Anyone that I’ve talked to knows that I’m a big believer that analytics, even the most automated edge uses, require the integration of industrial subject matter experts (SMEs), whether in design, training, or adjustment phases. On these pages, I’ve written about SMEs, including the roles of data scientists and citizen data scientists. I’ve also discussed how analytics can reshape what is considered expertise. I’ve talked about analytics and digitizing industrial subject matter expertise here and here and here…The horse has been beaten but it will never be dead—industrial subject matter experts are fundamental to maximizing the value of analytics.
But how are SMEs best incorporated into the analytics process? For example, as companies look to scale analytics, can their input be, for lack of a better word, “productized” from project to project?
Industrial Subject Matter Experts Are Expected to Improve the Business While Running It
SMEs understand (and often design) operational processes and best practices. These high-value workers have specific knowledge of how to operate equipment, execute maintenance, and ensure safety. For example, crude oil engineers have expertise around impact of crude types on equipment failure during the refining process. This intellectual property is invaluable, of course, and organizations are fearful it will leave the business as workers retire or move on.
Often, the industrial subject matter experts are simply overwhelmed. They have to keep things running, fight daily fires, and, in their very limited spare time, trying to figure out how to improve outcomes. And so, the urgency of the present prevents them from taking time out to implement step change, so improvements are limited in scope and, at times, duration.
Recently, I had a conversation with an operations engineer about how he began to use analytics to move from reactive to proactive operations in a chemical plant. I had known about what he had done and achieved, based on prior talks. This discussion, though, focused on how he took the time to actually identify the problem, build the case, and carry it out.
Frankly, he stayed late all the time. He worked on it when he was supposed to be doing other things (he knew were less valuable), letting fires burn so he’d never have to put them out again. He challenged the status quo because he saw the futility of doing the wrong thing over again. His results were remarkable and lead to an executive team more motivated for and open to change. He was the agent of change.
His reward(?) is that he gets to do the same thing at the next plant, and likely the next. Unless someone invents a cloning machine, he’ll be depended upon to lead the charge. As much as he has helped the company, that’s not a sustainable path to organizational improvement as economies become increasingly digital and the speed of business rapidly accelerates. He’s also likely to burn out. More formal ways to implement digital, data-driven improvement are needed.
Industrial Subject Matter Experts Can Provide a Knowledge Framework for Analytics
Technologies are now available that can mathematically model and capture expertise as part of the analytics. At the same time, SMEs are looking to analytics to help them solve the previously unsolvable, make life easier so they can be more productive, and provide insight into how the business can operate more reliably, proactively, safely, and profitably. Opportunity lies within that intersection of industrial subject matter experts and analytics technologies.
Organizations must take a moment to pause some of the daily activities of industrial subject matter experts. By doing so, the SMEs can better explores the possibilities of analytics, leading to a sum clearly greater than the parts. An example of a beneficial outcome of this timeout is a knowledge framework, which can be used to feed analytics. The framework is a formalized, high-level capture for how a problem will be solved or an improvement realized. It determines variables to consider; relationships between inputs; structure for decision flows; and processes, metrics, and stakeholders impacted.
SMEs provide the necessary context for what any data input into the framework means and how, where, when, and why insights should be applied. The analytics then populate it with data, turning the framework into a usable decision model.
Once a knowledge framework is built, analytics can also ensure it is dynamically updated. As operations continue, continually informed by industrial subject matter experts and data, the knowledge structure evolves via the analytics, so an organization can continuously make the best possible decisions.
To be effective, digital step change can’t resemble building the plane while flying it. For analytics to be effective, industrial subject matter experts are required. Rather than looking at technology as both the means and the end, organizations need to let SMEs take some time out to leverage their considerable expertise for problem solving. Doing so will ensure that all flights are eventually smooth.