Medical Manufacturer Implements Production Monitoring System for 200 Machines and Increases OEE Nearly 30%
Headquartered in Pierceton, Indiana, Paragon develops medical device implants, instruments, and advanced surgical products. With 15 sites across four countries, the company chose MachineMetrics to manage its 1000+ pieces of equipment. Matt Townsend, Paragon's Director of Operational Excellence, discusses how MachineMetrics has changed day-to-day operations and enabled deep production visibility, resulting in significant increases in machine capacity, OEE, and bottom-line financials.
The many machine assets and processes that Paragon employs have historically made it a challenge to understand the true machining capability and capacity of the operation. This challenge was amplified by the multiple locations the enterprise operates across as well as the manual data tracking strategies the team was using.
What were the goals in implementing a production monitoring platform?
Our primary goal was to more accurately measure and increase machine utilization across our facilities. Because our operations use so many pieces of equipment from different OEMs and generations, we knew that we needed an accurate measure of machine utilization and OEE across all equipment to effectively meet increasing customer demand.
Paragon Medical implemented MachineMetrics' production control platform and realized a 140% increase in machine utilization.
We also wanted to understand how machines are utilized across all sites. Machine data would enable us to identify and make decisions on how to best use any existing capacity.
What challenge did you experience?
The biggest challenge we experienced was the number of OEM equipment types across the enterprise. We have grinders, mills, lathes, and other standard machining equipment found in most discrete manufacturers. But we also have legacy assets and specialty equipment like brake press turrets, water jets, extruders, and thermoforming machines.
The bottom line is that we aren't just a typical CNC shop. And manually tracking metrics for such a diverse range of machinery was complex, time-consuming, and resulted in unactionable data. The different types of equipment spread over four sites made it difficult to have a standardized view of production performance. We were looking for a solution that was machine control agnostic to handle all types of equipment, as well as scalable across all our locations.
Why MachineMetrics?
I had prior experience with MachineMetrics and was impressed with the service, communication, and support. This experience at a prior company, combined with the ease of implementation, was the primary reason for choosing MachineMetrics.
VIDEO: Paragon Medical Doubles Capacity with No Additional Machines
The business climate at the time also played a part in our decision. Elective surgeries were halted during the pandemic, and only necessary or emergency procedures were allowed. As pandemic restrictions eased worldwide, pent-up demand resulted in the resurgence of the orthopedic and advanced surgical space, significantly increasing the demand for our products.
We needed a way to understand how our facilities were effectively utilizing machines to meet the growing customer demand. As mentioned before, we also deploy equipment from many different OEMs.
MachineMetrics was the only provider that offered a machine agnostic platform, superior customer service and support, and ease of implementation across our facilities.
Before installing the new platform, we utilized manual data tracking to manage processes and understand capacity and OEE. This resulted in delayed and inaccurate data that lacked actionability. Staff couldn't act quickly enough to events simply because they were not aware of the events occurring. Facility managers also relied on operators to track how long a machine had been down. This took them away from value-added tasks at the machine.
How was implementation?
We began with a six-month pilot across three different facilities.
Matt Townsend, Paragon's Director of Operational Excellence, says he now has the ability to tie the data from the operators and machines to actual financial results.
Once we installed the edge device, which we did ourselves, machine connectivity took about an hour per machine. It helped that we had machines already networked to the controls in all our facilities with a dedicated line. Because of this, we were up and running with ten machines at the Pierceton facility within a day.
The pilot we ran resulted in plenty of great data that helped justify the program and help us understand the ROI. The improvements made in a short period allowed us to make decisions very quickly and prove out the system in significantly less time than the original six months we had planned for.
Initial impact?
The initial reaction from the workforce was positive. It was a very welcome change for operators to not have to manually collect data or write down the timeframe for a downtime event since it was automatically collected on the platform. Also, tablets placed at machines enabled operators to get ahold of supervisors when material or programming support was needed, all without leaving the machine.
Managers and operators could visualize performance and see when machines were down to better understand how effectively they were working against goals. Having a solution that could calculate downtime and OEE automatically was a game changer.
The standardization of data across our machines and processes has also significantly improved communication across the shop floor. It gets everyone in the operation speaking the same language, from area to area, to site to site, and even at an enterprise level. We all know how our machines are running.
At Paragon Medical, they are creating an ecosystem of data to support key functions of the business and MachineMetrics is an essential part of that.
MachineMetrics has allowed us to track improvements, understand the issues encountered, and respond promptly to problems with a significant reduction in response time. This deep production visibility also plays a part in our monthly and quarterly business review calls, ensuring we can more accurately tie shop floor performance to overall business performance.
How does Paragon use machine data at the enterprise level?
When we first implemented MachineMetrics, we started to look at bottlenecks at only the area and site level. But now we're trying to understand how performance looks from an enterprise level.
We're looking at the data to understand how machines are being utilized across the entire organization. For example, if a lathe is not being utilized in Massachusetts, can it be moved to Indiana, or can the work be moved to Indiana? We can justify the decision with utilization of data. Another example is our capital spending. Before MachineMetrics, capital expenditures were based on feedback from GMs and engineers. The flaw here is that the recommendations were based on manual data.
Paragon can now easily connect to manufacturing equipment, capture real-time data, and automate insights to frontline workers and factory systems to quickly identify and address areas of operational improvement.
Since the data was less-than-accurate, machines might not have been fully utilized once they were in place. Machine utilization data from our equipment now informs decision-making for CAPEX. And this plays directly into production planning. We are able to provide machine utilization data to sales inventory and operations teams so that they can better plan production to meet demand at the enterprise level.
Bottom-line impact?
With the ROI from the Pierceton pilot alone, we could pay for the entire program for the rest of the company within three months. Here are a few of the bottom-line benefits we experienced after implementing MachineMetrics.
- In one facility, production doubled in bottleneck areas
- Utilization went from mid-20% to 60%
- OEE went from 57% to 85%
- The number of good parts went from around 4,200 to over 10,000.
All of these results occurred within five months without purchasing any additional equipment. Since this is my second time implementing a solution like MachineMetrics, a lot of people ask, "How do you use the data to increase your OEE? And what does that mean to the bottom line?"
The initial reaction from the workforce was positive. Operators were glad to no longer have to manually collect data or write down the timeframe for a downtime event.
That's the big question that executives have. And the answer lies in the ability to tie the data from the operators and machines to actual financial results, which we are now able to do.
With MachineMetrics, we have accurate data on the amount of downtime we experience, the reason for downtime, and overall machine utilization. This provides us a punch list of things to focus on improving, which increases OEE.
With these productivity gains, we're able to produce more parts, and after accounting for overhead, we are able to see huge gains in EBITDA. Currently, we have over 200 machines connected to the platform and our goal is to get over 800 connected across Paragon's facilities.
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