- How do the quickly changing consumer trends affect the design, form and function of packaging machinery?
- How is industry addressing the shortage of experienced technical employees?
- How much importance should the packaging equipment industry place on the IIoT and the productivity gains that big data promises?
- How will IP67 and IP69K components affect machine-design decisions?
As consumers grow hungrier and hungrier for packaged goods that satisfy their appetites for variety, it is having a profound effect on manufacturers that reaches all the way back to the machine builders that make the packaging equipment used in these production operations. Our panel of experts answer questions on topics ranging from changeover and retrofits to skilled employees, the Industrial Internet of Things (IIoT) and moving components out of the cabinet.
How much importance should the packaging equipment industry place on the Industrial Internet of Things (IIoT) and the productivity gains that big data promises?
The basis of IIoT is collecting data from sensors and actuators that can help to build some intelligent information about the system and the process. The very first thing is to have the framework in place for collecting such information. Again IO-Link is one of the most important frameworks that enables three types of communication on the same cable: process data—cyclical process-related communication; parameter data—acyclical messaging communication; and events data—data generated by the IO-Link device or sensor in case of abnormality.
Once the IO-Link framework is in place, the manufacturers can collect, store and build intelligence on their own, regarding their particular processes to enable the IIoT on the plant floor.
It’s generally low-hanging fruit for our customers. Given the low margin of most consumer-packaged-goods (CPG) customers, it’s surprising that this isn’t moving faster.
What we are calling IIoT today, and wrestling with, will come into its own as technology catches up, from security to interoperability, so we need to be patient, and in the Industrial Internet Consortium's Smart Factory Task Group we are identifying standards as well as best practices that are unique to our deterministic, high-speed machine operations. Modular, track-based machine design is developing in parallel.
Much is being said of analytics, and that's important because most larger manufacturing companies are awash in data. But someone with process knowledge and machine knowledge is going to have to determine what to analyze, to interpret the analysis and understand how to optimize the process, operations and/or materials. This knowledge will need to be captured in self-learning technology, such as servo algorithms that adapt to changing mechanical disturbances caused by wear, to predict and compensate automatically. This is already available.
All automation systems should be IIoT-ready, which means having the flexibility, as well as multiple means of connectivity. Internet connectivity is of course required, but connectivity to field devices through support for various protocols such as serial, Modbus RTU and Modbus TCP is also needed.
Overall equipment effectiveness (OEE) has reached amazing levels. Understanding the interaction between machine variables, product variances and the surrounding environment can further define areas of improvement. Analysis of production data allows data analytics to convert the “art of running a machine” into science. Imagine achieving your highest OEE at all times regardless of the operator, material or environment. Data analytics preemptively warns operators or control systems of impending problems. The operator or machine control can make adjustments to avoid the problem or schedule repair/adjustment during scheduled downtime. Data analytics will reduce downtime, increase throughput and turn art into science.