Order Promise Accuracy

A seller tells a customer a machine part will arrive by Friday; the warehouse dispatches it Friday, but delivery occurs Tuesday. A metric based only on dispatch would mark success even though the customer promise was missed.

Oracle documents separate promised ship and arrival dates alongside actual shipment and delivery dates, and its order promising guidance discusses availability estimates, so the business must choose the promise that customers actually received. Record the initial commitment, storing the date, quantity, destination and any agreed delivery window, since a vague "soon" cannot be scored reliably.

Separate ship from arrival, because a promise to ship on Friday is not a promise to deliver on Friday and the metric should name its event. Choose the unit, as order, line, shipment and unit measures yield different results when fulfilment is partial, and report the method clearly.

Keep original and revised dates: if a customer agrees to a change, record when and why, and do not overwrite the first promise and make history look perfect. Measure both reliability and bias, since an order delivered after the promised date misses the window while repeated early delivery may also inconvenience a customer.

Define the window, because some customers accept delivery within a range, and a one-day tolerance must be agreed and applied consistently, not created after misses; a high on-time score paired with many last-minute date revisions may signal a weak promise process. Use actual delivery evidence, since a carrier label or pickup scan is not proof the customer received the goods, and match timestamps to proof of delivery where appropriate.

Check time zones, because a midnight delivery scan can appear on a different day in the customer location, and apply a consistent local convention. Handle split shipments by counting the line accurately and disclosing the rule, since a header-level pass can hide missing units, and separate customer-requested postponements from supplier stockouts with reason codes and approval evidence.

Confirm inventory and capacity, because promising against stale stock can create an impossible commitment, so check reservations, quality holds and inbound supply along with warehouse cutoff, carrier pickup and transit time. Review exceptions such as extreme weather, customs or address errors without deleting them silently from reported performance, and provide updates early if a date is at risk, remembering that communication does not turn a missed original promise into an on-time delivery.

Check demand spikes, since a promotion may overload capacity, and compare similar periods rather than attributing every miss to individual staff. Keep the denominator stable by including eligible orders promised during a period and allowing enough time for outcomes, because excluding undelivered orders can inflate accuracy.

Use error distance too, since a one-day slip and a month-long miss both fail a binary rate but their impact differs, and examine order value and priority because a small delayed accessory and a critical spare have very different consequences. Look for root causes and avoid gaming with distant dates, as a promise far later than feasible can improve the pass rate while worsening customer experience; for owners, order promise accuracy connects operational planning to the date a customer was told, so preserve the original promise and measure the customer-relevant finish.