SAP Failure Codes for
Tier 1 Oil and Gas Client
Project Overview
A Tier 1 Global Oil and Gas operator engaged
Their existing SAP PM environment had failure codes that had been integrated since the SAP implementation. These were broad, generic and not asset specific. The result was fragmented data and component naming, with no use of damage and cause codes that could support reliability analysis or be meaningfully compared across sites. Cross site benchmarking was impossible. Their predictive maintenance ambitions and AI readiness roadmap were blocked at the foundation, because the data itself could not support them.
The client approached this work as a pilot project. It was a deliberate decision to prove the methodology, measure the outcomes, and build an internal business case for scaling the approach enterprise wide, while also enabling AI models to learn from clean data before their dead data could be annotated for use.
Scope Of Work
The scope of our activities included:
- Asset classification with defined boundary limits for each of the 44 asset class types, establishing what is inside and outside the asset scope.
- Development of standardised equipment hierarchies, with one consistent parent and child template per asset class type, applied uniformly across every asset instance.
- Construction of a maintainable item and component ontology derived from the equipment hierarchy, providing the bridge between engineering analysis and SAP PM record structure.
- Creation of a validated FMEA library covering the dominant failure modes for each asset class type, structured for fleet wide reusability.
- Translation of FMEA failure modes into SAP PM catalog profile conventions, including maintainable item codes, component codes, damage codes, and cause codes.
- Preparation of SAP compliant loadsheets for every asset class type, validated through a sandbox environment prior to production load.
- Structured batch based review cycles with the client's SAP master data team for approval, followed by loadsheet submission for production deployment.
Why Pilot approach first
The client recognised early that SAP failure code development at this depth, combining reliability engineering, asset classification, SAP master data structure, and catalog profile compliance, is specialist work. It requires an end to end capability that is uncommon in the Australian market. This includes reliability engineering expertise that does not stop at the FMEA report, SAP integration knowledge that does not stop at technical compliance, and industry specific asset understanding that does not rely on generic templates.
By running the engagement as a pilot, the client could validate the methodology on a defined, high value scope, measure the outcomes against their traditional approach, and build the internal business case for scaling the work across their broader asset base. The pilot became the template. Everything that follows it can be scaled without rebuilding the foundation.
Approach
Our team:
- Analysed the client's existing SAP PM and Catalog Profiles design configuration, and existing FMEA's for assets in scope to leverage their existing language and nomenclatures.
- Applied
Shivaan Asset Management 's data chaos to AI readiness framework, a five step methodology spanning Asset Classification, Equipment Functional Hierarchy, CMMS taxonomy standardisation, FMEA library development, and Engineer to SAP language translation. - Engaged with reliability engineers and the SAP master data team throughout the project, ensuring the outputs reflected business and operating context.
- Leveraged Nexaan APM to accelerate the structured data work, with pre built AI trained equipment hierarchies for 200+ asset class types, AI agents enabling data standardisation and scaling at every step, and output formats aligned directly to SAP PM catalog profile conventions.
- Delivered outputs in structured batches. Each batch was submitted for client review, approved, validated in the client's sandbox environment, then loaded into SAP production.
The five step methodology applied
Our data chaos to AI readiness framework turns fragmented SAP failure code environments into governed, reusable, AI ready data. Each step builds on the last, with clear inputs, actions, and outputs along the way.
Classify and Define Boundaries
Every one of the 44 asset class types is classified with defined boundary limits. Ambiguity is eliminated at the start and every downstream deliverable references the same asset scope.
Functional Hierarchy
Equipment is systematically organised into logical parent and child structure for maintainable item and component standardised ontology. One hierarchy template per asset class type, applied uniformly.
Standardise CMMS Taxonomy
A consistent naming and classification convention is applied across the asset register. It becomes the addressing system for every asset entering the SAP environment, now and in the future.
FMEA Library (80/20 Rule)
The dominant failure modes driving the majority of operational impact are documented in a structured FMEA library. Standardised, reusable, and validated for fleet wide application.
Translate Engineer to SAP
FMEA failure modes are converted into SAP PM catalog profile data, including maintainable item, component, damage, and cause codes. Delivered as loadsheet ready files for internal sandbox testing and production deployment.
From data chaos to AI readiness in five governed steps.
Project Outcomes
Every one of the 44 asset class types was delivered, reviewed and approved within the 8 week timeline with great support from internal stakeholders.
The outcomes delivered across the engagement:
- 5 asset classes and 44 asset class types with complete classification, boundary limits, and consistent equipment hierarchies.
