May 9, 2019 - The Internet of Things is disrupting every aspect of retail, design, manufacturing, sales, and consumer space. So it’s no surprise that the rise of IoT is making itself felt within production systems in the manufacturing environment.
But before we get ahead of ourselves, let’s talk a moment about what the IoT is reinventing – production systems. Simply put, a production system is composed of the elements and guiding principles that define how a business runs, reviews and improves its operations.
Designing the Production System
This is the stuff of systems engineering; it maps the guiding principles that define how tools, methods, and manpower are used to design and produce the product. A business’s production system also includes a continuous improvement loop, which reviews the results of the operation and brings changes in quality, productivity, scale and other considerations to continuously hone the overall business.
While production systems define that overall structure of a businesses’ operations, the reality is that production systems are often somewhat unstructured, defined in general terms by a variety of disparate tools and workforces, documented in various ways with best practices and printed guidelines. And whether you call yours a production system or not, your business’s manufacturing floor lives and dies by how well it performs.
Transformation From The Ground Up
The IoT is slowly transforming production systems from the ground up, and the future holds almost unlimited potential. An industrial IoT’s network of sensors, for example, combined with edge processing – processing the data at the edge of the network, on the shop and manufacturing floor – adds up to unprecedented levels of potential integration.
When you add the kind of near-real-time analytics you can get thanks to crunching the numbers stored in data lakes on site, it’s possible to get immediate and continuous feedback on the effectiveness of the production system, and to make changes in real time. Problem solving will become vastly simpler, with a wealth of current and historical data at managers’ fingertips. Isolating root causes of production problems will be much easier to accomplish.
Getting Instantaneous Feedback
Moreover, the continuous improvement process is traditionally a somewhat lumbering affair, one that often can’t respond until entire production cycles have completed – whether that’s a single shift or a complete manufacturing run. But now, thanks to the IoT, instantaneous feedback is available on what’s happening in the production process and how to improve it.
And critically, the entire production system can finally be codified – not in a set of binders, but thanks to the automation that the IoT delivers, in the settings and specifications of the actual tools and production workflows. This is no small feat, because it ensures that best practices aren’t simply referenced, but are instead implemented in the equipment and processes by design. The IoT ensures that the most efficient path is the one that’s automatically executed, and course corrections can be made in real-time via online dashboards.
Real World Examples
Early adopters are starting to reap the benefits of these kinds of production system improvements. Black & Decker, for example, integrated an IoT into its manufacturing plant in Reynosa, Mexico, for example. By adding Wi-Fi radio frequency tags to all of the materials used in the manufacturing process, Black & Decker was able to monitor the status and quality of every stage of the production process. The company said it has improved labor efficiency by 10 percent and bolstered quality, with first-time pass defects reduced by 16%.
Similarly, AW North Carolina – which manufactures automatic transmissions – added an IoT to its 1.3 million square-foot factory in Durham. The company produces over 3,000 transmissions per day, and each one contains as many as 800 specialized parts with their own set of quality and performance standards. AW is using the network to perform new and sophisticated enterprise resource planning (ERP) and manufacturing execution systems (MES) to automate and analyze its data and processes.