Tyler Grahovec is vice president of operations at Pacesetter Steel, a Kennesaw, GA-based processor of galvanized, cold-rolled, aluminized, and stainless steel for industrial end users. He joined the company in 1998 as a foreman in Pacesetter’s suburban Chicago service center. At the Smart Industry 2017 conference in Chicago in September, Grahovec shared what Pacesetter has learned in implementing IIoT-enabled tools, including real-time production dashboards, to help improve scheduling and reduce downtime.
When we got into the IoT side, we came back and went to the shop floor and said: “OK, here are all the metrics you guys have used over the past couple of years to guide yourselves. If you had a clean sheet of paper, what would you want to know on an hour-by-hour basis or minute-by-minute basis when you’re sitting at your line?”
We came up with what their dashboard should look like from their point of view. Then we went to the foreman and so on up the ladder. We kind of reverse-engineered it. Instead of going top-down, we went bottom-up, really focused on, what does every layer need to see and want to see?
So, for example, schedule attainment is a key metric that drives our ability to ship to customers with a high-90% on-time delivery rate. The operators need to know if they’re on time for the order they’re currently running and what the next order is and if (raw material) and skids are ready for it. The plant manager, when he walked by, he wanted to see, what was the last job? Was the last job on time? And then the foreman had a different look, but they also wanted to see at the line level so that as they were walking by each line, they could see certain team metrics. Really the focus was building from the bottom up and figuring out for the guys what was relevant to them and then saying, “Here’s all the data we can collect,” and then talking them through what did they really concern themselves with.
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PS: How did you solicit that feedback? Did you sit each team down in a room at once and talk everything through?
TG: No, I went out to each line, and we took a few minutes at the line and actually sat there and talked to them right at their workstation.
PS: You indicated that since getting its IoT infrastructure up and running earlier this year, connecting facilities’ smart sensor data with PLC data and presenting real-time metrics at lineside digital dashboards, Pacesetter has unprecedented visibility into its operations. You mentioned being able to drill down into digitized dashboards via a touchscreen to identify in real time, for example, what has shipped and what’s ready to ship, where in the plant your skids are, what’s causing downtime, and where you need to update production schedules. So what’s next?
TG: What’s next for us is getting rid of as much paper as possible. We’re really taking every piece of paper that’s out on the shop floor and seeing, what are we using it for? What list is this, and how can we automate it? As we have drop-in orders, a piece of paper is static, and having something where we can update the guys in real time, helps them act quicker and react to things without waiting for somebody to get out on the shop floor and reassign all the paperwork.
It’s paperwork and then really tracking the downtime at the line and being able to drill down even deeper but not making the system cumbersome for the operator. Because you don’t want to make a downtime tracking tool that becomes another downtime.
PS: You said that when Pacesetter was in the process of selecting an IoT partner, you considered 15 different consulting groups and software providers before selecting Industrial Intelligence. Throughout that time and considering the wide range of solutions you were pitched, how did you stay homed in on the priorities you had identified?
TG: Our owner Steve Leebow saw the interaction between the operations and IT team at Pacesetter and some of the tools we had developed on our own; he asked us what our IoT needs where and sent his assistant to trade shows (to scout vendors), so when they came in to present, we had a plan of what we wanted to attack. One of the biggest challenges you have is everybody has a shiny new toy if you will, so they're going to try to fit their shiny toy into your process to create your solution, and they don't really listen to your needs.
The other issue we saw was people will come in promising they can get you all this data, but it's how you use the data, that's the killer part. It was an interesting challenge which we solved by creating our own team, which is now Industrial Intelligence. Industrial Intelligence has helped us tremendously, as internally we have good software designers and developers, but we have very limited resources. It’s (a matter of) finding the right partner and then getting the two teams communicating and working effectively together.