Case Study: MachineMetrics provides real-time production data

"It is the same as adding two and a half weeks to each month.”

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by Graham Immerman, director of marketing with MachineMetrics

The Case Study

MachineMetrics is an IIoT-analytics platform that monitors production in real-time, providing visualizations of real-time production data, instant notifications and predictive capabilities that help identify production bottlenecks, measure process improvements and drive manufacturing efficiency.

shot oneFastenal Manufacturing has eight facilities across multiple continents and is growing in their machining capabilities, transitioning from a simple job-shop environment with low production quantities to a large-scale global operation with thousands of jobs. Fastenal recognized the need to embrace data-driven strategy and digital technology to enhance their understanding of machine utilization, primary downtime reasons and quality issues to optimize manufacturing efficiency and implement process improvements. Fastenal needed real-time visibility into jobs’ progress to ensure efficiency, quality and on-time delivery. Managers wanted the ability to collect and understand downtime/setup procedures (as previous manual tracking efforts were time-consuming and prone to errors), and make proactive vs. reactive improvements. The full team hoped to aggregate, structure and analyze data from numerous machine types, shop-floor systems and manufacturing locations that had been siloed, in order to provide true production transparency and create actionable insights.

The Solution

Fastenal installed the MachineMetrics platform to collect production data from machine controls and machine operators for the entire production floor and used this data to monitor machine conditions and track the progress of jobs via OEE production efficiency. Real-time dashboards mounted on the production floor provided an at-a-glance indication if jobs were performing at or below expectations, enabling enhanced job-scheduling. Operator touchscreens were mounted at each machine, empowering operators to meet production goals and track machine setup/downtime. Analytics reporting enabled managers to not only track efficiency and quickly identify production bottlenecks related to specific machining operations, but also helped measure the effect of process improvements. MachineMetrics’ open APIs enabled data-aggregation across shop-wide systems, along with the ability to build custom applications, dashboards and analytics.

Here see an interview with Fastenal’s Tim Borkowski (VP of manufacturing), Joe Garteski (operations manager) and Matthew Nelson (manufacturing engineer).

What challenges were you trying to solve by implementing a machine-monitoring system?

Tim: I think the biggest thing we were looking for when first considering an implementation like this was to find something that was a good fit our company and our process. Previously, we had used a competitive software, and at the time they had the DNC side covered. The software was a bit clunky, and we needed something more user friendly. If you wanted information it was rather difficult to get from the reporting side of the software. The truth is technology has changed, and with the variation of controls on our more modern machines our monitoring needs changed as well. The fact is we are transitioning from a job shop. When we had smaller quantities, it was harder for us to track information. It felt like we were shoveling sand; throwing it over our shoulder repeatedly and never looking back. Now, our quantities are going up, and we have the ability to look at our process control and make impactful improvements. We need to know our spindle time and how it’s going 24/7.

Joe: One of the main issues we faced was determining options when there wasn’t enough management. We wanted to know what happens from first shift, to second shift to the weekend. What happened between those shifts? Was there any variation in productivity? If so, why? Why was that machine sitting idle? Was the machine down that day? Were there more setups that day?

Another challenge was the lack of real time information. Having to wait for an individual to come back from that shift to gather specifics on various issues was very frustrating. Sometimes we would have to wait 16 hours before an operator came back on shift to speak with them to understand why a machine was down or why a specific fault occurred. Was there an issue with tooling? These are the things that we want to determine but didn’t have a solution for; it all leads to how to keep the machines up more.

shot pre oneWhy did you choose a MachineMetrics?

Matthew: We started out using MachineMetrics to monitor 11 Swiss machines and three TTs. Since then, we have added more machines, and we’ll certainly be adding more. We installed the monitors on our shop floors so that everyone can know what’s going on, including operators and managers. We also put the tablets in right away so we could start gathering feedback from the operators.

Joe: As we were discussing different systems, what immediately caught our eye from MachineMetrics was the simplicity of use and the availability of real-time information. Getting the data out of our previous system wasn’t easy, and the dashboards weren’t especially legible. That was what started us on MachineMetrics. There was no clutter. The ease of reporting was a huge factor. Our previous system was just barcodes, whereas MachineMetrics visualizes the data in a way that makes it so easy to use, it’s just plug and play.

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