Practical Guide: Optimizing Transport Plans for Retailers
Transport optimization in non-food retail uses a mathematical solver to decide which orders to deliver to each store, on which day, in which truck and with which loading plan, at the lowest possible cost. It applies to DIY, furniture, home equipment, sporting goods and discount retailers, whose flows combine bulky products, mixed parcels and strong seasonality.
Five key takeaways
- In non-food retail, the first lever is “when”, not “where”. Without fresh products, the delivery day becomes an optimization variable: smoothing volumes across the week fills trucks without hurting service.
- Trucks are limited by volume, not weight. Furniture, building materials and oversized products fill the floor long before the payload is reached: a transport plan without a 3D load plan is often infeasible.
- One kilometre in five is still driven empty. In 2025, 21.8% of vehicle-kilometres in European road freight were run empty (Eurostat, 2026): supplier returns and reverse flows are a direct source of savings.
- The cost per kilometre is set to rise. The ETS2 carbon market will cover road fuels from 2028, adding an estimated 15 cents per litre of diesel on average (AUTF, 2026).
- Handling priorities, shipping day, loading and routing together cuts transport costs by up to 25%. That is the result achieved by a European furniture leader across more than 300 stores, with a 90% fill rate (Atoptima customer case).
Contents
- What is transport optimization for a retailer?
- What makes store replenishment planning so specific?
- Which optimization levers should you use?
- Customer case: replenishing more than 300 furniture stores
- Which KPIs should you track?
- Glossary
- Frequently asked questions
1. What is transport optimization for a retailer?
Short answer: it is the joint optimization of the store replenishment plan and its transport execution. Where grocery retail first optimizes daily routes under tight freshness constraints, a non-food retailer gains most by choosing what to deliver, on which day and how to load it optimally.
The common thread: the more flexible the delivery date, the more room optimization has, provided you know which orders are priorities and which are not.
2. What makes store replenishment planning so specific?
Short answer: five constraints combine: bulky and oversized products, highly heterogeneous products, seasonal and promotional peaks, limited capacity at both warehouse and store, and differing order priorities. Each one shifts the optimum of the others, which makes manual planning structurally inadequate.
Bulky, heavy or oversized products
A sofa, a flat-pack kitchen, a roll of insulation or a pallet of cement bags cannot be loaded like standard cartons. Non-palletizable loose items, stacking rules and fragility require a feasible, reliable 3D load plan: a truck that is 95% full in theoretical volume may be impossible to load in practice.
Wide ranges and mixed parcels
A single truck carries full pallets, mixed pallets and small parcels. How small parcels are palletized determines the number of pallets, and therefore how full the vehicle is.
Seasonality and promotions
Spring garden season, back-to-school, sales, Christmas, weekly discount arrivals: volumes swing sharply, and some promotional products must be on shelf by a fixed date (see our article on Peak Season).
Limited capacity at both ends of the chain
Each warehouse has a limited daily picking volume. Each store has limited receiving and storage capacity, delivery windows and sometimes access restrictions. A full truck that overflows the stockroom shifts the cost onto the store.
Differing order priorities
A stock-out on the shelf, a click-and-collect order or a promotional product comes before routine replenishment. Omnichannel (click-and-collect, ship-from-store) adds flows with a date commitment to the end customer.
3. Which optimization levers should you use?
Short answer: the biggest gains come from optimizing holistically five decisions that are usually taken in silos by different teams: selecting priority orders, the shipping day, flow consolidation, the load plan and routing.
| Decision | Question answered | Source of savings |
|---|---|---|
| Priority order selection | Which lines must ship now? | Serve stock-outs, customer orders and promotions first, and use routine replenishment to top up trucks |
| Shipping day | On which day should each store be delivered? | Smooth volumes across the week to fill trucks without hurting service |
| Flow consolidation | Which orders go together in which vehicle? | Pool flows from several warehouses and stores to reduce the number of trucks |
| Loading plan | How is the truck actually loaded? | Palletize small parcels, position pallets and oversized items, respect stacking and fragility |
| Routing | In which order should stores be delivered? | Cut kilometres and driving time while respecting delivery windows and access |
Why handle these decisions together?
