Driving reliability and working capital efficiency across 12+ Tier-1 mining operations, spanning 10+ countries and 750,000+ active spare parts SKUs.
Mining is among the most asset-intensive industries on earth. Five years of rapid growth had left each site running its own criticality model and replenishment rules, the pattern typical of decentralised MRO inventory management run mine by mine rather than as one network. That fragmentation showed up as excess stock at some sites, dangerous exposure at others, and $30M to $50M a year in avoidable cost across the portfolio.
Downtime cost: $250K to $400K per hour. Single 18-hour mill stoppage: ~$5M in lost production. 3 to 4 major stockout events annually: $15M to $20M total exposure.
The download covers the full six-pillar transformation framework, the financial exposure model behind the $30M to $50M diagnostic, and the multi-year results across all 12+ mine sites.
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Deployed across all major mining operations with no ERP replacement, the framework replaces static, site-level rules with risk-calibrated intelligence that spans the entire network.
Multi-dimensional criticality scoring tied to throughput and safety, paired with failure-driven demand modelling across the asset lifecycle.
Pillars 1–2Probabilistic, service-level-driven stocking in place of static min-max rules, with predictive inputs drawn directly from maintenance history.
Pillars 3–4Systematic identification of non-moving stock, plus network-wide visibility that rebalances duplicate components before a new purchase order is raised.
Pillars 5–6Measured over a multi-year deployment horizon, these results directly address the $30M to $50M annual exposure identified in the diagnostic phase.
| Area | Outcome |
|---|---|
| Working capital | $40M+ unlocked across global operations |
| Inventory reduction | 15% to 22%, without increasing production risk |
| Emergency procurement | 25% reduction in rush orders and premium freight |
| Service level | 20% improvement for critical parts |
| SKUs classified | 750,000+ enterprise-wide, risk-based |
Results reflect documented outcomes for this deployment. The full financial, operational, and enterprise-level breakdown is in the downloadable case study.
Responsible for uptime on high-value processing assets where a single stoppage costs $250K to $400K per hour.
Managing MRO procurement across multiple mine sites, reducing emergency spend and airfreight premiums.
Accountable for planned shutdown execution and parts availability weeks before mobilisation begins.
Managing multi-instance SAP and Maximo environments, looking to add intelligence without replacing the ERP.
This case study covered five core challenges behind one $30M to $50M diagnostic, the six-pillar Enterprise Spare Parts Inventory Intelligence Framework, and the complete multi-year results. If you're weighing where your own operations stand before a decision like this, start with a benchmark.
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