What does asset performance optimization mean for energy and manufacturing?

At both OpenText™ Enterprise World Europe and Enterprise World Asia, I spoke with a number of customers about using digital technologies, especially predictive analytics, to transform their maintenance activities and optimize asset performance.

These conversations boiled down the three core issues around asset performance: deploying digital technologies, the ‘first to be second’ approach and the potential of predictive maintenance.

1. The importance of deploying the latest digital technologies

Focusing on asset performance is fundamental for business in every part of the energy and manufacturing sector. To get there, companies must identify where disruptive digital technologies can deliver real transformation and start bringing those technologies into their operations.

One customer I spoke with is a world leader in developing, manufacturing and servicing systems for the energy industry. They emphasized the importance of deploying the latest digital technologies to continually monitor and improve the performance of their products and services. The company has developed an IoT based system to deliver advanced diagnosis and maintenance services to its customers’ operators. The team started working with OpenText to integrate predictive analytics into the system to identify and address potential issues before they become a problem and provide end users with the information they need to make improved operational decisions.

The customer shared: “Our customers want the ability to analyze their data to understand why, when and even where something happens … we can provide state-of-the-art visualizations and heat maps of condition-based events, such as over-heating pumps in a specific asset. This helps our customers put measures in place to reduce component failures, extending component life, and ultimately saving money.”

2. “First to be second” approach

Although companies know they need to transform digitally to remain competitive, progress is probably slower than it should be. It’s easy to look at headline figures that show 80% of energy companies are digitally transforming and see things moving in the right direction; however, when you drill down a little you find that only 13% say they have an established digital transformation program.

Recently, a senior executive of a global energy company described their digital transformation strategy as ‘we want to be the first to come second’. The company has an appetite for new digital technologies and they monitor things like AI and predictive analytics closely, but they don’t want to be in the innovator or early adopter camp. Once they see a few good use cases where the technologies are working in their sector then they’ll begin their transformation.

It seems a sensible approach – except that when asked, almost three-quarters of Utility companies in the UK identified themselves as ‘fast followers’ when it comes to digital transformation. It begs the question: What are the 13% of leaders doing while the rest of the industry waits to follow? According to a customer I spoke to in Singapore, they’re differentiating themselves from the competition: “The powerful and flexible analytics we are providing to our customers is new and a real differentiator for us in the market.”

3. The journey to predictive maintenance and beyond

Over the past few years, the industry has been excited about the potential of predictive maintenance: It can maximize the uptime, performance and safety of any piece of equipment or production asset. Where companies have successfully implemented predictive maintenance, the benefits have been impressive. But research shows that companies were less enthusiastic about predictive maintenance in 2018 than they were in 2016. According to the research, organizations continue to move towards predictive maintenance but are finding it harder than expected to bring together data from different sources and extract the expected insights from that data.

The conversations I had with customers mirrors this: they understand the need to build predictive analytics into their existing approach to maintenance. They know they can significantly increase asset performance through sophisticated and easy-to-use analytics capabilities, which also creates the foundation to move to predictive maintenance – when the business conditions are right.

Takeaways

My key takeaway here is that organizations in the energy and manufacturing sectors should see asset performance optimization as an evolutionary process. Predictive maintenance may play a major role in your strategy – but for many energy companies, this is is probably still some way off. Getting there is a journey that begins with assessing where your digital transformation programs are today. Take a look at the digital technologies that are available to you and determine how they can be used to first improve and then transform your asset management operations. Finally, it may sound good to be the first to come second but, in today’s digital world, if the leader has already lapped you a few times then your race could already be over.

Enterprise asset management and operational excellence are among the topics at Enterprise World 2019 in Toronto. Reserve your place today.

To learn more about OpenText’s AI-powered analytics for asset performance optimization and predictive maintenance, click here. 

Martin Richards

Martin Richards is a Senior Director for Energy Industry Solutions at OpenText. For over twenty years, he has worked with ECM technology, delivering professional services and solutions for the energy and engineering industry.

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