Inventory optimisation for a global mining and processing company
PTL Co developed consistent inventory policies and scenario models for a complex international materials supply chain.
THE CHALLENGE
Creating consistent inventory policies globally
Client
Specialised mining and minerals processing
Sector
Mining and minerals
Scale
Operations across North America, Europe and Asia
Engagement
Inventory optimisation
PTL Co helped a mining and processing business establish a more consistent inventory policy through a seven-stage review covering more than 200 SKUs, 12 months of demand history and four connected policy levers. The work focused on demand behaviour, service requirement, stock position and lead time across an international supply chain with varied product profiles, supply routes and replenishment constraints.
Existing inventory parameters did not consistently reflect item behaviour, lead time or service requirements. This made it difficult to distinguish essential buffers from avoidable stock and to apply common rules across the wider operation. The client required an evidence-based policy that recognised demand variability, replenishment constraints and the commercial importance of different products. It also needed scenario analysis to show the likely inventory and service implications of revised settings before implementation, together with clear controls and ownership for ongoing management.
200+
live SKUs
$9m
total inventory value
12,000
tonnes of stock located across 3 continents
4 levers
demand, service, stock and lead time
WHAT WE DID
A seven-stage programme
PTL Co validated inventory and demand data across more than 200 SKUs, reviewed 12 months of demand behaviour and tested lead-time, service and replenishment assumptions across the product base. The seven-stage review converted inconsistent policy settings into scenario models that showed how different stocking rules would affect inventory, availability and operational control before the client moved into implementation. This gave the business a clearer way to separate essential buffers from avoidable stock while recognising the practical constraints created by international supply routes, variable demand and different product priorities.
1. VALIDATE DATA
Check inventory and demand information to create a reliable baseline before changing policy settings.
2. SEGMENT PRODUCTS
Classify items by behaviour, value, demand profile and operational importance so policy rules reflected different product roles.
3. ANALYSE DEMAND
Assess variability and consumption patterns to understand which items required genuine protection and which were carrying avoidable stock.
4. REVIEW LEAD TIMES
Test supply assumptions and replenishment constraints so stock policies reflected real-world availability and logistical risk.
5. DESIGN POLICY
Set four connected policy levers covering demand, service, stock and lead time so availability, investment and control could be balanced across the product range.
6. MODEL SCENARIOS
Quantify inventory and service impacts to show the trade-offs behind each proposed policy change.
7. PLAN IMPLEMENTATION
Define controls, ownership and next steps so revised policies could be introduced and maintained consistently.
WHAT WE ACHIEVED
Outcomes that carried into day-to-day operation
The client gained a consistent global inventory policy framework covering more than 200 SKUs and four core policy levers, with scenarios built from 12 months of demand history and tested through a seven-stage review. The approach gave management clearer visibility of the trade-offs behind each policy choice, helping avoid simplistic stock reduction decisions and creating a more controlled route to implementation. It also gave teams a practical foundation for reviewing inventory decisions in future, making policy ownership, service assumptions and exception management easier to govern across a complex supply chain.
Structured inventory model.
Stocking rules were linked to product behaviour, demand profile and operational importance, allowing the business to move away from generic settings and apply more appropriate policies across the SKU base.
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Clearer policy settings.
Service requirements, lead-time assumptions and supply constraints were brought into the policy logic, giving teams a more consistent basis for deciding which items needed protection and which could be controlled more tightly.
Scenario visibility.
Inventory and service trade-offs could be reviewed before implementation, helping management understand the consequences of different stocking assumptions rather than relying on a single target position.
Implementation roadmap.
Controls, ownership and review steps were defined so the revised policies could be introduced consistently and maintained as demand patterns, lead times and product priorities changed.
PTL CO EXPERTISE
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