At a glance
Absolute count of DPD parcels that arrived after the promised delivery date in the last 7 days. The actionable companion to OTD Rate, OTD says “what percent missed”, this card says “how many tickets are about to land in CS”. Late means past the calendar date; out-of-slot is a separate measurement on Predict Slot Accuracy.
| What it counts | COUNT(parcels WHERE actual_delivery_at > estimated_delivery_at) over the trailing 7 days. Each late parcel scores 1; multi-piece consignments count one per parcel, not one per consignment. |
| Delivery success criterion | DPD parcelStatus = DELIVERED with a POD scan timestamp. In-transit parcels with no actual_delivery_at yet are excluded from this count even if their estimated date has already passed; track those on the in-flight live count. |
| On-time threshold | DPD’s estimatedDeliveryDate per parcel, no grace window. A parcel delivered at 23:55 on the promised date counts on-time; 00:05 the next day counts late. |
| Service-level scope | All DPD services pooled (Next Day, Next Day by 12:00, Next Day by 10:30, Saturday, Sunday, EU Classic, EU Express). Predict-slot parcels score against their date; the in-slot score is on its own card. |
| Tracking event semantics | DPD pushes a delivered webhook on POD scan. The card uses the first delivered event; subsequent corrections (carded then redelivered next day) leave the original timestamp in place. PUDO-shop drop-offs scan delivered when the parcel reaches the shop, so a late parcel that PUDO-recovered late counts late on this card. |
| Returns / RTO | Excluded. Refused, undelivered-after-3-attempts, and RTO parcels appear on Returned to Sender, not here. |
| Geographic scope | UK domestic + DPD UK outbound to EU and rest-of-world. EU origins served by DPD Group sister carriers report through their own connectors. |
| Peak-period degradation | Q4 typically lifts the count by 2 to 4x vs the trailing 30-day baseline; this is normal for any UK courier. The alert threshold (>5% of total) is a rate, not a flat count, so it scales with volume. |
| Lost vs late | This card includes late-but-arrived parcels only. Truly lost parcels (no scan in 14 days) appear on Open Claims; permanently undelivered surface as Failed Deliveries. |
| Time window | 7D (rolling 7 days; the 30D rate is OTD Rate) |
| Alert trigger | >5% of total (late count exceeds 5% of total parcels delivered in the same 7-day window) |
| Roles | owner, operations |
Calculation
Calculated automatically from your DPD data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
A UK premium-DTC consumer-electronics brand (AOV £240, ~1,400 orders / week) ships every order via DPD Next Day with predict-slot enabled. Reading taken at 09:00 GMT on 12 Mar 26 for the trailing 7 days (05 Mar 26 to 11 Mar 26).| Service tier | Parcels delivered | Late count | Late rate |
|---|---|---|---|
| Next Day (standard) | 1,920 | 58 | 3.0% |
| Next Day with Predict | 740 | 19 | 2.6% |
| Saturday | 180 | 11 | 6.1% |
| EU Classic | 145 | 17 | 11.7% |
| All DPD (this card) | 2,985 | 105 | 3.5% |
>5% of total alert is not tripped at the aggregate. Five things to notice:
- The 105 is roughly the CS-ticket volume to expect this week. Industry rule of thumb: ~80% of late-delivery customers contact support (where-is-my-parcel tickets), of which ~25% escalate to refund-the-shipping-fee or refund-in-full. Plan ~85 WISMO tickets and ~20 refund requests against this number.
- EU Classic 11.7% is the live problem. Even though the absolute count is small (17), the rate is over 2x the alert threshold for that lane. Likely a customs-clearance issue at the destination depot; pair with UK to EU Cross-Border Exception Rate to confirm.
- Saturday 6.1% trips on rate but only 11 parcels. Saturday delivery is paid premium (~£3 to £5 uplift) and customers expect higher reliability. 11 missed Saturdays = 11 weekend-ruined customers; the cost in goodwill exceeds the £1k revenue at risk.
- Cost of the 105 misses. At AOV £240 with industry-average 4% post-late refund propensity, exposure is ~£1,000 in refunds + ~£400 in CS handling time + unmeasured churn. Compare against Late-Delivery Revenue at Risk on the DPDLocal connector for the proper finance-CFO calculation.
