The strategy of optimization

April 10, 2018
The biggest mistake companies make is just digitizing everything.

“Turning digital transformation into business outcomes requires a relentless focus on operational excellence,”

AspenTech's John Hague

says John Hague, senior vice president and general manager with AspenTech’s APM Business Unit. “A business is at its most competitive—and profitable—when all operational assets are running as close as possible to their performance limits.”

That sounds great. But how do we get there? We chatted with John to learn more...

Smart Industry: How can companies work to get more value out of their digital transformation initiatives?

John: Achieving this requires a twofold approach: a holistic optimization strategy that covers the entire asset lifecycle; not just operations and a pragmatic execution roadmap, driven by realizable value, built on proven best practices and aligning IT strategy with business strategy. It’s only made possible by the convergence of real-time data, advanced analytics and rich-process knowledge.

Technology has evolved to the point where companies can predict where and how an asset breakdown will occur with months—not just days—of notice. Just as important is the experienced hands who understand the inner workings of complex industrial processes and environments. The software that can produce quick wins for operational excellence and put insights into the hands of process operation and maintenance teams hasn’t historically been as readily available. The IIoT platform layer is well established, but the software layer on top is still relatively thin.

Smart Industry: What do you mean by “thin”?

John: Let’s look at reliability. In recent years, an advanced strategy meant gathering multiple data scientists to build condition-based models to simulate possible scenarios. That’s a lot of man power and reliance on data expertise. A stat in our recent survey of process companies found that 49% say a huge roadblock for them to adopting data analytics is a lack of in-house expertise.

Right now, there’s very few out-of-the-box and ready-to-use software applications that eliminate reliance on data scientists. That’s why so many companies are still in early stages of getting ROI out of digital transformation. But there’s a real need for this kind of low-touch machine learning, or “ready-to-use” applications. Where other technologies require the people using them to interpret the data and the best plan of action—low-touch does not and it’s driving reliability forward. This is where the real value will be realized.

Data and analytics have been used by asset-intensive industries for many, many years. Digitalization itself is not new, but macroeconomic and overall pressures of a highly competitive global market have catapulted interest among industry executives in scaling it. The result is high expectations (and hype), though its underlying drivers and opportunities are real. Our survey found 40% of companies believe digitalization can save 16% or more in operating expenses (OPEX). But it also found companies are facing real challenges: 35% don’t believe they can benefit from big data in under two years.

New levels of computing power, data-lake architecture and analytical techniques have already had dramatic impact on cost savings and efficiency, but this high-speed access to more and more data is still not giving decision-makers the time nor the insights they need to become best-in-class producers and manufacturers. That’s what will unlock the value that organizations such as the World Economic Forum estimate to be as high as $1 trillion within the oil-and-gas industry alone.

Success isn’t achieved just with sensors and data collection, but by focusing efforts on technology, processes and people—using insights to improve operational excellence.

Smart Industry: For an enterprise looking for software to bring value to their digital transformation, what is the best way to yield results? 

John: The biggest mistake companies make in their digital-transformation journey is just digitizing everything, collecting data and hoping they’ll find a use case and problem to pinpoint. We need to flip that pyramid. Determine where your biggest pain points and problem areas are within a system or operation and put resources there. Part of operational excellence is continuous improvement, and once you improve in one area, there is always opportunity to tackle another. Mounds of data are not going to win in digitization at the end of the day. It’s the applications and analytics around existing pain points—like reliability—that empower people to take action. You can have all the low-touch technology in the world, but unless it’s applied to the right problems it won’t drive the value you’re after.

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