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History can teach us a great deal about improving plant operations. During the Industrial Revolution, processes were created to evaluate core performance indicators such as quality, worker safety, deliver time and inventory. These hallmarks of performance have stood the test of time, and meeting them today are imperative for every successful business. Yet new technologies and system advancements are quickly driving them forward and creating a level of magnitude not seen before. Now, digital technologies and networking infrastructure supported by the industrial internet provide valuable data for faster production processes, greater consistency and safer work environments.
With the proliferation of the industrial internet of things (IIoT), continuous improvement is becoming a hybrid of human intelligence and digital intelligence. Thanks to the IIoT, everything that has an electronic pulse can be network-connected, from large machinery to handheld electric tools. Although the IIoT is not yet ubiquitous, the market size is expected to grow to trillions of dollars during the next few years.
The IIoT was initially adopted by manufacturers who wanted to predict and protect the performance of high-capital equipment such as computerized numerical control (CNC) machines, air compressors and heating, ventilation and air conditioning (HVAC) equipment. Motor vehicle manufacturers were also pioneers in the application of the IIoT to improve manufacturing, just as they have been the first-movers on many other continuous improvement methods. They started using industrial internet technology for safety-critical assemblies and procedures but not for a class C or non-critical joint. After seeing the value the IIoT brought to high-value applications, they understood that connected devices can add another layer of safety, quality and performance at every level of the manufacturing process. As a result, many plants are converting all their tools to connected tools or building assembly lines from the ground up with only connected equipment.
In the early days of the IIoT, operators could only justify the costs of the digital controllers and the networks for high-value equipment. Now that the costs of sensors and controls have come down and networking is aided by economical fiber-optic cabling and cloud-based data management, manufacturers can apply the IIoT to just about any equipment and reap rewards. Cloud technologies give manufacturers the low-cost processing and storage needed to support the IIoT while leveling the playing field for manufacturers of all sizes to quickly and cost-effectively scale connected plant operations on demand.
For example, a manufacturer of high-end kitchen faucets realized that a busted faucet in a luxury home could equate to a high-stakes product liability claim and began using connected tools to track the data for the fastening point during the assembly of its faucets.
Machinery and tools equipped with embedded sensors and actuators send data to analytic systems that provide data for key process indicators. While data collection begins with equipment on the production line, it can also be aggregated with cloud metadata. By combining closed-loop data with an industry-wide data perspective, plant managers and engineers can gain new insights into trends in quality, throughput and efficiency to help them quickly and efficiently solve a myriad of production problems.
The IIoT is also beginning to play a role in information flow, work sequencing and error-proofing processes to optimize the time and effort spent collecting, organizing and understanding production data. A manufacturing plant with smart, connected equipment is able to share data with operators on the line, quality-control personnel and plant managers in real-time. The more data collected and analyzed from the production line, the more robust the sample sizes obtained to help manufacturers gain actionable insights, so they can make informed decisions for reducing waste and improving safety and product quality.
Total productive maintenance is an approach that applies lean tactics to the maintenance environment. With the IIoT, productive maintenance can be further enhanced by improvements to predictive maintenance. Adding sensors to monitor equipment and predict when the next downtime might occur enables companies to proactively address potential problems before equipment fails, eliminating unnecessary maintenance and downtime. When digital diagnostics identify a necessary repair, connected tools can be used to fix the equipment.
That's one of the many advantages of the IIoT; it allows manufacturers to be systematic and strategic, rather than simply reacting when a problem occurs. The integration of connected equipment, data and analysis helps manufacturers achieve higher quality standards and more control over their processes.
Despite recent advancements, surprisingly some of the most high-tech industries in the marketplace are still reticent to adopt, or even try, IIoT technology simply based on fear of change. Even those who are not early adopters will eventually convert, as enterprise customers will expect the peace of mind of knowing the data behind the performance and quality of a product.
As we look forward and the IIoT becomes integrated with artificial intelligence (AI), manufacturing will see a reduction in many time-intensive manual tasks. By leveraging machine learning, real-time predicative recommendations will give plant engineers advanced notice before production workers experience problems and safety issues. As more data is collected, machines will continually accumulate intelligence through assimilation, and virtual production assistants will help guide engineers to make improvements before operators even know an issue exists.
The IIoT is improving lean manufacturing exponentially, and its adoption will only become more rapid. The ability to gather raw data, as well as analyze and act on the data, is what will continue to fundamentally advance lean manufacturing and give manufacturers the control they need to improve processes in ways they have never been able to achieve before.