While much has been written about applying the principles of the Internet of Things to industrial manufacturing to improve processes and outcomes, there’s clear evidence that those principles are being put into practice and are having a real impact.
The IoT is one of those concepts where the old phrase, “When all’s said and done, there’s more said than done,” would seem to apply. But that’s changing rapidly. LNS Research just brought out a study, “Manufacturing Metrics in an IoT World,” that measures the progress of the industrial IoT and the comparisons to 2015 are startling on many levels.
For example, the number of respondents who didn’t understand or know about IoT fell from 44% to 19%, the number of those investigating the impact of IoT rose from 21% to 33%, and, most tellingly, the number of those who do not expect to invest in IoT technologies fell from 36% to 25%. In parallel with the latter, the number expecting to start investing in IoT technologies in the next 12 months rose from 23% to 29%, but they still have to establish a budget.
Clearly, something has fired up the back office enough to consider rethinking manufacturing norms. As we’ve discussed to date, much of it has to do with the improved processes, products, reliability, outcomes and savings. The applications are many, and LNS Research detailed each of those uses and respondents’ attitudes toward them (Figure 1.)
Figure 1. LNS Research identified and listed respondents’ uses for big data analytics in the enterprise. However, having the data and analyzing it properly are two different things. (Image courtesy of LNS Research.)
While the many uses are clear, it turns out that the biggest opportunity for manufacturers resides in getting the right analytic skills: LNS notes that only 40% have the right analytic skills, with a serious split and a certain level of mistrust between engineers and data scientists (for more on that, and the many other interesting findings, you really should see the full report.).
The overarching point is that big data analytics is rising in both awareness and its real-world investment and application, but it’s not an easy path: new frameworks need to be put in place and at the factory floor, there are legacy plants that need to be kept running while upgrades to an IoT analytics-based approach is applied.
From the frameworks point of view, LNS has already laid out what it calls a Digital Transformation Framework that will help both IoT Solution Providers and their customers encapsulate and articulate the steps required to migrate old manufacturing to an IIoT-driven business (Figure 2.)
In the meantime, many companies are already applying IoT principles to improve their business. Linear Technology bought Dust Networks, a small wireless mesh networking company, and has now retrofitted its semiconductor wafer manufacturing facilities with Dust Networks technology to acquire data on 175 gas cylinders required for manufacturing. This is a classic example of retrofitting established plants to monitor critical systems to ensure uninterrupted supplies and avoid downtime. IC wafer facilities are optimized for yield and throughput and downtime is unacceptable.
However, the IIoT extends beyond the factory floor as manufacturers realize the importance of value-added services to their customers. A good example of this is Diebold, a manufacturer of ATMs, which developed a data gathering and analytics approach called Opteva to service its equipment – without expensive and time-consuming truck rolls. The result? 17 percent of problems have been resolved remotely, with a 15% decrease in downtime, while turnaround time for problem resolution has been reduced from three hours to under 30 minutes.
The number of real-world examples is continuing to rise, but LNS Research has some good advice for companies who may not be aware if the potential of IIoT: Get out of the 19%.
Start investing the IIoT, try analytics beyond the plant, and building commercially viable pilots to use as platforms to investigate more complex analytics and better integration.
New facilities are ripe for the application of IIoT principles, but as Linear Technology has shown, an installation already in operation can also be upgraded to take advantage of the analytics and maintenance advantages.