We help you build the foundation for your maintenance and reliability program.  Your technical hierarchy is that foundation.  It is the infrastructure that supports all master data and equipment failure reporting processes.  We can help you design and build a new technical hierarchy or can help you retrofit your existing technical hierarchy to make it industry standard compliant.

We help you extend the ISO 14224 taxonomy for equipment specific to your industry.

While the ISO 14224 methodology is relevant to any asset intensive industry, examples in Annexes A and B are specific to equipment used in offshore oil and gas industries. While there is some overlap of equipment types in offshore oil and gas and other industries, e.g. pumps, motors, and heat exchangers, many equipment classes from other industries are not included in the standard. They must be developed in order for you to use the ISO 14224 standard effectively.

We extend the ISO 14224 taxonomy by developing additional equipment class taxonomy definitions in a manner consistent with existing content in Annexes A and B.

We think it’s important for you to see end-to-end processes in action BEFORE you buy the solution.  Let us give you a complete demonstration of ISPM for your own facilities. 

Here is how it works:

  • You provide us with the following data for one of your equipment installations
    • Equipment list
    • P&IDs
  • We set a functional taxonomy structure for your equipment in Reliability Dynamics’ SAP system
  • We give you access to our system and let you test it yourself
  • Reliability Dynamics will create test data and give a guided tour

Fundamentals of ISPM equipment taxonomy and failure reporting methodology:

  • High-level objectives
  • Common issues
  • Equipment taxonomy
  • Malfunction data collection
  • Failure Metrics
  • Review your ERP system data and methods
    • Equipment taxonomy construction
    • Malfunction data collection processes
    • Preventive maintenance condition reporting processes
  • Review your equipment failure data
    • Assessment of data quality level
    • Highlight specific issues
  • Comparison of your current practices versus industry standard practices
  • Provide summary report with findings and recommendations