Supplier On-Time Delivery

A manufacturer expects 100 orders from suppliers in a month, and if 92 arrive within its agreed time window, an order-based on-time delivery rate is 92%. The number alone does not say whether the eight late orders contained critical parts.

Choose the denominator, since counting purchase orders gives each order one vote while counting lines or units weights orders differently, and a large shipment partly late can be classified in several ways, so define the rule before reporting. Agree which date is tested, which may be the original purchase-order promise, an approved revised promise, the requested date or the time goods were actually available for use, because quietly moving the promised date after a delay can make the metric look good without improving supply.

Oracle's supplier-delivery documentation compares receipt date with promised date and lets a company configure days allowed early or late, and a tolerance can reflect operations but should not hide meaningful misses. Record receipt correctly, since a truck arriving at a gate late at night is not always the same as stock checked in and ready for production.

Distinguish the supplier's portion of the process from internal delays, because an on-time truck can still wait for inspection or warehouse booking and both stages should be investigated before assigning a late result to the supplier. An illustrative formula is on-time qualifying orders divided by orders due in the period times 100, so if 92 of 100 due orders qualify the result is 92%.

Cancelled orders should be excluded according to a written rule rather than whichever treatment improves the rate. The Association for Supply Chain Management lists on-time supply among practical supply-chain KPIs and links it to supplier reliability, and a useful measure helps identify a pattern and guides a conversation, not merely a ranking.

Look at delivery completeness as well, since a supplier may arrive by the deadline but deliver half the quantity, and pair timeliness with quantity accuracy or an on-time-in-full measure when complete supply matters. Inspect criticality, because one late low-value bolt may stop a plant while several late discretionary items may have little impact, so segment the results by item class and production consequence.

Analyse trends and variability, since a supplier averaging 95% can be hard to plan around if misses cluster around seasonal peaks, and the distribution of delay days may matter more than the average. Check whether the buyer contributed, because late approvals, changing forecasts, unclear specifications or urgent orders outside agreed lead times can make promised dates unrealistic, and exception reasons should stay visible without automatically forgiving every miss.

Use lead-time data for planning, since repeated late deliveries can justify earlier orders, alternate sources or a review of supplier capacity, while extra safety stock costs cash and should be targeted to items where disruption matters. Avoid punishing early delivery blindly, because some sites cannot store goods early while others benefit from an early arrival, so a defined window should reflect capacity and contract terms, and validate the data, since order amendments, multiple receipts, return shipments and incorrect timestamps can alter the rate and a sample of results should be traced back to orders and receiving records.

Discuss improvement with the supplier by showing the specific dates, items and causes and agreeing a corrective step with an owner, because a rate without operational context may damage a useful partnership, and compare suppliers fairly, since a local vendor delivering stocked items has a different challenge from a custom overseas manufacturer, so show lead times and service terms alongside the percentage. For an owner, this KPI answers how reliably promised supply arrives in time to be useful, so pair the percentage with complete quantities, critical item impact and a credible cause for the misses.