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The recent IoT collaboration between IBM and Cisco beautifully illustrates how modern solutions to old problems can potentially transform the industrial space. With IBM’s Watson IoT and business analytics technologies and Cisco’s edge analytics capabilities, users can more deeply understand and act on critical data on the network edge. These solutions work their magic by focusing on data: collect massive data at the edge of the network, and apply advanced analytics and cognitive computing technologies to take action and optimize results. There are many use cases that concentrate on machine health data, which can be used to optimize machine maintenance based on real-time condition monitoring. But we are also seeing applications that are more like conventional control optimization. For example, consider what SilverHook Powerboats is doing. SilverHook Powerboats is a company that designs high speed racing watercraft that reach speeds of up to 200 mph. Each boat containing two engines worth approximately $1.5M USD. Silverhook is using Cisco edge analytics and IBM Watson IoT analytics to help race pilots react immediately to environment and engine multi variate conditions in real-time, indicating the need to throttle back in a split second, for example, to help prevent the boat’s systems from failure and to perform optimally. Previously, without this instant insight into the critical data, the outcomes could spell disaster.
This marks a significant departure from older approaches, which typically involved local controls combined with dedicated process optimization software, execution software, and visualization software applications. With these applications, running the process is the main focus, and the data is secondary. Process insight was provided by engineers and coded into systems to run. The new approach turns this on its head by focusing on the data, which when combined with technologies such as cognitive computing, artificial intelligence, machine learning, streaming analytics, and edge analytics, can potentially optimize performance at a higher level.
This new IIoT approach surely has limitations - but exactly what they are isn't yet clear. How can users benefit from the many years' experience with engineered solutions, yet still take the important and transformative steps needed to move into the future? Let me know what you think!
This marks a significant departure from older approaches, which typically involved local controls combined with dedicated process optimization software, execution software, and visualization software applications. With these applications, running the process is the main focus, and the data is secondary. Process insight was provided by engineers and coded into systems to run. The new approach turns this on its head by focusing on the data, which when combined with technologies such as cognitive computing, artificial intelligence, machine learning, streaming analytics, and edge analytics, can potentially optimize performance at a higher level.
This new IIoT approach surely has limitations - but exactly what they are isn't yet clear. How can users benefit from the many years' experience with engineered solutions, yet still take the important and transformative steps needed to move into the future? Let me know what you think!