“One of our primary goals was data accuracy. Before the implementation, we could look at data, but it was anywhere from a few days to a week old. We were looking for something to provide real-time data, so we could accurately know where our production and utilization was. We also wanted to find out the leading reasons for our downtime. One of the biggest things was equipment utilization versus parts goals. How often were our spindles spinning versus how many parts we were actually producing? This is incredibly valuable to us in understanding cycle time.”
The biggest change has been in efficiency. Specifically, being able to visualize the data and act upon it in real-time. We didn’t have a lot of historical data and what we did have could only be addressed reactively. And planning and scheduling was a top priority. Without accurate data, planning and scheduling were difficult. We had spent over $16 million on equipment but still felt like we were running at capacity. Our production department was always “busy”; there were gaps in the data and not being visible in real-time caused a lag. The benchmarking information MachineMetrics provided was eye-opening. It was staggering to realize how bad we were. We didn’t have a reasonable expectation of how well we should be doing, making it difficult to gauge where we should be. One of the biggest benefits since implementation has been equipment utilization— knowing when the spindles are spinning. Previously, we had to rely on operator perspective that wasn’t tied to actual data. Now, with measured and categorized downtime, we can see when the production of a specific part isn’t lining up to standard—and understand why it's happening.”
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