Maintenance is an inevitable part of operating and managing a plant, regardless of industry. The costs incurred from traditional time-based and reactive failure-based approaches to maintenance, however, can be avoided. Of the $260 billion each year that U.S. industry spends on maintenance, $85 billion is spent on lost or unnecessary maintenance, representing a nearly 33% loss.
While it’s impossible to predict the future, asset performance management (APM) technology enables organizations to do much better at “predicting” when assets will need to be serviced. By capturing and integrating data from connected sensors and enterprise asset management systems and then applying sophisticated machine-learning algorithms across those data sets, organizations can develop a true picture of asset and plant health and more accurately assess the criticality of each asset to prioritize maintenance activities based on risk.
Assessing asset criticality
An analytics-driven asset management strategy typically focuses on three key elements: asset criticality (how critical it is to the system), risk of failure (how likely a failure is to occur), and cost (of routine and reactive maintenance). An asset’s criticality is based on the potential cost or consequence of a failure. With thousands of assets to oversee, plant operators can’t realistically monitor each one closely, but APM-enabled analysis offers a method for understanding and codifying the risk associated with each individual asset.
Asset criticality analysis offers a way to rank the risk associated with each asset, allowing organizations to more accurately prioritize efforts and resources to more-critical, higher-risk assets. By understanding the level and type of potential risk across safety, environmental, and production dimensions, an organization can confidently tailor its preventive maintenance and mitigation plans to ensure the proper emphasis on the most-critical assets. Not all assets pose the same risk to a business, and utilizing criticality analysis techniques allows reliability and maintenance teams to move away from a “one-size-fits-all” model, which can dilute the focus around risk and waste money on unneeded maintenance.
At the outset, engineers must complete criticality assessments for all assets, where the lowest level reflects an asset that is likely less severe in terms of safety and production consequences, while a high-criticality asset represents an asset that would result in high costs for an organization in terms of maintenance as well as lost production if it were to fail.
As reliability engineers know, the criticality level of an asset typically is determined by carrying out a qualitative criticality analysis, which takes into consideration both the likelihood and the consequence of an asset failure. In addition to this qualitative analysis, a company could consider quantitative risk assessment (QRA) technique, hazards and operability analysis (HAZOP), or what-if analysis to identify worst-case scenarios and associated potential consequences of an asset failure or downtime. These are key data points that are then aggregated with reliability-centered maintenance (RCM) studies as well as root-cause analysis (RCA) reports from prior incidents.
These assessments examine the risk of an asset both at the individual and system level. For instance, a piping segment in a process manufacturing facility handling water could be considered low in safety consequence in the event of a loss-of-containment event. However, if that segment is supplying water to a core secondary process critical to production, it could be considered high in production consequence. How you mitigate risks for the same piece of equipment in one scenario versus the other may be completely different based upon the knowledge that was captured in the criticality analysis.
A strong APM program, derived from true equipment manufacturing and operating knowledge with advanced software algorithms, allows plant operators to incorporate and leverage criticality assessments at scale across an entire plant or fleet within an organization. As assets and equipment age over time, data is ingested and analyzed to constantly update the risk level of each asset through machine learning algorithms.
Maximizing resources to reduce maintenance costs