Inventory optimisation consulting
Decide where inventory is necessary, where it is excessive and which planning assumptions need to change. I help European manufacturers and supply chain teams assess those trade-offs using their own data.
The problem to solve
Reducing inventory everywhere can expose critical operations to shortages. Increasing it everywhere ties up cash and can create obsolete stock. Optimisation starts with different policies for different materials, based on demand, supply uncertainty and operational consequences.
Useful starting data
- Material-level stock, usage and demand history
- Stock value, ageing and known obsolete inventory
- Supplier lead-time variation and order constraints
- Service priorities, critical materials and existing stock policies
How I approach the work
- 01
Agree the stockout, inventory value and availability objectives.
- 02
Segment inventory by operational importance, demand pattern and supply uncertainty.
- 03
Review where buffers, lot sizes or slow-moving stock deserve attention.
- 04
Compare proposed policies and agree a staged review with your planning team.
What we can agree to deliver
The scope and outputs are agreed before work begins, based on your priorities and the data available.
- A segmented inventory policy with documented assumptions
- Prioritised shortage and excess-stock opportunities
- An agreed baseline and review plan for tracking changes
How is inventory optimisation different from replenishment planning?
Inventory optimisation considers which stock policies best balance availability, cost and uncertainty. Replenishment planning applies those policies to when and how much to order. The two should be reviewed together, but they answer different decisions.
Can you guarantee an inventory reduction?
No. The appropriate inventory level depends on demand, supplier reliability, operating constraints and service priorities. Some critical materials may need more protection while other materials can be reduced. Any recommendations need validation against your data.