The Impact of Poor CMMS and EAM Data Quality on Maintenance
Asset-intensive organizations rely on the data in their EAM or CMMS systems to drive the maintenance practices that keep their business running and profitable. When that data is in disrepair, the effects are often most apparent within the maintenance organization.
Effective and Efficient Work Management
Effective and efficient work management requires an in-depth understanding of the processes and skills required, the work to be performed, and the constraints affecting the work. Organizations cannot accurately define any of these without accurate records of their assets, and CMMS or EAM data quality, including well-defined work tasks and maintenance plans.
The average wrench time (average hours per shift that a technician spends directly performing maintenance activities) in many industries is less than 40%. This figure applies even in companies with the world’s leading Maintenance Management software. The problem rarely lies in the system: it is usually with the quality of the data. If planners and technicians cannot trust the quality of their CMMS and MRO Supply Chain data, they may spend as much as 30% to 40% of their day locating and verifying the information they need to do their jobs.
Poor data quality negatively affects work management through:
- Decreased wrench time
- Incomplete or incorrect work tasks and maintenance plans
- Poor allocation of human capital
- Missing skills or inefficient use of specialized skills
- Sub-optimal maintenance spending when compared to total Replacement Asset Value (RAV)
The current generation of maintenance workers built careers on their detailed knowledge of maintenance information, such as plant configurations, repair techniques, and spare part requirements. As these experienced workers retire, companies must fill the knowledge gap they leave behind with thoroughly documented IT systems. Systems that provide an accurate, rich presentation of vital maintenance information become critical to companies and their next-generation workforce.
Corporations need to adopt effective strategies that leverage both technology and the existing knowledge of their maintenance personnel, subcontractors, and suppliers, to provide them with the right information, at the right time to optimize execution of their maintenance philosophies. Failure to do so results in incomplete and inaccurate data infiltrating every corner of the maintenance organization, across the enterprise.
Knowledge gap can result in:
- An inability to create comprehensive maintenance plans and strategies
- An inability to set maintenance best practices that can be adopted across the enterprise
- Increased risk of environmental, health, and safety issues
The most direct cost to an organization with poor-quality EAM or CMMS data is an increase in maintenance costs. Asset-intensive companies typically spend between 5% and 10% of their annual revenues on maintenance. Reactive maintenance is two to five times more expensive than a planned and predictive approach. Planned maintenance based on accurate and reliable EAM and CMMS data can help to avoid rapidly escalating costs.
The following are just a few of the problems that drive up maintenance costs:
- Critical spare parts that are not stocked and must be sourced urgently
- Incomplete preventive maintenance plans and task descriptions
- Obsolete parts catalogs
- Downtime while waiting for a technician with a particular skill set to become available
If planners and technicians cannot trust the quality of their CMMS and MRO supply chain data, they may spend as much as 30% – 40% of their day locating and verifying the information they need to do their jobs.
Data quality trust issues can lead to:
- Increased time spent due to missing parts and incomplete information
- Increased overtime and decreased productivity
- Under or over utilization of maintenance personnel
- Unnecessary parts replacement due to inadequate preventive maintenance