pick errors, packing delays and missed cutoffs to execution, and carrier exceptions, customs and weather to transport, reporting cause and customer impact without automatically removing difficult cases.

Segment customers and products, since a blended company-wide number can hide poor service for critical accounts, and set escalation rules so a predicted miss prompts early customer contact and recovery planning, not just a red mark after the due date. Pair the measure with lead time, since a business could promise much later dates to improve it, and with quality, since a technically complete on-time delivery may contain damaged goods or wrong documents, which the perfect-order measure can add.

Count only customer-approved equivalents as full, because a different item shipped without approval is not a pass. Avoid gaming by retaining an audit trail, since holding an order open until a new date is entered or deleting late orders undermines the metric; for an owner, this measure asks whether customers receive the whole agreed order when they expected it, and the definition must not erase a broken promise.