Undergoing a digital transformation is no longer an objective for certain types or sizes of industrial companies. It’s simply where industrial automation technologies are taking industry as a whole. As your facility updates devices and equipment, you will be digitally transforming your company.
The range of production and business optimization possibilities enabled by these new automation technologies implies that some strategic level of planning needs to be applied to derive as much benefit as possible. To better understand how to step back and assess your digital transformation, whether you’re already well down that path or just getting started, we connected with Bill Pollock, president and CEO of Optimation, a company that provides mechanical and process engineering services, as well as design automation and systems integration for a recent episode of the “Automation World Gets Your Questions Answered” podcast series.
We began our discussion by attempting to define what the term “digital transformation” really means, since it can cover so much ground. Pollock explained that the term, of course, means different things to different people, but it tends to focus around apps on smartphones and how that’s changing the way we interact with both people and things.
When it comes to manufacturing, the term digital transformation “takes on a different strategically leveraged place that includes things that are digital, but not things that necessarily live in our daily lives,” Pollock said. “Predominantly, it’s about finding ways to improve the overall manufacturing process. And a certain amount of that happens at the supply chain level, like with product specifications around just-in-time manufacturing, or quality or analytics.”
Vertical differences
Discussing how the digital transformation process may differ across industry, Pollock said that, at the factory floor level, the sensors and data collection devices will be different. “But once you get past the factory floor and start looking at the big picture—at productivity and quality and start doing analytics—then you get to a high enough level that there really isn't a difference from one kind of process to another because the amount of data you're looking at. And the type of analysis you're doing is going to be pretty much universal, whether it's a rolling machine or chemical fluid flow machine or a transmission manufacturing machine.”
Beyond the level of similarities to the digital transformation across industries, the biggest impediment to digital transformation has been the antiquated nature of many manufacturing facilities. In those kinds of facilities, the sensors and interfaces on the factory floor that “would allow all that data to be collected and transformed digitally [often] don’t exist,” said Pollock. “But suppliers like Siemens and Rockwell and many others have put the pieces in place to interface [new technologies] to existing infrastructure so that some of the lag and drag of digital transformation is less disruptive.”
As much as the digital transformation process is about technologies and data, it’s just as much about vision. Pollock said that business and operations management “really need to have a vision of where they want to go and what they want to achieve [with the digital transformation]. Once you have the vision, you can start to explore the possibilities that exist and the tools that can move you towards the digital transformation you envision.”
Pollock added that companies will obviously have to look at the return on investment to get them to their digital transformation vision. Which is why it’s critical not to begin the process “willy nilly, thinking it would be really cool to have robots or big screen TVs [to display production data], or whatever. First you need to know what it is you want to achieve to know what kinds of technologies you likely need to get you there.”
Mistake avoidance
Having worked with a number of manufacturers on their digital transformation process, Pollock said the first mistake many companies make is not defining their plan well enough before putting it into action. Part of this is not allowing “a long enough timeframe to achieve a return on investment,” he said. “There needs to be some flexibility. And that includes admitting, if you go down a path and things aren't going as well as they should, that it might be time to sit down and reassess it.”
Above all, having a good set of analytic tools for measuring is important, according to Pollock. “If you use your analytics tool correctly to look at all the variables, you can see which ones are moving along sort of steady state and which ones are bouncing all over the place. And that will give you insights into all the variability in your processes,” he said. “And sometimes it requires bringing in a human expert who can help you find the variables. The people working in the plant day to day are so used to doing what they do that they often don’t see the alternatives because they haven't been to other plants to look around and see the differences.”
