Unplanned downtime costs mining operations up to 50% of their margins, often driven by fragmented data, inconsistent BOMs, and poor maintenance records. The PiLog DQG Suite eliminates these risks by cleansing and consolidating your data. Gain reliable asset masters, unified BOMs, and accurate history to maximize uptime, enhance safety, and protect your bottom line.



Every one of these challenges has a data root cause. PiLog DQG Suite addresses each systematically – not as a one-off project, but as a sustained governance program.
Poor material master data creates duplicate spare parts records across multi-site operations. BCG research shows 5-15% of MRO inventory. Clean MDM delivers 15% spare parts improvement.
tied up in redundant or obsolete stock, costing per redundant part annually
Mining average Overall Equipment Effectiveness (OEE) sits below 70% vs. The 85% + world- class benchmark. Clean equipment hierarchies, accurate BOMs, and standardized failure codes are prerequisites for data-driven OEE Optimization.
OEE improvement represents millions in additional throughput
Inaccurate asset hierarchies and missing BOMS prevent root-cause analysis and predictive maintenance. Maintenance costs by 18-25% – but only when built on clean, enriched asset master
data.
McKinsey shows predictive maintenance reduces unplanned downtime
Fragmented vendor master data leads to duplicated freight contracts, emergency procurement. MDM- driven vendor rationalisation and spend visibility consistently delivers 8-15% logistics savings across multi-site operations.
freight premium on rush orders
Al initiatives in mining – covering equipment health, ore grade prediction, and autonomous haulage analytics etc. – require clean equipment masters, accurate sensor-to-asset linkages, and enriched maintenance histories.
Gartner (2024) research warns that 60% of Al projects lacking Al-ready data eventually FAIL, and 63% of organisations currently lack Al-ready data practices. Without a trusted MDM foundation, predictive models generate false alarms that maintenance teams ignore – destroying both Al Rol and stakeholder trust.
of expected ROI due to poor data quality (Gartner 2024)
when data prep time drops from 80% to 20%
achieved by PiLog clients vs. 30% industry average
Every capability in PiLog DQG Suite is domain-specific, built for the complexity of offshore platforms,
remote terminals, and multi-facility EAM environments.
PiLog’s Al-powered deduplication and ISO 14224 taxonomy standardisation identify hidden duplicate MRO stock across all mine sites, release trapped working capital and reduce emergency purchases by 20-40%. For a $100M inventory portfolio, that’s $3M-$10M+ released.
25M+ Gold Records
30K+ ISO-Standard Taxonomies
Deduplication
SAP MM
Vendor Master
Reduction in Unplanned
Downtime
Inventory Portfolio
OEE: world-class target from <70% industry average
Reduce Emergency Purchases
PiLog builds ISO & other industry standard compliant equipment hierarchies, normalises failure codes, links BOMs to asset masters, and enables full lifecycle cost roll-up. This data foundation unlocks OEE dashboards, MTBF/MTTR analytics, and predictive maintenance targeting the 85%+ world-class benchmark.
ISO 14224
Asset Hierarchy
SAP APM / EAM
Lifecycle Costing
BOM <-> Spares Linkage
Reduction in Unplanned
Downtime
Annual savings for typical Mining & Metals operator
OEE: world-class target from <70% industry average
Maintenance cost reduction via predictive approach
Companies moving from SAP ECC to S/4HANA risk multi-million-dollar project overruns without clean master data. PiLog’s expertise ensures 95%+ data accuracy at cutover, Clean Core compliance, and on-time delivery protecting your SAP investment from underperforming due to poor data
S/4HANA Migration
Clean Core Compliance
BOM <-> Spares Linkage
SAP Endorsed App
Post-Go-Live Governance
Data accuracy at
go-live cutover
Project overruns and
remediation costs avoided
Go-live achieved with PiLog DQG
Suite vs. 30% timeline slips
Return on MDM investment for
RISE/GROW program
PiLog creates Al-ready data: enriched equipment masters, standardized schemas, governed change histories. Clients achieve 4x data-scientist productivity gains, 90%+ Al project success rates, and capture McKinsey’s benchmark of 15% supply chain cost reduction through Al optimisation.
ISO 14224
Asset Hierarchy
SAP APM / EAM
Lifecycle Costing
BOM <-> Spares Linkage
AI project success rate with PiLog DQG Suite vs. 30% industry average
Data scientist productivity (data prep time reduced 80% → 20%)
Supply chain cost reduction via AI (McKinsey benchmark)
Faster AI time-to-value vs. 12-24 month AI pilots
The only MDM platform built specifically for asset-intensive industries – with 25M+ golden records, ISO 8000 and ISO 14224 adherence, and 220+ enterprise integrations. Every Mining capability described above runs on PiLog DQG Suite.
Asset Master
Material Master
iContent Foundry
AI Ready Operations
These are not projected estimates: they are benchmark outcomes
achieved across PiLog’s Mining client base

MRO Inventory Reduction

Maintenance Productivity Gain

Unplanned Downtime Reduction

Investment Payback Period
Despite massive working capital tied up in inventory, a major mining operator struggled with material records that lacked reliability and significant data fragmentation.
Let us quantify the downtime, maintenance leakage, and inventory waste in your specific Mining environment and build a business case for fixing it.
The same data lifecycle management expertise applied to the specific challenges of your industry.
Oil & Gas
Utilities & Power
Manufacturing
Transportation
Defence
Pharma & Life Sciences
Retail
Chemicals
PiLog Group is a global specialist providing end to end Data Lifecycle Management for asset intensive industries, with over 30 years of expertise, and 300+ customers globally
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