How PIMvendors helped a global mining group consolidate 500,000+ product records across 12 regions into a single, AI-enriched catalogue that cut procurement costs and accelerated maintenance turnaround.
The situation
A global mining and commodities organization operates across 12+ regions, each running its own procurement, warehousing, and maintenance processes. Over decades of organic growth and acquisition, every site had built its own way of describing products: different languages, different classification logic, different levels of completeness. The result was a fragmented catalogue of over 500,000 product records spread across multiple ERP instances, procurement platforms, and local spreadsheets.
Nobody had a single version of the truth. The same hydraulic pump might appear under five different descriptions in three different languages, classified under two different UNSPSC codes, with no linkage between them. For an organization spending billions annually on MRO (maintenance, repair, and operations) materials, this was a cost control, safety, and operational performance problem.
Maintenance crews couldn’t find the right parts. Procurement couldn’t identify duplicate spend. Strategic sourcing couldn’t consolidate suppliers. And every regional team had learned to work around the mess rather than fix it, because previous cleanup attempts using generic AI tools and bulk reclassification had failed. Mining-specific parts, think crusher liners, conveyor belt segments, and specialized filtration media, don’t appear in consumer product databases. Generic enrichment tools produced garbage output.
The organization engaged PIMvendors to design and deliver a solution across three workstreams: building a unified product data model, selecting and implementing the right PIM/MDM platform, and deploying AI-driven cleansing and enrichment tooling purpose-built for industrial MRO data.
Workstream 1: Product data model design
The problem to solve: No two regions described products the same way. Attribute structures varied wildly. Some sites used detailed technical specifications; others relied on free-text descriptions entered by warehouse operators. There was no shared taxonomy, no agreed attribute set, and no governance framework defining what “complete” product data should look like.
What we did: PIMvendors designed a unified product data model anchored on UNSPSC classification (United Nations Standard Products and Services Code), the most widely adopted taxonomy for indirect materials and MRO. We mapped existing regional data structures against this model to identify gaps, conflicts, and redundancies. The model defined mandatory and optional attributes per product category, standardized units of measure, and established naming conventions that work across languages.
Critically, this was not a theoretical data modeling exercise. We worked with procurement leads, maintenance engineers, and warehouse managers in four pilot regions to stress-test the model against real operational scenarios. If a maintenance technician searching for a conveyor idler roller couldn’t find it within three clicks using the new model’s classification and attributes, the model needed revision.
The outcome: A single, UNSPSC-aligned product data model that covers 100% of the organization’s MRO categories. Each category carries a defined attribute template with validation rules, so data quality is enforced at the point of entry rather than cleaned up after the fact.
Workstream 2: PIM/MDM platform selection
The problem to solve: The organization had no central system of record for product data. ERP master data modules were used for transactional purposes but offered no workflow, governance, or enrichment capability. Data stewards in each region maintained their own “golden records” in Excel. Any attempt at centralization required a platform that could handle multi-regional governance, multi-language support, and the specific data volumes and complexity of industrial MRO catalogues.
What we did: PIMvendors ran a structured vendor selection process, starting from a long list of 15+ PIM and MDM platforms and narrowing to a shortlist of 3 based on fit-for-purpose criteria. Those criteria went beyond standard feature checklists. We evaluated platforms on their ability to support UNSPSC-based hierarchies at scale, handle multi-source data onboarding from heterogeneous ERPs, integrate with the AI enrichment tools we planned for Workstream 3, and operate within the organization’s IT architecture and security requirements.
We facilitated scripted demos with each shortlisted vendor using the client’s own data, not generic demo datasets. Evaluation panels included IT, procurement, maintenance, and data governance stakeholders. PIMvendors provided an independent scoring matrix and recommendation, with total cost of ownership projections across a 5-year horizon.
The outcome: A PIM/MDM platform selected and implementation roadmap delivered within 10 weeks. The chosen platform supports the organization’s scale (500,000+ SKUs, 12+ regional data feeds, 8 languages) with built-in workflow for data stewardship, approval routing, and supplier data onboarding.
Workstream 3: AI-driven cleansing and enrichment
The problem to solve: Even with a unified data model and a central platform, the existing data was still a mess. Over 40% of records consisted of free-text descriptions with no structured attributes. Classification accuracy across the catalogue was estimated at below 60%. Duplicates were rampant. And generic AI tools, including large language models fine-tuned on consumer product data, consistently failed on industrial MRO items because they lacked the domain context to distinguish between a Type 3 butterfly valve and a Type 5, or to correctly classify a ball mill liner as mining-specific rather than generic metalwork.
What we did: PIMvendors identified, evaluated, and deployed niche AI tooling specifically designed for industrial and MRO data enrichment. These tools are trained on millions of industrial product records and technical data sheets, which gives them the domain vocabulary and classification intelligence that general-purpose AI lacks.
The AI pipeline operated in three stages. First, automated classification: each record was mapped to the correct UNSPSC code based on its description, existing attributes, and contextual signals. Second, attribute extraction: structured attributes (material, dimensions, operating specifications, OEM part numbers) were parsed from free-text descriptions and populated into the data model. Third, duplicate detection: probabilistic matching algorithms identified duplicate and near-duplicate records across regions, flagging them for consolidation.
Every AI-generated output passed through a human-in-the-loop validation step. Data stewards reviewed AI suggestions in batches, accepting, correcting, or rejecting them. This feedback loop continuously improved model accuracy while ensuring that the organization maintained full control over its master data.
The outcome: Free-text records dropped from over 40% to below 10%. Classification accuracy rose to above 95%. Duplicate clusters were identified and consolidated, reducing the active catalogue by an estimated 15-20%. New product onboarding, which previously took weeks of manual entry and cross-referencing, now takes days.
The combined impact
The three workstreams delivered compounding value. The data model provides the structure. The PIM/MDM platform provides the governance and operational backbone. The AI tooling provides the speed and scale to transform hundreds of thousands of records in a timeframe that manual effort could never match.
All 12+ regions now work from a single trusted product catalogue. Procurement runs reliable spend analysis for the first time, enabling supplier consolidation and volume-based negotiation that was previously impossible. Maintenance teams find the right parts faster, reducing equipment downtime. And the organization has a scalable foundation: every new product, new supplier, and new regional operation feeds into the same model, the same platform, and the same AI-enhanced data pipeline.
For an organization of this scale, even a 1-2% improvement in procurement efficiency on MRO spend represents tens of millions in annual savings. A single source of truth for product data is the prerequisite for capturing that value.
Why PIMvendors
Most PIM consultancies advise on software. PIMvendors designed and delivered across all three layers: the data architecture, the technology selection, and the AI-powered data transformation. That integrated approach is what made this engagement successful where previous isolated efforts had failed. Fixing the data model without fixing the tooling produces a beautiful taxonomy that nobody populates. Deploying AI without a sound data model produces fast, confidently wrong output. Selecting a platform without understanding the data and the enrichment workflow produces an expensive system that doesn’t fit.
PIMvendors brought independent, vendor-neutral expertise across all three dimensions, and delivered measurable results within months, not years.
