Fastenal's objective was to better understand their machine downtime, utilization, quality issues, and to embrace cutting-edge manufacturing technology/process improvement capabilities to bring their team to the next level. However, there was a lack of real-time data, visualization, and actionable insights made this transition impossible.

Gary Bruner, the president of Carolina Precision Manufacturing, a contract manufacturer that specializes in small-diameter, close-tolerance CNC Swiss turned parts, had a problem. Over breakfast that morning, Gary had logged onto the MachineMetrics monitoring system on his laptop at home to check the status of his machines assigned to a lights-out operation, but saw immediately that two of his machines were not in production. “What’s wrong with machines 35 and 36?” he thought to himself. Upon arrival to the shop, Gary learned from his operator on duty that this type of hold up was nothing out of the ordinary, and was in fact a product of inefficient startup procedures that had simply never been analyzed or augmented previously. In an industry with razor thin margins, Gary understood that the keys to growth and success were in efficiency and quality. He understood the importance of keeping tabs on production stats, job status, uptime, and setup. However, there was no way to know how well machines were doing in real time. What was causing this additional downtime? Furthermore, CPM’s current methods of measurement and data collection were not only time consuming, but had quickly becoming outdated. Historically, CPM had an employee dedicated to the collection of utilization data. This employee would walk around to each of the machines, collect scrap tickets post-production, talk to operators, and record yesterday’s data into their current ERP system; not to mention that this manual data collection was prone to errors, and would take upwards of 2 hours per day. Without the ability to visualize their results, the recorded data was not very actionable.

There’s always a sort of lag in the information we get from the ERP. It’s not real time, its not updated every second. We were able to see what happened yesterday, what happened last week but not what was happening right now, and we would always have to somehow fill that gap. Also, to get data into the ERP we were relying on a human being to accurately define and manually input the information to enter it into our database. All of the real-time monitoring systems that Carlson Products had seen were only dedicated to machining. They had chats with some of their equipment providers and the solutions they attempted to provide them with were not visually appealing, not web-based and felt quite rudimentary. They did attempt to do manual tracking before MachineMetrics, and they got so much junk data that they just stopped trying. They kept a Microsoft access database where employees would track this information themselves but with all the huge outliers, data it went to hell and a handbasket. The most important thing Carlson Products wanted was to look at was historical labor data information: whether or not the jobs were on target. Tracking down the supervisors and operators to understand why they weren’t on target was quite time consuming, not to mention most of their employees had difficulty remembering why problems had occurred when reviewing the issues reactively. There was no ability to identify systematic issues vs singular issues. Also, the lack of historical data made it difficult for them to calculate their utilization as well as their potential capacity.

Wiscon Products, a precision parts manufacturer, faced a significant challenge in data accuracy. The company's manual data collection methods were outdated and inefficient, often resulting in data that was several days to a week old by the time it was reviewed. This lack of real-time data made it difficult for the company to make informed decisions and implement improvements. Wiscon also struggled with gaining insight into production downtime, cycle time, and equipment utilization. The company had questions about how often spindles were turning and how many parts were being produced. Despite investing over $16 million on equipment, Wiscon was always running at capacity, and there were gaps and delays in the data, leading to a lack of visibility and an inability to respond to issues quickly.