Shivaan Asset Management delivered SAP compliant failure codes (catalog profiles) for a Tier 1 oil and gas client: codes built for each asset class type and loaded into SAP production, in place of codes too general to analyse.
An 8-week pilot across valves, pumps, compressors, engines and gas turbines.
A Tier 1 Global Oil and Gas operator engaged Shivaan Asset Management to develop and deliver asset classifications, standardised equipment hierarchies, library FMEA data, and SAP catalog profile-ready failure code loadsheets across their most critical rotating and flow control assets.
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. Shivaan Asset Management delivered the complete pilot scope in 8 weeks.
Shivaan Asset Management was contracted to design and deliver a governed failure code library for five critical asset classes commonly found across oil and gas processing, compression, and flow control operations, covering 44 distinct asset class types in total.
The scope of our activities included:
How asset classes, class types and variations are defined
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.
Shivaan Asset Management combined reliability engineering experience, deep SAP PM master data knowledge, and oil and gas asset specific understanding with a structured, phased delivery approach that kept the client's operational teams engaged without disrupting their day to day work.
Our team:
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.
Read the framework in full, phase by phase
From data chaos to AI readiness in five governed steps.
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:
The illustrative comparison below shows the scale of improvement against how this work has, in our experience, historically been delivered in the industry. Same scope, same rigour, radically different delivery outcome.
| Metric | Traditional approach (illustrative) | Shivaan AM |
|---|---|---|
| Duration | 12 to 18 months | 8 weeks |
| Team required | 5 to 8 reliability engineers | 2 engineers |
| Cost (AUD) | $800K to $1.9M | Less than 10% of traditional |
| Output format | Spreadsheets, manual SAP loadsheet development | Standardised, SAP compliant, loadsheet ready |
| Standardisation | At least 5% errors or inconsistencies | No errors identified in the client's assessment |
| Knowledge retention | Leaves with the engineer | System governed, stays with the business |
| Reusability for future assets | Low, engineer dependent | Full, governed and scalable enterprise-wide |
Comparison basis: a typical traditional manual failure code development programme of equivalent scope. The traditional figures, and the improvements derived from them, are illustrative rather than sourced benchmarks.
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, with a Net Promoter Score of 10 out of 10 (charted below).
The enduring value is the structural change to how the client's asset data works, and keeps working. Shivaan Asset Management did not deliver a consulting report. We delivered a governed capability that the client now owns, operates, and extends independently.
The primary value delivered to the business:
What changed in ISO 55001:2024
By delivering this engagement as a structured pilot, Shivaan Asset Management gave a major Global Oil and Gas operator the foundation they needed for long term data governance. In a fraction of the time, at a fraction of the cost, and with an outcome measurable enough to build the case for enterprise-wide rollout.
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 brought great experience with assets, knowledge of how assets operate, Reliability Engineering and SAP master data was immaculate.
Failure codes are the damage and cause codes recorded against Notifications and Work Orders in SAP PM. Here they dated back to the original SAP implementation and were too generic to be useful: the same broad codes applied to unlike components, so the records could not feed reliability analysis or stand up to comparison between sites.
The scope ran through a five-step methodology, with outputs issued in structured batches rather than as one final report. Each batch was reviewed and approved by the client, validated in their sandbox environment, then loaded into SAP production, so review and loading ran alongside development instead of after it.
A pilot let the client 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 deliverables then became the template, so everything that follows can be scaled without rebuilding the foundation.
A governed failure code library that lives in SAP as a managed asset rather than in the knowledge of individual engineers, standardised equipment hierarchies and a maintainable item and component ontology, a validated FMEA library structured for enterprise-wide reuse, and the loadsheets and conventions needed to code new assets of the same class type without redoing the analysis.
Independent client assessment: all 11 dimensions rated 5.0 out of 5.0. Dimensions: Reliability engineering (RCM, FMEA); Asset hierarchy quality; Failure code development; SAP master data and catalog profiles; RE to SAP integration; Industry asset understanding; Communication and availability; Deliverable quality and accuracy; Timeline management; Value for investment; Professionalism.
Independent client feedback
At project completion the client completed an independent feedback assessment covering technical capability, delivery quality and collaboration. Every dimension was rated at the maximum.
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