Tarigma software applications implement the distributed network reference architecture known as Fog Computing. Fog computing is a term created by Cisco and The OpenFog Consortium that refers to distributing cloud computing to the “edge” of the enterprise. Also known as edge computing or fogging, fog computing facilitates the distribution of computing, storage, and networking services between edge devices and cloud computing data centers. By moving the collection and conversion of raw data to actionable information closer to the data sources, we minimize the latency between critical events and required responses, all while reducing the network load and consumption of bandwidth. Fog Computing has emerged in direct response to the exponential growth of three technologies: IIoT, 5G and AI.
The Manufacturing and Energy Management Industries that Tarigma serves are being impacted by all three of these trends:
Manufacturers, seeking to improve their use of labor and capital in the pursuit of happy customers and profits, are moving their business models from make-to-stock to make-to-order. Suppliers monitor order flow and vendor-managed inventory for just-in-time delivery to the process or assembly line. Smart sensors measure quality and specs of incoming raw materials, work-in-process, and finished goods alike. The flow of all materials and processes is tracked from supplier to customer by serial and lot number while cloud-based neural networks look for sources of quality defects, production bottlenecks, and shifts in consumer demand.
Meanwhile, Energy Management Utilities whose revenues and expenditures are managed by a bureaucratic regulatory framework are not immune to these advances in technology. In fact, there may be no better use case for the intersection of these three technologies than the Smart Grid initiatives washing over Electric Utilities. As IIoT smart meters are rolled out to homes and businesses and smart sensors monitor generation, transmission and distribution at every stage of the grid, outages can be accurately pinpointed and rapidly resolved with a huge improvement in service availability and reduction in maintenance costs. Likewise, IIoT smart switches, augmented by battery and other energy storage technologies, enable time-shifting to level peak demand and delay the capital costs of building out new generation. Cloud-based deep-learning applications use this vast data resource to predict equipment failures before the fact to support “preventative” and condition-based maintenance programs. But as distributed network technologies are pushed to the very edge of the smart grid, their vulnerability to cyber-attacks and threats increases.
Hence, the just-in-time arrival of Fog Computing to: