Today, most IoT analytics operations occur in the cloud. This is partly driven by supplier offerings and partly because the cloud offers a centralized location for large amounts of affordable storage and computing power. There are, however, a growing number of instances in which it makes more sense to perform analytics closer to the “thing” or activity that is generating or collecting data – equipment deployed at customer sites (generators, trains, wind turbines). This is particularly true in industrial and manufacturing environments, which are familiar with the challenges of managing massive amounts of generated data (typically by parking it in a data lake or the like) and general digital product development (e.g., CAD models), but lag when it comes to the virtualization of business-critical infrastructure. Advances in intelligent process manufacturing, factory automation, and AI/ML model development benefit from edge analytics implementations yet are nothing but islands of automation without the industrial cloud.
The industrial cloud covers everything from the factory floor to the industrial campus, and it is unifying the supply chain as companies employ a combination of digital business, product, manufacturing, asset, and logistics planning to streamline operations across both internal and external processes; make it easier to optimize asset and process allocations by modelling the physical world; and use data and subsequent insights to enable new services; and improve control over environmental, health, and safety issues.
This report provides foresight on the drivers, inhibitors, and inflection points fueling future IIoT strategy decisions. It examines the different cloud service models and how they are changing; identifies the key players and how they are jockeying for position, and unpacks the value chain. It also includes a snapshot of ABI Research survey data with a special lens on industrial and manufacturing.