A cost-effective, pragmatic approach to distributed intelligence
A scalable analytics platform can provide a cost-effective and pragmatic way to distribute actionable intelligence across all levels of the organization – on the edge, on-premises or in the cloud.
For example, take a look at this new analytics solution, which embeds analytics and machine learning capabilities at the edge – and closest to the source of the information and plant-level decision makers.
Delivered on a plug-in appliance, set-up is simple. No rewiring of existing smart sensors is required. Connect Ethernet and wait about five minutes while the solution crawls the industrial network and discovers smart assets. As devices are discovered, data is collected, and health and diagnostic dashboards are built and delivered.
As the appliance uncovers information about how the devices are related to each other, such as fault causality, it starts to understand the system on which it is deployed – and can make prescriptive recommendations. For example, it can send an “action card” to a user’s smartphone or tablet.
A scalable approach to analytics can have an immediate impact in many areas of tire manufacturing. And will become increasingly important in complex applications like mixing and curing, where machine learning can have a dramatic effect on product quality, manufacturing agility and energy efficiency.
But that’s just part of the solution. The scalable platform extends beyond the edge to compute engines and tools that automatically orchestrate data from multiple sources – and allow users to see commonalities and gain operational and business insights faster.
The bottom line? A scalable approach enables tire producers to more deeply engage each level of the organization in optimizing its manufacturing process – from local engineering and maintenance to the top levels of the company. And from the device to the cloud.