A transport plan computed without loading assumes a truck is feasible as soon as the total volume fits in the trailer; real parcels, however, do not always fit. Conversely, optimizing the loading of a fixed selection of orders deprives the planner of the most powerful lever: the choice of day.
Additional levers
- Delivery frequency per store: at tactical level, moving a store from 3 to 2 deliveries a week with fuller trucks, or the reverse to relieve a stockroom that is too small.
- Return flows: collecting pallets, packaging, customer returns and supplier pickups on the way back, instead of returning empty.
- Fleet mix: choosing between dedicated fleet, chartered trucks and parcel carriers according to daily volumes, as 3PLs and carriers do.
- Simulation: quantifying, before deciding, the opening of a warehouse, a change in delivery frequency or the arrival of a low-emission zone.
4. Customer case: replenishing more than 300 furniture stores
Short answer: by jointly optimizing order priorities, palletization, 3D loading and routing, a European leader in home equipment, furnishing and interior decoration cut its transport costs by 25%, with a 90% fill rate and a computation time of under one minute.
The customer’s challenge
The retailer replenishes more than 300 stores with products ranging from small decoration parcels to oversized furniture. The goal was to organize tactical and operational replenishment planning by handling, in a single model:
- delivery volume planning, with selection of priority order lines;
- flow consolidation, assigning each line to a vehicle and a shipping day;
- routing, under constraints of priorities, vehicle capacity (volume and weight), delivery windows, picking volume per warehouse and deliverable quantities per store;
- the loading plan: palletization of small parcels, positioning of pallets and oversized loose items, stacking restrictions.
Solution and results
Atoptima’s optimization engine makes these decisions together rather than locally. It produces realistic flow consolidations and load plans that can be executed directly at the dock.
| Indicator | Result |
|---|---|
| Transport costs | -25% |
| Filling rate | 90% |
| Computation time | < 1 min |
The retailer also reduced left-behind freight and significantly improved its store service level. Read the full customer case.
5. Which KPIs should you track?
Short answer: the pivotal indicator is transport cost per unit shipped (€/m³, €/pallet or €/parcel), complemented by fill rate, left-behind freight and store service level. A cost saving achieved by degrading service is not a saving.
| KPI | What it measures |
|---|---|
| Cost per unit shipped | Full transport cost per m³, pallet or parcel delivered |
| Fill rate | Truck utilization in volume, weight and loading metres |
| Left-behind freight | Volume planned for departure but not loaded, for lack of space or a feasible plan |
| Store service level | Share of orders delivered on the planned date, especially stock-outs and promotions |
| Empty running rate | Share of kilometres driven without a load, especially on return legs |
| CO2 emissions | Emissions per unit shipped, driven by mileage and fill rate |
Tracking these indicators by store and by weekday reveals structurally sub-optimal deliveries that are invisible in an overall average.
Conclusion
DIY, furniture, home equipment or discount: transport for non-food retailers depends less on the order of stops than on the choice of orders, day and load plan. These decisions are linked: an order’s priority sets its possible date, the date sets the truck, and the truck only exists if the parcels actually fit.
No planner can weigh all these combinations by hand across hundreds of stores. A solver does it in minutes, with RouteSolver for transport planning, PackSolver for palletization and 3D loading, and FlowSolver for flow optimization.
Non-food retail transport glossary
| Term | Definition |
|---|---|
| 3D loading | Positioning each pallet and parcel in the truck, taking into account real dimensions, weight and stacking rules |
| ETS2 | The second EU Emissions Trading System, which will add a carbon cost to road fuels from 2028 |
| Fill rate | Share of vehicle capacity (volume, weight, floor space) actually used |
| Flow consolidation | Grouping orders from several lines, stores or warehouses in the same vehicle |
| Left-behind freight | Goods planned for departure but not loaded, carried over to a later shipment |
| Mixed pallet | A pallet combining several SKUs, often built from small parcels |
| Non-palletizable loose items | Products loaded directly into the truck, without a pallet |
| Oversized product | A product whose size or shape prevents it from fitting on a standard pallet |
| Routine replenishment | Regular, non-urgent restocking of a store, whose date can shift |
| Ship-from-store | Fulfilling an online order directly from a store’s stock |
| Stackability | The ability of a pallet or parcel to bear another load placed on top of it |