By Bill Bither, co-founder and CEO of MachineMetrics
The IoT is finally gaining momentum within the industrial space, and manufacturers are now able to leverage a whole new arsenal of data to drive decisions. In an increasingly complex manufacturing environment and a globalized economy, companies are looking for any edge they can get to remain competitive.
MachineMetrics' Bill Bither
Our recently released 2019 State of the Industry (SOTI) for CNC Machining report, driven exclusively by real manufacturing data collected from hundreds of companies and thousands of machines globally, confirms many of our gut feelings about the industry. It also uncovers some manufacturing behaviors and trends we didn’t expect.
Let’s take a look…
What type of insights do we see?
We break our report into three sections: machine-level insights, plant-level insights, and economist-level insights, with each section telling its own unique story driven by data. For example, over the course of this year, we see trends for each specific machine type, with grinders being especially high performers. Utilizations for most machine types vary approximately in tandem with each other, showing stronger performance earlier in the year. This reflects a slowdown in manufacturing this year.
In addition to collecting raw utilization data, we also prompt operators to provide reasons for long periods of downtime. Looking at the rates at which various downtime reasons occur reveals a kind of “fingerprint” for each machine type in terms of its average operational and maintenance needs. Stamps stand out as particularly prone to losing productivity due to lack of an operator, and also tend to be stopped more often for changeovers. This is consistent with their overall low utilization. Horizontal lathes exhibit relatively high rates of cleaning and planned mechanical service compared to other machine types. By contrast, grinders and Swiss CNCs encounter downtime more rarely and generally for broader sets of reasons.
A shop owner could learn a lot about their own machines and operations by comparing against these patterns.
Digging into data at the factory level (all machine data aggregated together) reveals even more unique insights. We calculate utilization for each of the companies that we work with by averaging over all of the machines in their individual shops. What happens when we look at the distribution of these company-level utilizations among our customers? Most companies appear to perform at the 25% utilization level, with a few high-performers stretching into the 60% range. A shop owner can get an immediate sense of how competitive they are by just learning where they fall on this curve.
Finally, aggregating over all machines for all of our companies reveals industry-wide trends. One of the most basic questions that we can ask is: “On what days of the calendar year are machine shops actually up and running?” Utilization is lower for the week of Christmas, suggesting that is when many shops are shut down, and not for many federal holidays like Columbus Day or Martin Luther King day. These insights can help inform your HR policy. What days are other employees taking off?
Perhaps it makes the most sense to align your operation’s holidays with those of the industry, which can help align resources to best serve your customers. (Note: here at MachineMetrics we did this; we are adding more holidays around Christmas and removing Columbus and MLK Day holidays as a result of this data.)
Our data has also been tracking our correlation to several economic indicators, and we’ve found ourselves to be highly correlated to several key economic series including Industrial Production for Miscellaneous Metal Goods (the Fed’s proxy for Medical Device Manufacturing) and Value of Manufacturers’ Shipments for Motor Vehicle Components. This makes sense to us, as automotive and medical are two of the biggest industries discrete manufacturers serve. When our customers manufacture more of the component parts of cars and medical devices, their utilization goes up. The output of machined goods (engine parts, brakes, surgical implants, bone screws) closely correlates to the production of the products they ultimately go into: motor vehicles, trucks, metal implants, etc.
So what does this mean for the industry and the future of manufacturing?
The democratization of manufacturing data will undoubtedly change the manufacturing landscape forever. Gartner forecasts that 20.4 billion connected things will be in use worldwide by 2020. The IoT is already driving unprecedented disruption in what we know is a notoriously slow-adoption industry. IoT technology is transforming traditional manufacturing supply chains into dynamic, interconnected data systems that affect every stakeholder within the manufacturing lifecycle.
From small, medium and large manufacturers; operations, supply chain and IT leadership; operators and engineers; IT and OT system integrators; platform and software vendors; equipment builders; research, education and training—everyone stands to gain from these new technologies and data-driven insights.
Legacy business and operational structures will transform into horizontal, flexible and agile business opportunities more readily capable of being a part of a connected partnership ecosystem. This newfound ability to focus on what everyone does best will reveal opportunities for a more sustainable industrial economy.
Of course, data democratization is a tremendous challenge, as we’ve learned. But if leveraged properly, the opportunities for continuous improvement for all manufacturing-lifecycle members will be far greater than ever before.
