Introduction
Last-mile delivery is the final leg of the supply chain — and it's where most of the cost lives. Industry estimates put last-mile costs at 41–53% of total supply chain costs. [13, 14] For local couriers and 3PLs, it's effectively the entire cost structure.
Most last-mile cost waste is preventable. Failed deliveries, inefficient routes, manual exception handling, and billing processes that require hours of spreadsheet work are not unavoidable — they are operational gaps with fixable causes.
Tactic 1: Optimise routes (not just plan them)
There is a meaningful difference between planning routes and optimising them. Manually planned routes are typically 15–30% longer than algorithmically optimised ones. [12] On a 60-stop route, that gap can represent 45–90 minutes of unnecessary drive time per driver.
The value of optimisation is in whether the routes generated match how your operation actually runs, not just whether they minimise raw distance. Look for time-window constraints, vehicle capacity limits, toll/highway/ferry avoidance, and clustering by terminal or geography. [1]
Tactic 2: Cut failed first-attempt deliveries
A failed delivery doubles its own cost — driver time and fuel are consumed twice. Industry estimates suggest 5–10% of delivery attempts fail on the first try. [12, 14]
Three interventions address most failures:
- Address validation before dispatch: catch bad addresses before routes are dispatched, not after a driver arrives at a non-existent location. [1, 15]
- Delivery time windows: customers who know when to expect delivery make themselves available.
- Structured driver instructions: access codes, building entry instructions, and preferred drop locations surfaced automatically through the driver app.
Tactic 3: Capture why deliveries fail (not just that they did)
Structured failure reasons — customer not home, wrong address, refused, damaged, access issue, weather, other — each with a driver note, turn individual failure events into operational intelligence. [1]
Exception reports that answer why deliveries fail let you identify address data quality problems in specific client feeds, recognise patterns by building type or geography, and report exceptions to client accounts with the specificity required under service agreements.
Tactic 4: Improve load density through smarter clustering
Underutilised vehicles generate the same fuel, labour, and vehicle wear as full ones — with less revenue per stop.
Geographic clustering in route optimisation produces compact route areas with more stops per mile and less dead-head driving. Optimisers that cluster by terminal, customer, and geography — as Alchemira does — produce routes that stay within natural geographic groupings. [1]
Tactic 5: Make exception handling a structured workflow
In many courier operations, exception handling is informal: driver calls dispatch, dispatch calls the client, someone figures out a resolution, nothing is tracked. At scale, this becomes a significant labour cost and a source of client relationship risk.
Structured exception handling means failures are captured in the system at the moment they occur, linked to the order record and client account, and populate client-facing reports at the end of the billing period. [1]
Tactic 6: Close the billing week without spreadsheet surgery
For 3PLs, billing at end of week is often more work than it should be. When order data, delivery confirmations, and exception records live in different systems, someone manually assembles invoicing data from multiple sources.
A delivery platform with proper billing report functionality produces a report by customer, by date range, by delivery status — all of which can be exported as CSV and ready for invoicing. The on-time delivery report compares actual timestamps to due dates with variance in minutes. [1]
Tactic 7: Track cost per delivery and find the real cost drivers
Cost per delivery (total operational cost divided by successful deliveries) is the north star metric for last-mile efficiency. Tracking it weekly, by client account and by route area, surfaces patterns that management intuition alone can't see.
Supporting metrics: first-attempt success rate; stops per driver per hour; exception rate by client; fuel cost per delivery; billing cycle time. [1]
A practical roadmap for implementation
- Weeks 1–2: Implement VRP-based route optimisation with time-window constraints and address validation.
- Weeks 3–4: Add structured failure reason capture to the driver workflow.
- Month 2: Review billing and exception reports from the first four weeks. Identify top failure reasons.
- Month 3: Analyse route density and zone efficiency. Adjust clustering or terminal assignments.