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The growing imperative for EAM in a high-stakes industry
So , how can asset managers in the oil and gas industry work smarter and more collaboratively to increase productivity , optimize plant conditions , and minimize downtime while meeting their sustainability and safety goals ? The answer comes through implementing an enterprise asset management ( EAM ) solution , which is a collection of software , systems , and services that can effectively manage the maintenance of physical assets throughout the lifecycle . Most EAM software
provides an intuitive ability to manage assets , work activities , and costs , simplifying health , safety , and environment ( HSE ) compliance , and futureproofing processes . This granular information is supplied in a clear and concise manner , with data available on both desktop and mobile devices for ease of use .
Indeed , EAM adoption has been shown to bring significant benefits to oil and gas companies by digitizing assets to boost transparency and collaboration , improve documentation , and maximize access to data and knowledge . Consequently , EAM has emerged as a vital tool , yet its adoption does not come without challenge .
Understanding the EAM maturity model : from reactive to ultimate strategies
Let us look at EAM in greater detail , starting with adoption rates . The latest IFS EAM Trend Report , which is based on the responses of several hundred asset managers , many of whom work in oil and gas , shows that 63 percent of respondents are currently using an EAM solution , with this figure creeping up year-on-year . The Trend Report also indicates that the application of EAM differs dramatically from one company to another . This is reflected in the five-stage maturity model ( see graphic ), with stages 1 and 2 based on reactive and corrective maintenance , while stage 3 indicates ‘ proactive ’ approaches such as condition-based maintenance , while focusing on increasing asset reliability and HSE compliance . Stage 4 , meanwhile , is characterized by ‘ smart ’ EAM adoption , enabling a predictive maintenance approach . Examples include risk-based maintenance
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