worker competency gapRobert Golightly is product marketing lead for the AspenTech Asset Performance Management suite. Previously, he managed the product marketing function for the company’s Advanced Process Control and Manufacturing Execution Systems product lines. Golightly’s professional background includes work for SaaS provider FineTooth and Pavilion Technologies.

 

 

Q: Manufacturers, plants and refineries are battling a widening competency gap as longtime experts retire and leave behind young professionals. That is, if workers are filling the roles at all. It is projected that by 2025, 2 million manufacturing jobs will be unfulfilled. What are manufacturers doing to address this problem?

A: As longtime experts are retiring, companies cannot afford to wait for new professionals to acquire decades worth of experience. These companies are under pressure to operate at the highest level of productivity and safety, and technology is being used to quicken that learning cycle. “Low-touch” technologies, like machine learning and prescriptive analytics, are having the biggest impact. If implemented so people on the front lines with minimal experience can use them, these technologies can address the competency gap head on.

Q: What are some examples of “low-touch” technologies and the types of real-world training they can help with?

A: One example is in training. Rather than waiting for operators to learn about normal and abnormal plant conditions by experiencing them over decades, industrial companies are using Operator Training Simulator (OTS) software to simulate dynamic real-world conditions and train personnel quickly. For example, refiner CESPA reduced startup time and reduced off-spec production with OTS, while specialty chemical company Perstorp saved valuable production days by reducing startup time after plant revamps. Total industry savings from OTS are estimated at $15 million per project, though safety advantages are priceless.

Technology is also being used to turn operations and maintenance into a science, like in production operations, where machine learning algorithms are mining data to identify underlying equipment issues (such as operational practices that degrade assets) and how to address them before they cause breakdowns.

Q: With the focus on Industry 4.0, what new skills are needed and what are the top barriers to success with digital transformation?

A: Industry is still very reliant on data scientists and analytical expertise. In our recent survey, 49 percent of engineering, procurement and construction (EPC) firms and 45 percent of upstream/midstream companies cited lack of in-house expertise as the top barrier to realizing the benefits of analytics.

Industrial companies are not data analytics companies, but they need to become proficient in using data and low-touch machine learning is changing this, giving operations, maintenance and other teams on the plant floor data insights from autonomous software systems rather than relying on data scientists to model scenarios and conditions out. The technology is not only doing data analysis beyond the capacity of any human to compute and analyze, but also gives guidance on how to address problems in real-time.

Q: There’s an overarching focus on automating knowledge work. What does that actually look like?

A: It’s not just about training and closing the skills gap. It’s also about reimagining the way things have been done forever, making the way plants run more scientific, data-driven and efficient, and supplementing the knowledge people have.Automating knowledge work is about leveraging prepackaged analytics applications that embed deep process and operational knowledge for collaborative decision-making that cuts across silos to make a much bigger impact.

Take maintenance, for example. Many still do scheduled maintenance, but we know that’s not effective at preventing failures. ARC Advisory Group says traditional maintenance practices cost the global process industry $20 billion per year and 80 percent of downtime can be traced back to operational events. Machines can process more data faster than any human is capable of, meaning they can look at many inputs in real-time to not only see when asset degradation is happening, but also predict failures far out. It’s not a replacement for human knowledge, though. Combining what real-time data tells us with the expertise and operational knowledge of professionals is where huge gains will be made.

Q: How can companies ultimately be successful in bringing these sorts of technologies in to address the skills gap?

A: Technology only makes a mark when capability and culture come together. 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.

In our same survey, 40 percent of companies believe digitalization can save 16 percent or more in operating expenses, but also 35 percent don’t believe they can benefit from big data in under two years.Anyone can sensorize and collect data, but to see change companies need to make it available and digestible.

The industry has gone as far as it can with traditional technology (modeling that relies on data scientists to model out different plant scenarios). The companies that adopt advanced technologies, align their IT investments and strategy with business needs and use them to enable culture changes are the ones who will ultimately realize the promise of digital transformation.