Can Sharing CNC Data Lift the Machining Industry?
I’m writing this column a week after attending a March 25 webinar about the effects of the coronavirus on the manufacturing industry. Although the presented data will be out of date by the time this is published, the pandemic was not the point, at least not for me. The broader takeaway was the potentially vast power of widely shared machine utilization data for improving business planning and benchmarking, whatever the economic situation.
The power of this dataset is that it is available in real time, in this case through webinar host MachineMetrics’ multi-tenant cloud platform. Taken from thousands of machines in every sector of the industry, the anonymous, aggregated data often reveals trends and insights prior to leading economic indicators such as industrial production and the Purchasing Managers’ Index (PMI), says company co-founder Bill Bither.
He went on to show graphs of utilization and downtime data illustrating steep declines in the automotive industry after the “Big 3” automakers shut down only days before. “We have the real-time element — the pulse of manufacturing,” he said.
Quarantined at home, my first reaction was that anyone could see things are changing, and that the pandemic’s grip on the economy has only begun to tighten. However, the potential in this data was just as obvious, and other insights are already coming into focus. For instance, during a phone conversation a week after the webinar (which was the first in a planned series of presentations on the impact of the virus), Mr. Bither speculated that an uptick in automotive utilization might be a result of manufacturers retooling for medical supplies. Pandemics aside, less obvious correlations and trends that might guide manufacturers’ planning are likely to show up here first.
Perhaps more importantly, revealing broad economic trends is only a beneficial byproduct of the company’s data collection. “The real benefit for the machine shop is how this data correlates to their own operations,” Mr. Bither said.
For instance, MachineMetrics’ regular reports reveal that having a “case of the Mondays” is a very real phenomenon. The aggregate data show that, in general, machine utilization tends to be lowest on that day before peaking by Wednesday and dropping again into the weekend. This can also be narrowed to hours of the day, and extrapolated to the entire calendar year. In one case, proving a gut instinct with real data led one MachineMetrics customer to eliminate Columbus day and extend the Christmas holiday instead.
Finding surprises in the data is even more valuable, Mr. Bither said, pointing out that shops’ actual average machine utilization, which is generally around 25%, pales in comparison to the 60% or greater that most users assume prior to implementing company’s machine monitoring system. Where does a shop stand in relation to other businesses in machine utilization as a whole, or, more specifically, time lost to changeovers? Which machine types experience more downtime on average, and why is that? Are shops in the Northeast experiencing higher utilization rates than those in other regions?
MachineMetrics aims to make such insights available at any time to everyone via regular “State of the Industry” reports published online, Mr. Bither said. These reports would not be possible without the system’s users, all of which must opt in. Beyond being willing to share data, they are obviously comfortable with machine monitoring generally, as well as cloud computing and, if that is any indication, other tools of data-driven manufacturing. Data democratization, and the benefits all stand to share, begins with individual shops understanding that in many ways, they’re all in it together.
A manufacturer that is distinctive for its attention to in-cycle machining productivity describes its efforts to obtain efficiency improvements outside of the machining cycle. The shop’s primary tool is a simple, daily, graphical recap that illustrates when each machine tool was and was not making parts.
Decisions about the cutting tools used in machining operations are arguably among the most important in modern manufacturing.
An introduction to the standards, decision-making, training, cybersecurity, sensors, machine monitoring and cloud computing that make up the IIoT.