Delivery Route Optimisation

Route optimisation should improve completed-delivery cost and reliability without encouraging unsafe driving or excessive order grouping. The system needs accurate inputs and clear constraints; otherwise it merely calculates a precise answer to the wrong problem.

Define the optimisation objective

Possible objectives include:

• meeting promised arrival windows;
• reducing food wait after preparation;
• reducing driver distance and paid time;
• increasing reliable grouped drops;
• balancing work between drivers;
• protecting outer-zone coverage.

Choose priorities explicitly. Minimising distance alone may delay a time-critical order.

Use realistic constraints

Constraint	Why it matters
Ready time	An order cannot leave before preparation and checking
Promise window	Grouping must not break the customer commitment
Bag and vehicle capacity	Physical capacity and product separation limit grouping
Access time	Flats, gates and parking change route duration
Driver shift and break	Availability is not unlimited
Food suitability	Some items tolerate grouping poorly

Evaluate software with historical orders

Use actual Friday or event-night data. Compare the proposed route with what happened, including late-ready orders and failed addresses. Ask how the tool updates after cancellation, reassignment, traffic or preparation delay. Confirm whether staff can understand and override the recommendation.

Control multi-drop delivery

• Set a maximum journey and stop rule based on food and service evidence.
• Do not group only because two destinations share a postcode.
• Label bags and sequence them to prevent the wrong handover.
• Show the customer a realistic promise rather than hiding the extra stop.
• Review complaints and remakes associated with grouped routes.

Avoid unsafe incentives

Do not score drivers solely by route speed or compare journeys without context. The route plan and customer promise should allow lawful, safe travel. A late but safe delivery is an operational planning issue, not evidence that the driver should take greater risks.

Pilot with a rollback

Test one shift or zone, then compare promised arrival, food wait, driver distance, paid time, complaints and manual overrides. Roll back if the tool improves distance while worsening service or staff control.

Protect input quality

Optimisation depends on accurate ready times, validated addresses and current driver availability. Create exception flags for missing access notes, uncertain preparation and stale GPS. The tool should not silently optimise around bad data.

Define dispatcher override and escalation

Staff need to know when they may reject a route, split a batch or change the sequence. Record the override and outcome. Repeated overrides may show that the constraints or travel assumptions are wrong rather than that staff resist automation.

Plan implementation and exit

Confirm integration fields, support ownership, mapping provider, data export and contract terms. Run the optimiser in shadow mode before allowing automatic assignment. Keep the previous dispatch method available during the pilot and document how to return to it if routing or status updates fail.

Review automated decisions

Sample routes regularly and compare the recommendation with the actual outcome. Investigate repeated manual overrides, excessive driver waiting and customer promises missed after grouping. Update constraints only through a controlled change with a test and rollback.

Practical next step: give suppliers ten difficult historical routes and require them to show how the system handles late preparation, access delays and reassignment.

Related guides

• Delivery Management — the main guide for the wider topic.
• Route Planning and Efficiency — the broader guide that frames this implementation.
• Managing Delivery Density — a closely related operational control to review alongside this page.