IoT

The Industrial Internet of Things (IIoT) specifically refers to the IoT in industrial operations, typically associated with manufacturing, supply chain management, transportation, healthcare, energy, or utilities. Common applications for IIoT include monitoring, predictive maintenance, and solutions for operational improvements. More advanced deployments of IoT can help enable entirely new business models, such as outcome-as-a-service.

The demands on IIoT are typically (but not always) higher than on consumer or commercial oriented IoT in terms of availability, security, and scale. The Industrial Internet of Things is also referred to the Industrial Internet or Industry 4.0.

Broadly speaking, an IoT Platform (or IIoT Platform, for industries) refers to the technical foundation enabling accelerated digital innovations with IoT. An IoT Platform promises to significantly accelerate the time to value with Digital Innovation and Digital Transformation initiatives, lower cost, and improve success rate. All of these initiatives require a business-led approach to IoT projects. An IoT platform allows businesses to stay focused on outcomes versus technical implementation details.

Key attributes of an IoT Platform include: edge connectivity; device management; integration; data management and analytics; including streaming data and streaming analytics; digital twin management; security; composability / modularity; edge-core-multicloud orchestration.

Digital Twins are digital representations of real-world entities (sensors, devices, machines, processes, complex systems, and even people/person/living beings). They are deployed to drive business outcomes. They provide connectivity, metadata management, data management, increasingly advanced analytics, and often integration with business applications and process systems. Digital Twins can be organized and structured in different ways, such as hierarchies, topologies, etc. to represent the taxonomy of entities.

In its most basic form (low fidelity), a Digital Twin encompasses metadata about the respective entity/asset, and a means to monitor it in (near) real-time. More advanced (high fidelity) Digital Twins encompass analytic models (physics-based, or increasingly machine/deep learning-based) that enable prediction and simulation, allowing for comparison of expected versus real behavior, "what-if" scenarios, and continuous improvement of models through feedback loops.