- Standardised maintainable item and component ontology replacing fragmented site by site conventions with a single governed structure.
- Complete FMEA failure mode library for all 44 asset class types, structured for scalable fleet wide reuse.
- SAP PM catalog profile compliant failure code loadsheets covering maintainable item, component, damage, and cause codes, delivered to the client's SAP master data team for production loading.
- 100% standardisation across all deliverables, compared to the 5%+ error and inconsistency rate typical of traditional manual approaches.
- All technical capability and delivery categories scored 5.0 out of 5.0 in the client's independent post project feedback, with a Net Promoter Score of 10 out of 10.
- Recognised by the client as a trusted partner of choice for reliability engineering to SAP integration work, positioning
Shivaan Asset Management for the next phase of scaled rollout across their broader asset base.
Traditional approach vs Shivaan AM with Nexaan APM
The comparison below shows the scale of improvement against how this work has historically been delivered in the industry. Same scope, same rigour, radically different delivery outcome.
| Metric | Traditional Approach | Shivaan AM + Nexaan APM |
|---|---|---|
| Duration | 12 to 18 months | 8 weeks Up to 9× faster |
| Team required | 5 to 8 reliability engineers | 2 engineers 75% fewer resources |
| Cost (AUD) | $800K to $1.9M | Less than 10% of traditional 90%+ cost reduction |
| Output format | Spreadsheets, manual SAP loadsheet development | Standardised, SAP compliant, loadsheet ready |
| Standardisation | At least 5% errors or inconsistencies | 100% with zero errors |
| Knowledge retention | Leaves with the engineer | System governed, stays with the business |
| Reusability for future assets | Low, engineer dependent | Full, governed and fleet scalable |
Independent client feedback across 11 assessment dimensions
At project completion, the client completed an independent feedback assessment covering technical capability, delivery quality, and collaboration. All 11 dimensions were rated 5.0 out of 5.0.
Assessment dimensions
Value Delivered
The 8 week delivery and the cost saving are the headline. The structural change to how the client's asset data works, and keeps working, is the enduring value.
The primary value delivered to the business:
- A governed failure code library that survives team changes. Because failure codes were built to a structured ontology rather than assembled from individual engineer knowledge, the library persists in SAP as a managed asset. When experienced reliability engineers move on, their knowledge remains in the system. It stays documented, governed, and accessible to anyone in the business.
- Reusability that compounds over time. When a new asset of the same class type enters the fleet through acquisition, capital expansion, or new site development, the failure analysis does not need to be redone. The codes already exist, correctly structured, and ready to apply. The investment in this pilot pays forward into every future project of the same asset type.
- Cross site benchmarking that is actually credible. Every asset of the same class type is now coded consistently using the same damage and cause codes. Failure patterns across sites can be compared meaningfully. The data is no longer an artefact of different coding conventions. It reflects real operational behaviour.
- A foundation for AI and predictive maintenance. Machine learning models, digital twins, and predictive maintenance programs can only be as accurate as the data feeding them. Structured, consistent failure data is the prerequisite, not an afterthought. This pilot established that foundation for the client's broader digital roadmap.
- Lower cost of SAP ownership. With a governed catalog profile structure and consistent hierarchy underlying it, SAP PM data can be extended, audited, and managed without degrading the standardisation established here. New assets land in the right place. Existing assets can be reviewed against a known standard. The long term cost of maintaining data integrity falls.
- ISO 55001 (Asset Management Systems), ISO 14224 (Collection and Exchange of Reliability and Maintenance Data for Equipment) and ISO 55013 (Management of Data Assets) alignment. The governed library aligns naturally with the asset information, reliability data, and data asset management expectations of these standards. Documentation, traceability, and controlled change position the client's Asset Management System for maturity assessment and continual improvement.
- A validated template for scaling. The pilot deliverables now serve as the template for extending failure code governance across the client's broader asset fleet, with the confidence of a proven methodology, a measured outcome, and an internal business case grounded in real data.
By delivering this engagement as a structured pilot, accelerated by Nexaan APM,
This is not master data cleanup. It is the architectural foundation on which reliability, predictive maintenance, digital twin, and AI initiatives actually work.
Working with Shivaan Asset Management and Jitesh was an easy engagement with consistent communication throughout. The deliverables were high quality, delivered on time and fit for purpose.
The framework and methodology was impressive to work with and the technical depth was exactly what this work needed. They rbought great experience with assets, knowledge of how assets operate, Reliability Engineering and SAP master data was immaculate. Nexaan APM was a game changer for this work.