Manufacturing & logistics
Manufacturing and logistics
A plant, a depot or a field-service business lives inside hard limits: so many machines, so many crews, so many bays and vehicles and hours. Which orders you accept, how you batch them, how you schedule the bottleneck and how you route around it set service, utilisation and margin for the week, and they are usually decided before the shift starts by three people who do not sit together. We model those decisions as one problem, so the plan holds when the forecast is wrong, which it will be.
The decisions that bind here
Four decisions competing for one capacity
Acceptance, batching, scheduling and routing are handled by different people as if they were different problems. They compete for the same finite capacity, and the cost of treating them apart is paid in overtime, missed dates and idle machines.
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Which orders to accept?
A profitable order can still be the wrong one once it displaces work already promised. We weigh expected margin against the capacity and the commitments it consumes.
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How to batch and sequence?
Setup, changeover and travel are costs like any other. Batching well protects throughput, and batching badly starves the orders that matter most.
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How to schedule the binding constraint?
People, machines, bays and skills are all limited and rarely limited together. A feasible plan respects every binding constraint at once.
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How to route and dispatch?
For logistics and field service, the sequence of stops, loads and crews is a constrained optimisation. Small routing gains recur on every run.
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Service, inventory or margin?
These trade against each other continuously. The question is which plans stay good across the futures you can expect, and a single best plan is rarely one of them.
What it costs to get them wrong
Why a full schedule can still lose money
Utilisation is not margin
A plant can run at full utilisation and still have taken the wrong mix. Every hour spent on a low-margin order is an hour a better order, arriving next week, will not get.
The tail sets the cost
A plan optimised for the average week breaks on the bad one: the yield miss, the late supply, the rush order. The expensive days are exactly the ones an average forecast cannot see.
Accept now, pay later
An order accepted today books capacity a better order tomorrow will need. The cost arrives weeks later as overtime, expediting or a penalty clause, and by then nobody connects it to the acceptance.
How we engage
How an engagement runs here
We do not arrive with a scheduling platform. We start on one decision, prove on your own data that a better rule beats the current one, and only then build the system that runs it. Each step is paid for separately, and each can be the last.
1. Audit
Find the decision that pays
A Decision Opportunity Audit runs on one acceptance or scheduling decision, establishes the baseline and the economic stakes, and ends candidly, including the possibility that your current rules are already close to good enough.
2. Proof
Beat the current rule, on your data
A proof of value tests a policy against your existing heuristic on a shared benchmark before anyone trusts it, with the comparison reported in full. Our standard for evidence is set out in how we validate.
3. Build
Put it where the plan gets made
Only what proved out is built into the planning workflow and owned by your team. Find, Prove, Build keeps scope following evidence, and treats a decision system as a system you can run, in place of a dashboard.
What we need to see
Order and demand history, the resources and constraints that bind: capacities, skills, changeover, travel. And how you record acceptance, service and cost today. Where a baseline was never captured, establishing one is the first deliverable. An optimiser cannot improve on a number nobody is measuring.
The solution behind it
The method behind it
A single optimal schedule is optimal only for the forecast you fed it. The capacity and scheduling solution models the uncertainty explicitly and returns a small set of plans that differ in kind, each defensible, so the choice between service, inventory and margin stays yours. For a worked illustration on synthetic data, see the case Which orders to accept when capacity, skill and margin all bind.
Where it applies
- Discrete and process manufacturers with changeover and yield risk.
- Logistics, distribution and fulfilment operations planning loads and routes.
- Field-service businesses dispatching constrained crews and skills.
- Any operation where demand routinely exceeds capacity and the mix is a choice.
The research behind it
The method, published
When we are a fit
- Demand routinely exceeds capacity, so acceptance and sequencing bite.
- Constraints are physical and knowable: machines, skills, bays, vehicles, service windows.
- Someone can act on a plan and change what the operation accepts or schedules.
When we are not
- Capacity comfortably exceeds demand, so scheduling is not the binding problem.
- No usable record of orders, resources or the constraints that bind.
- The plan cannot change what the operation does day to day.
Who you deal with
A partner owns the engagement from the first plant visit to the last invoice, and is the person you call when a schedule does not hold. Work is confidential and unattributed by default: no plant, route or customer of yours appears in a deck or on this website without written permission. An audit is a fixed fee agreed before it starts. References are owners and operations directors who will tell you plainly what did not work.
FAQ
Questions from manufacturers and operators
Which decisions do you work on here?
Order acceptance, batching and sequencing, scheduling the binding constraint, routing and dispatch, and the continuous trade-off between service, inventory and margin. They compete for the same finite capacity, so we model them together, in one pass.
Do you arrive with a scheduling platform?
No, and we would be wary of anyone who did. We start on one acceptance or scheduling decision you make today, prove on your own data that a better rule beats your current one, and only then build the system that runs it. A platform bought first has to justify itself afterwards, and most never do.
What data do you need to start?
Order and demand history, the resources and constraints that bind: capacities, skills, changeover, travel. And how you record acceptance, service and cost today. Where a baseline was never captured, establishing one is the first deliverable.
What if our current scheduling is already good?
Then the audit says so, and we do not recommend building anything. It is a finding we have delivered before, and the planner who has been running the schedule by hand for fifteen years is usually pleased to hear it.
Start with the decision
Bring us one decision
One acceptance or scheduling decision you make today. A short audit run by a partner will say what the current rule costs you in displaced capacity, and whether a better one is worth building. The fee is fixed before we start.