The Impact of Poor Quality Data on CMMS and EAM Asset Reliability

Organizations require a clear understanding of the status of their operation to successfully introduce proactive change by enhancing the reliability of the equipment and the underlying maintenance strategies that support them. This understanding comes from reviewing historical data to identify patterns. If the data is incomplete or inaccurate, any resulting maintenance strategies are flawed.

Asset Availability

CMMS & EAM asset reliability is directly related to its ability to achieve peak performance with minimum downtime. An asset that is poorly maintained cannot achieve its performance potential and typically experiences an increased failure rate, resulting in more downtime.

Incomplete and inaccurate EAM and CMMS data results in:

  • Increased failure rate due to poor maintenance practices
  • Unscheduled repairs, which require more time than scheduled maintenance activities
  • Increased downtime due to technicians searching for maintenance information or the correct spare parts
  • Increased downtime waiting for unstocked critical spare parts to be delivered

Production Throughput

Production throughput requires all components to be functioning at their peak, with no unexpected outages. Assets that are poorly maintained increase the risk of stoppages and unplanned outages.

Incomplete and inaccurate EAM and CMMS data results in:

  • Reduced throughput or poorer quality output, or both
  • Increased overtime as a result of time lost due to unexpected outages

Enabling Reliability Centered Maintenance (RCM)

Reliability Centered Maintenance (RCM) strategies recognize that all components fail, and that it is possible to intervene to try to prevent all failures. RCM strategies evaluate failures based on impact: to operations, health and safety, and costs. Because it is not practical to try to prevent all failures, RCM strategies evaluate, categorize, and prioritize failures, based on their impact, and identify the appropriate interventions.

RCM is used to determine optimal maintenance requirements and reduce the frequency and severity of asset failures. These methodologies rely heavily on data. Prediction of failure requires detailed historical data. Prevention of failure requires detailed, strategic maintenance guidelines. Criticality scores are based on frequency of occurrence, which requires historical data, and severity.

An RCM strategy is only as good as the data on which it is based. If the data is incorrect, the result, as they say, is “garbage in, garbage out”. This also holds true when implementing expensive Asset Performance Management (APM) software. Without a strong data foundation, many businesses fail to realize the potential value of the technology and struggle to correct the underlying issue.

Incomplete and inaccurate EAM and CMMS data results in an inability to create RCM strategies due to:

  • Missing or outdated vendor/supplier recommendations
  • Missing characteristic data on which to determine criticality
  • Missing historical data on which to base failure forecasts
  • An inability to predict the types of failures that can occur
  • An inability to assess the effects of a failure, to the affected system, or to production
  • A poor or ineffective implementation of Asset Performance Management (APM) software