The factory of the future will be self-optimizing
October 2, 2017
By Jeff Nedwick, Infor director of automotive product management & strategy
Forget about your image of assembly lines churning out identical products. The factory of the future is likely to be the opposite of mass production.
Mass customization of consumer products has been spreading slowly for nearly two decades. One recent example comes from Nike, which launched NIKEiD in 2012, an online service allowing consumers to create a unique pair of sneakers by picking their own colors, designs, and performance features on certain shoe models. Customers pay a big premium for that kind of customization.
Unfortunately, when applied to the complex automotive manufacturing processes, this kind of customization also squeezes profit margins and leaves little room for waste or inefficiency.
But clearly mass customization is here to stay, so the challenge for automakers comes down to this simple question: How do they make customized, low-volume cars cost effectively?
One way is to ensure manufacturing operations are running at optimum efficiency by ensuring that plant floor equipment can detect and correct inefficiencies and anticipate the need for maintenance through predictive analytics. Underpinning this future self-optimizing state is the ability to integrate and collect real-time information from every piece of equipment, device, and sensor on the plant floor.
That’s where digital transformation via the Industrial Internet of Things (IIoT) comes into play.
The automotive industry has a head start with IIOT. Automotive manufacturers were automating and connecting manufacturing equipment on the plant floor long before the term “Internet of Things” was coined, and that experience has made it easier to now see the value obtained by collecting and analyzing all the data being generated on the plant floor.
The advanced shop floor management techniques of the factory of the future will rely on data collected directly from shop floor equipment to calculate actual machine performance vs. planned machine performance in real time. This immediate feedback detects and predicts breakdowns or inefficiencies — in both processes and equipment — and allows operators to take corrective action if a deviation from target is detected.
In addition, plant managers can compare manufacturing performance between plants, lines, or machines to ensure all processes are running at peak efficiency.
Data collected from plant floor equipment can also eliminate unscheduled downtime by assessing the health of critical equipment components and predicting equipment failure to schedule repairs before a breakdown occurs. This paradigm shift from preventive to predictive is enabled through analysis of machine and sensor data and seamless integration to enterprise asset management applications.
In a typical high-volume automotive manufacturing environment, it can be difficult to trace with precision the production lineage of a given product. But in the factory of the future, access to historical data collected from manufacturing operations enables both backward and forward end-to-end traceability to the root cause of a product defect.
As automakers struggle to meet the demands of more sophisticated and demanding consumers, the factory of the future provides an effective way to offset the increased costs associated with lower volumes and highly customized vehicles.