Project showcase

CMMS Data Enrichment and Preventative Maintenance Optimization

Project goal

To enhance the plants equipment reliability and optimize maintenance and materials expenditure without compromising safety barriers.


Power Plant


4 Months




Why Add Energy Were Contracted

The power plant was facing the following challenges:

  • Issues with equipment reliability and critical system outages
  • Inability to accurately plan and schedule maintenance effectively
  • Money and time was being wasted buying spare parts with the wrong specifications
Why Add Energy Were Contracted

The Initial Review

To reduce costs associated with the improvements required, Add Energy conducted an initial CMMS Data Analysis review to identify the extent of the gaps in the asset register, in order to put a focused plan in place to plug the gaps and enrich the data.

The Initial Review

This review identified:

  • Significant gaps in existing asset register; equipment on the physical plant was not full replicated in the asset register

  • 86% of tags in the asset register had missing name plate data (manufacturer, model, serial number), making it difficult to source the correct spares

  • The asset register hierarchy was flat, making it difficult to identify equipment inter-dependencies (parent/child/package/system/equipment)

  • 87% of equipment was missing “equipment type” coding, meaning it was difficult to run analysis at the correct level and efficiently assign maintenance

  • 46% of equipment was assigned the wrong criticality ranking, making it difficult to understand how important a piece of equipment is and thus how it should be maintained

  • 69% of the equipment was not assigned any criticality rankings, meaning it was difficult to prioritize maintenance and backlog, as well as identify optimum stocking levels for spares

  • Maintenance allocation and frequencies were much higher than expected on equipment that was deemed as “non-critical”

  • Maintenance work instructions on equipment classified as “critical” were very high level making the task requirements subjective

  • 67% of maintenance was derived from OEM recommendations, which did not consider the operating environment and demand

How We Helped to Enrich and Optimize the Data

In order to help improve the plants equipment reliability, assure safety and optimize expenditure, Add Energy:

  • Completed a physical asset verification (PAV)
  • Enriched and optimized the asset register
  • Conducted an equipment criticality analysis
  • Developed an optimized maintenance management regime
How We Helped to Enrich and Optimize the Data

The Deliverables

  • Findings report detailing the initial gaps and data collected during the PAV
  • CMMS upload templates containing the updated and enriched asset register (including verified tags, functional location hierarchy, criticality and name plate data)
  • Equipment criticality analysis outputs and justifications
  • Optimized maintenance strategies
The Deliverables

The Benefits

  • Assurance that all equipment on the plant is accounted for in the asset register, enabling equipment to be managed and maintained, helping to ensure productivity and safety integrity of the plant

  • Accurate criticality rankings will enable maintenance and spares to be assigned, prioritized, and stocked effectively

  • A structured hierarchy enables the maintenance team to navigate through the data easily, and inter-dependencies of equipment to be clearly defined, which is a crucial success factor for efficient maintenance planning and scheduling

  • Up to date and accurate name plate data is now readily available, assuring that the maintenance team have the correct information about the equipment, in order for BoMs to be assigned/developed

  • Able to avoid wasting money buying and storing the wrong parts, through accurate name plate data which details the correct equipment specifications

  • Increased reliability through optimized preventative maintenance, enabling the asset team to intervene at the right time, to avoid known failure modes

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