ARC Advisory Group reported that the global process industry loses $20 billion annually from unplanned downtime. While companies have spent millions trying to address the

unplanned downtime issue, until now, they have only been able to address wear- and age-based failures because they lacked insight into the process-induced failures that cause some 80% of unplanned downtime.

In order to address this issue, organizations require a new approach to manage asset performance and move from reactive to proactive operations. According to the head of a European chemicals business, “despite all the optimization in our chemical assets, when something breaks down, all the optimization work and investment is compromised because production is impacted/stopped, quality is impacted, commitments are missed. We need to improve the reliability of our assets.”

Asset optimization: the next frontier

Asset optimization is the next frontier, providing a comprehensive, holistic approach to optimizing production assets across the entire lifecycle, spanning the initial capital investment to ongoing operations to subsequent capital investments to eventual retirement. In combination with asset data, first principles and empirical models, and process knowledge, asset optimization goes beyond process optimization to provide advanced analytics to create a world that doesn’t break down. As a result, organizations have the ability to provide process insights, predict failures, and obtain prescriptive actions to mitigate or prevent problems, building sustainable competitive advantage throughout the entire lifecycle of the asset. 

For example, with advanced machine-learning software, companies have already demonstrated incredible successes in the early identification of equipment failure. Such software is near autonomous and learns behavioral patterns from the streams of digital data produced by sensors on and around machines and processes. Automatically, and requiring minimal resources, this advanced technology constantly learns and adapts to new signal patterns when operating conditions change. Failure signatures learned on one machine “inoculate” that machine so that the same condition will not recur. Additionally, the learned signatures are transferred to similar machines to prevent them being affected by the same degrading conditions.

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A North American energy company, for instance, was losing up to a million dollars in repairs and lost revenue due to repeat breakdowns of electric submersible pumps. The advanced machine-learning application learned the behavior of eighteen pumps. The software detected an early casing leak on one pump that caused an environmental incident. Applying the failure signature to the rest of the pumps provided an early warning, allowing early action to avoid a repeat incident; thus solving a major problem.

Creating a system of success with asset optimization

Asset optimization empowers better and more informed decisions. It is made more powerful with the Industrial Internet of Things (IIoT), which accelerates the optimization of business assets. Supported by the capabilities of cloud computing, visualization and mobility, key stakeholders gain better insights into the use of data to address real-time operational needs. The transformative potential of applying asset optimization to the world’s most complex, capital-intensive industries will be experienced first by design, operations and maintenance departments, and then throughout the enterprise in new and unexpected ways.

Greg Mason is vice president of sales with AspenTech Asset Performance Management.