Data has turned into a powerful asset in the time of Industry 4.0, offering unique insights and opportunities to optimize production. This article examines the strategic use of analytics to transform the efficiency of sheet metal working and CNC production lines. It moves from the immediate, real-time monitoring of these processes to look at how such immediate, data-driven decision-making can serve as a foundation for a next-level transformation in the use of predictive maintenance—driving both lines and the companies they serve toward operational excellence.
The budding concept of data-driven decision-making starts with the real-time monitoring of production processes. In sheet metal working, as well as in CNC (computer numerical control) machining, KPIs (key performance indicators) such as cycle times, energy consumption, and overall machine health are monitored using sensors embedded in the machinery. These massive data sets can be stored and analyzed in the cloud. Onshore and offshore manufacturers of all kinds can now access cloud-stored data for near-instantaneous analysis.
Manufacturers can now look to data analytics for an early warning system for their equipment. By sifting through the data from their machines, they can either anticipate or predict failures. The manufacturers can then use this knowledge to schedule maintenance for machinery that's in predicted danger of failure. This is the kind of maintenance ahead of the curve that not too many dollars ago might be called "state-of-the-art."
Production data from the past is a treasure trove for fine-tuning production schedules. By scrutinizing their historical performance, manufacturers can spot not only the times when production surged but also the sites where resources were constrained and the near misses that almost hit the target—but didn't. They can, in other words, study the last set of "time series" data to make the next set of optimized schedules. They can push the next set of production resources to align better with the next set of demand fluctuations.
The real-time analysis of data from all stages of the sheet metal working and CNC production processes enhances product quality and reduces defects. The increasing ability to collect and analyze massive amounts of data from the manufacturing process can also help with product design. By analyzing the near-endless variety of designs that these machines can produce, coupled with the many combinations of parameters that they can take, manufacturers can gain incredible insights into possible design trends and customer preferences. These insights are then fed back into the design process.
Making decisions based on data extends to the optimization of costs and energy use. When it comes to energy consumption, manufacturers have a distinct advantage. By virtue of their size and the intensity of their operations, they have a broader array of potential efficiencies to tap into. "Energy efficiency and optimization are not about finding a single silver bullet," says Keith Kline, a senior researcher at Oak Ridge National Laboratory. "They're about a whole portfolio of projects, and analytics makes it possible to find and implement them."
The integration of data analytics with the supply chain is almost seamless. Manufacturers are increasingly turning to data analytics for clear visibility and greater control over their supply chains. By synthesizing data from disparate sources across the supply chain, manufacturers can understand the current state of their supply chains and make better decisions about the future—preventing shortages and outages, for instance, from happening in the first place.
The continuous improvement of any process or system requires that key performance indicators (KPIs) be established and that performance metrics be analyzed on a regular basis. In a manufacturing context, this means keeping tabs on things like production yield, machine uptime, and overall equipment effectiveness. If a factory can’t do this for some reason, then it’s hard to see how the factory can be made to function any better.
Moving to data-driven decision-making in sheet metal working and CNC production lines represents a shift toward intelligent manufacturing. Harnessed correctly, data can and should illuminate every corner of your facility, offering (and sometimes demanding) the insights necessary for even smarter operations. Not every outfit will be able to reach this point of data democratization, but analytics promised and deliver at least some intelligence, if not deep intelligence, to the manufacturing operation. The basic frameworks of intelligence hold good whether talking about cost savings or some framework of innovation that helps manufacturers regain their competitive edge.