- Compared against same week last year (05 Mar 25 to 11 Mar 25), this brand had 84 late on 2,610 parcels = 3.2%. Volume up 14%, late rate flat. DPD network performance is stable year-on-year for this account; the alert is volume-driven, not service-decay-driven.
Sibling cards merchants should reference together
Late count is the operational trigger metric. Pair it with these to triage the root cause and the downstream business impact.| Card | Why pair it with Late Count | What the combination tells you |
|---|---|---|
| On-Time Delivery Rate | The rate counterpart of this absolute count. | Rate = late count divided by total parcels. If volume is climbing while late rate holds, the absolute count rises mechanically; do not panic. |
| Predict Slot Accuracy | Out-of-slot is a different miss. Most out-of-slot parcels are on-time-by-date and do not show here. | High late count + high slot accuracy = network-wide date misses; high in-slot misses + low late count = predict promise breaking but parcels arriving same day. |
| Exception Rate | Catches parcels that never reached the customer. | Late + rising exceptions = a slice of “late” is actually “lost or refused”; verify against claims. |
| OTD by Route | Splits the misses by destination. | One-region late spike = local-depot or driver issue; spread across all routes = peak-period or weather. |
| Failed Deliveries | The structural-failure counterpart. | Late = arrived after promise. Failed = never arrived (3 attempts then RTO). The two are sequential; today’s failed cohort started as last-week’s late cohort. |
| Open Claims | Downstream financial impact. | Late count predicts claim volume at 5 to 10 days lag (customer waits a few days, then files). |
Cross-connector: shopify.unfulfilled_orders | Upstream cause. | Climbing unfulfilled count predicts a late-count spike 1 to 2 days later (warehouse couldn’t pick in time, parcel handed to DPD too late for SLA). |
Cross-connector: shopify.refund_rate | Downstream impact. | Late count rise of 2 to 3x precedes a 0.5 to 1.5 pp refund-rate rise at 7 to 14 days lag. |
Reconciling against the vendor’s own dashboard
Where to look in DPD’s own dashboard: MyDPD Business portal -> Track -> Filter “Late deliveries” -> Last 7 days. The portal shows per-parcel rows with the gap between estimated and actual delivery; export as CSV for the audit trail. The aggregate count is on Reports -> Service Performance -> Late Parcels with the same date filter. Why our number may legitimately differ from MyDPD’s portal:| Reason | Direction | Why |
|---|---|---|
| Time zone | Boundary days off | MyDPD defaults to Europe/London (BST or GMT). The card uses UTC. The count for the most-recent boundary day can shift by a few parcels. |
| Tracking-feed lag | Ours lower for last 4 to 6 hours | DPD pushes status webhooks within 5 to 30 seconds of each scan, but rural-route POD scans may not upload until the driver returns to depot. Most-recent-day numbers fully reconcile by T+1. |
| Peak-period throttling | Ours lower during BFCM | Webhook queue can throttle during BFCM peak; deliveries may post 2 to 6 hours late. T-2 days fully reconcile. |
| Predict-slot vs date semantics | MyDPD higher (some views) | MyDPD’s “Late” filter includes both date-misses and slot-misses by default. The card here counts date-misses only. Toggle the MyDPD filter to “Date late only” to align. |
| Re-attempt timestamp | Ours uses first delivery | If a parcel was carded then delivered next day, the card uses the first delivered scan (next day). MyDPD’s “delivered date” column shows the same. Some merchants run their own warehouse-side reports against the carded date which inflates the count. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
shopify.unfulfilled_orders | Upstream cause. Late picks become late shipments. | Persistent unfulfilled count rising while DPD late count holds = warehouse problem not yet hit DPD; expect the spike in 24 to 48 hours. |
shopify.refund_rate | Downstream impact. | 2 to 3x rise in late count -> 0.5 to 1.5 pp rise in refund rate at 7 to 14 days lag. |
Cross-3PL: shipbob.sb_otd_rate | Peer 3PL outcome metric. | Different parcel populations entirely. Do not arithmetically reconcile. |