Improved Service Reach
Network design aligns closer to customer and plant demand geographies.
Network Optimisation - Redesign for Cost and Service Balance
Optimize node structure, inventory placement, and transport flows for measurable performance gains.
Legacy Networks Rarely Match Current Demand
As demand patterns shift, older network designs become expensive and slow. We model alternative network scenarios to improve service reach while reducing cost-to-serve.
How We Optimize Networks
1
Demand and Node Analysis
Analyze demand clusters, service expectations, and current node performance.
2
Scenario Modelling
Model alternate node, lane, and inventory placement scenarios.
3
Phased Rollout Plan
Create an execution sequence with risk controls and KPI milestones.
1
Demand and Node Analysis
Analyze demand clusters, service expectations, and current node performance.
2
Scenario Modelling
Model alternate node, lane, and inventory placement scenarios.
3
Phased Rollout Plan
Create an execution sequence with risk controls and KPI milestones.
Lower Cost-to-Serve
Optimized lane and node structures reduce avoidable movement and overhead.
Higher Planning Confidence
Data-backed scenarios support stronger decision-making before rollout.
The number of stocking points is the decision
Network design comes down to how many places inventory sits and what each of them is for. Almost everything else - transport cost, service level, working capital - follows from that.
The tension is straightforward. Fewer stocking points mean less total inventory for the same service level, because demand variability pools rather than being covered separately at each location. More stocking points mean shorter final legs and faster response. Neither is universally right, and the balance moves with the cost of transport relative to the cost of holding stock.
What makes this analysable rather than a matter of opinion is putting both costs in the same model - freight, handling, holding, and the cost of the service failures each design permits. Optimising freight alone reliably produces too many warehouses; optimising inventory alone produces too few.
Serve from the nearest stock, not the nearest owner
Many networks allocate demand by relationship - the site that owns a customer serves that customer - rather than by proximity or availability. It is an organisational convention rather than a logistics decision, and it is often the single largest recoverable inefficiency in an otherwise sensible network.
Changing it requires two things that are not always present: visibility of stock at location level across every site, and a rule for which site serves which demand that everyone accepts in advance. Without the rule, allocation reverts to ownership under pressure, because that is the default nobody has to justify.
Where both exist, the same physical network delivers noticeably better service without any change in inventory, which is why this is usually the first thing worth testing before considering a new location.
- Model freight, handling, holding and service failure together, not separately.
- Test allocation rules on the existing network before adding or closing sites.
- Give every site a defined role, so new SKUs and demand spikes have an obvious home.
Design for the demand you have, not the average
Network models are frequently built on average volumes, which produces a design that is correct for a month that never occurs. Real networks are constrained by their peaks and by the shape of their variability.
Two flows with identical annual volume can require quite different networks if one is steady and the other arrives in concentrated bursts. The steady flow can be served from further away with scheduled transport; the bursty one needs either local stock or the ability to surge capacity, and those are different investments.
So the useful inputs are the distribution rather than the mean - how often demand exceeds capacity, by how much, and for how long. A design that handles the ninetieth percentile and degrades gracefully above it is usually cheaper and more robust than one built for the average and rescued by premium freight.
Node Rationalization
Consolidate or expand network nodes based on modeled demand and service outcomes.
Lane Optimization
Reconfigure lane architecture to improve fill, frequency, and turnaround.
Hybrid Program
Combine node, inventory, and transport redesign in one transformation roadmap.
Distributed network with high transfer dependency and uneven service levels.
18% Reduction in Logistics Spend
Modeled network redesign improved service consistency and reduced route inefficiencies.
Read Full Case StudyFrequently Asked Questions
We run structured scenario modelling with demand and lane assumptions to compare outcomes.