Using forecast demand to improve delivery decisions

Illustrative schematic

One booking changes the next decision

Explore the new customer request

Overview Nearby request A Distant request B

Reading the picture

• Confirmed orders
• Forecast orders not yet placed
• Planned route

Decision for a new request

Slot A is near the route and may be offered at a lower charge.

Slot B requires a detour and may displace future orders; a higher charge or withholding it may be appropriate.

Opportunity cost estimates the expected profit lost on future orders if this request is accepted.

1. Future demandForecast where orders may arrive.
2. Forecast ordersReserve provisional route capacity.
3. Slot availabilityCheck which windows remain feasible.
4. Opportunity costEstimate future profit displaced.
5. PricingUse the estimate to set delivery charges.
6. Route updateReplace forecasts as bookings arrive.

Customer flexibilityWindow 1Window 2Adjacent windows may be combined

Simulated resultsDoctoral research · studied settings

13.57–21.43%

simulated profit improvement relative to the comparison policies across the reported research scenarios

0%10%20%25%

The problem

An online grocer lets each customer choose a delivery slot when they place an order. Every accepted order constrains the routes the vans can drive, and therefore which slots can still be offered to the customers who have not yet ordered. The retailer wants to fill its vans profitably and avoid unnecessary driving, but at the moment it makes each decision it does not know who else will order, where they live or which slot they will want.

The standard tools treat these as separate problems: forecast demand, then price slots, then plan routes. My research connected them. The central idea is to forecast the orders still to come and to include them, provisionally, in the routing plan, so that the estimated cost of accepting any new request reflects the future orders it might displace. That estimate, the opportunity cost of a slot, then drives which slots are shown to a customer and what delivery charge attaches to each.

My contribution

• Forecast orders inside dynamic routing. Rather than planning routes only around confirmed orders, the routing model carries a set of forecast orders that are progressively replaced by real ones as bookings arrive. This gives a more realistic picture of remaining capacity in each area and slot (European Journal of Operational Research, 2023).
• Better opportunity-cost estimates. The 2026 paper improves the estimate of revenue lost by accepting a request, using the forecast-based dynamic routing approach, and uses it to set delivery charges.
• Dynamic slot combination. Many customers are flexible about when they receive a delivery. The 2026 paper introduces an augmented time-window strategy that exploits this flexibility to improve route efficiency without changing the underlying routing system or requiring complex computational modifications.
• Emissions as well as profit. Because unnecessary driving is a direct environmental cost, the thesis treats reduced mileage as an objective alongside profitability rather than a by-product.
• A further extension, incorporating the effect of delivery-price reductions into the opportunity-cost estimate, is currently under revision for publication.

How the work was evaluated

The methods were tested in computational experiments driven by real-world customer and operational data from a UK online grocer. Each experiment simulates a booking horizon: customers arrive over time, are shown slots and prices generated by the policy under test, make choices according to a fitted choice model, and the resulting orders are routed. Policies are then compared on profit, delivery efficiency and service.

Across these experiments the proposed policies achieved simulated profit improvements of 13.57–21.43% relative to the comparison policies under the studied settings. The range summarises those research scenarios; it should not be interpreted as a single live operational trial.

These are simulation results. They show what the policies would have earned under the modelled behaviour of customers and vans on real historical data; they are not a report of profit realised in Ocado’s operations, and the research was conducted independently as doctoral work rather than as a commercial engagement.

What the work produces in practice

For an operator, the outputs are practical rules rather than a single number: which slots to display to a customer at a given address and time, what delivery charge to attach to each, when to combine adjacent slots to gain flexibility, and how to update the plan as orders arrive. A useful property of the approach is that it can be implemented within existing systems that use standard time windows, so a retailer can adopt it without redesigning its routing software.

Limitations

• Results depend on the fitted customer-choice model; if customers respond to prices differently from the model, the profit estimates shift.
• Forecast orders are only as good as the demand forecast behind them. The research assumes a reasonably stable booking pattern, which large promotions or disruptions would violate.
• The experiments cover one retailer’s operating context. Transfer to other delivery settings (parcel, pharmacy, meal kits) is plausible but was not tested.

Why this matters beyond grocery

The same pattern, forecast what is still to come and let that forecast shape the decision you make now, appears in field-service scheduling, appointment booking, ride-hailing and any business that sells constrained capacity over a booking horizon. The research is also the origin of the way I approach applied forecasting work: the forecast is not the product; the decision it supports is.

The results above are simulated research. Explore a routing example →

Publications from this work

2026

Efficient Forecast-Based Routing and Dynamic Time Window Management for Attended Home Deliveries

Mohammad Abdollahi, Xinan Yang and Michael Fairbank. Annals of Operations Research. Open access

DOI: 10.1007/s10479-026-07285-9 PDF

2023

Demand Management in Time-Slotted Last-Mile Delivery via Dynamic Routing with Forecast Orders

Mohammad Abdollahi, Xinan Yang, Moncef Ilies Nasri and Michael Fairbank. European Journal of Operational Research, 309(2), 704–718.

DOI: 10.1016/j.ejor.2023.01.023