Inventory Optimisation
Inventory is a balance between money tied up and orders missed. Making that balance explicit — and reviewing it with real numbers — beats a reorder point somebody set years ago.
What this covers
Demand analysis
What actually sells, including the seasonality and the one-offs.
Lead time modelling
Supplier reality rather than supplier promise.
Reorder policy
Levels and quantities derived from a service level you chose.
Exception alerts
Where the policy is about to fail, in time to act.
Supplier performance
Who delivers when they say, measured.
Scenario testing
What a policy change would have done, before it is made.
Who this is for
Distributors
Wide ranges where cash is tied up in the tail.
Manufacturers
Components whose absence stops a line.
Retailers
Availability promises across channels.
Service operations
Spares that decide first-time fix.
How we work
People who have done it before
Teams that have worked in this setting, not a generic squad reading the brief for the first time.
One team, start to finish
The people who design it are the people who build it and the people who answer for it afterwards.
Working software early
Something running in weeks, so the direction is judged against a real thing rather than a document.
Bought once, owned by you
What you keep at the end
The code, the documentation and the accounts are yours throughout. Nothing here depends on us staying, and any arrangement that continues does so because it is worth continuing.
Talk to us
What we promise
- A named team you can reach, not an account manager relaying questions
- A working version early, and every fortnight after that
- Written estimates with the assumptions and the risks stated
- Your code, your data and your accounts, yours throughout
- A direct answer when something is late or will not work
How it runs
Scope
What the solution has to do, what it must integrate with, and what is deliberately out.
Pilot
A working configuration with one team or one process, measured against how it works today.
Roll out
Extended in stages, each with a way back if it does not hold.
Improve
Measured after launch and changed on evidence, which is when most of the value arrives.
Common questions
No. This works from the data the ERP already holds.
Ideally two years. Less is workable with wider tolerances, and we say so rather than implying precision.
Only if you want it to. Most teams start with recommendations and a human decision.
Handled explicitly, because there is no history to model and pretending otherwise produces nonsense.
Would this work here?
A short conversation is usually enough to tell whether this fits your situation. If it does not, we will say so.
Get in touch