Global Manufacturer Cuts Unplanned
Downtime by 47% with IBM Maximo

The Challenge

A Tier-1 automotive components manufacturer — supplying to 7 of the world's top 10 OEMs — was operating 8 production plants across Europe, North America, and Southeast Asia. Each plant had evolved its own maintenance management system over decades: SAP PM in Germany, a bespoke CMMS in the US facilities, and spreadsheet-based work order management in the Asian plants.

The consequences were measurable and severe. Unplanned downtime cost the business an estimated £4.7M annually. Maintenance teams had no shared parts inventory visibility, leading to emergency procurement at 3× standard cost. ISO 55001 certification — required by two of their largest OEM customers — was failing audit due to inconsistent asset record quality across sites.

The board's directive was clear: a single EAM platform, across all plants, with the operational data quality to support ISO 55001 certification within 12 months of go-live.

Why IBM Maximo MAS

The platform selection was rigorous. We evaluated IBM Maximo Application Suite, SAP Plant Maintenance, Infor EAM, and IFS Cloud against a 140-point requirements matrix developed with the client's engineering and operations teams. Maximo was selected for three decisive reasons: its IoT-native architecture (critical for the client's 2,400 connected sensors), its Maximo Predict module's failure prediction capabilities, and IBM's pre-built automotive industry configuration accelerators.

Crucially, Maximo's asset hierarchy data model natively supports the hierarchical classification required for ISO 55001 compliance — eliminating the customisation burden that alternative platforms would have required.

Implementation Approach

We used a "lighthouse then replicate" delivery model. The German flagship plant — the most complex, with 4,200 assets and the most mature maintenance data — was implemented first over 12 weeks. This plant served as the blueprint: every configuration decision, every integration pattern, and every data migration process was documented and templated for the subsequent 7 plants.

The IoT integration layer connected Maximo Monitor to 2,400 OPC-UA sensors across the German plant's CNC machines, press lines, and conveyor systems. Maximo Predict was trained on 4 years of historical failure data to generate remaining useful life estimates for 340 critical assets — the foundation of the predictive maintenance programme.

The remaining 7 plants were deployed in two waves of 4 and 3, using the lighthouse template with local configuration variances handled by dedicated site leads. Full deployment across all 8 plants was completed in 22 weeks.

The Outcomes

At 12 months post go-live, the client recorded a 47% reduction in unplanned downtime — from an average of 6.4 hours per asset per year to 3.4. The predictive maintenance programme had flagged 23 imminent failures in its first six months, all of which were resolved during planned maintenance windows rather than becoming unplanned production stoppages.

The consolidated parts inventory visibility — previously impossible across three separate systems — eliminated £680,000 of emergency procurement spend in the first year. The ISO 55001 audit was passed with zero major non-conformances, six months ahead of the client's OEM contractual deadline.