Modernization and Transformation spur IoT
Industrial and enterprise organizations are accelerating planned digital transformation efforts during the market disruption caused by the global health crisis. 28% of organizations responding to the 451 Voice of the Enterprise, Digital Pulse: Coronavirus Flash Survey October 2020 said that modernization and transformation have greater priority as a result of the coronavirus outbreak. These efforts combine process changes, organizational changes, and the use of sensors, communications, compute and analytics, broadly bundled under the moniker ‘The Internet of Things’ or ‘IoT’.
Industrial / Manufacturing at the forefront of IoT
While no industry vertical is immune from the forces of transformation, they are most clearly seen in the heavier industries of manufacturing, transportation and energy, referred to as IIoT, or the Industrial Internet of Things. These verticals are no newcomers to industrial automation, fleet telematics, and the suites of technologies for extracting operational efficiencies from energy infrastructure as diverse as offshore oil rigs to electric substations, however the introduction of new connectivity networks, pervasive inexpensive sensors, and edge computing and analytics has provided these verticals with a fresh toolbox to redesign their operations.
Industrial Use Cases
IIoT verticals are the most pragmatic and focused of all industries, balancing operational benefits such as increased uptime and cost savings with capital expenditures. The projects, or workloads, that are deployed reflect this pragmatism as shown in Table 1 below. They balance the utilization of equipment, avoiding unscheduled downtime, tracking vehicles and inventory, and ensuring the safety of workers in these ‘boomable’ industries.
|Primary IIoT Use Cases Implemented by Vertical|
|Manufacturing||Transportation||Oil and Gas|
|Production Monitoring (72%)||Fleet Tracking (46%)||Production / Asset Management (36%)|
|Predictive or Condition-based Maintenance (63%)||Vehicle Diagnostics / Predictive Maintenance (45%)||Worker Management / Safety (34%)|
|Inventory monitoring (57%)||Cargo Tracking (38%)||Field Optimization / analytics (30%)|
Table 1: IoT use cases implemented today by industrial vertical.
Source: 451 Research Voice of the Enterprise: IoT, The OT Perspective, Use Cases and Outcomes 2020
The rise of Digital Twins
Safety concerns and automation were already driving the adoption of Digital Twins within these industries. Digital Twins are 3D models built from sensor and actuator data on connected devices, providing a window into equipment operational status including data that may not be accessible when looking at the physical asset itself. Digital twins’ primary use cases within organizations are to speed access to cross-plant instrumentation, to reduce cycle time for designing new processes and installing new equipment, and simulating ‘what-if’ scenarios for maintenance, diagnostics and process improvement.
The use cases mentioned in Table 1 each differ in their operational parameters. Some require real-time or near-real-time feedback for manufacturing or energy processes, some are broader ‘aggregation’ exercises that require data from multiple machines or sites to train machine learning models. This has highlighted the multi-polarity of workload venues, from the cloud, to aggregation points like corporate datacenters and hosting, to all the way out at the point of data origination at the edge of the network. The reasons for workload placements differ, based on the security, latency requirements, cost and availability of bandwidth, access to legacy (non-IP) equipment for data acquisition and control, data sovereignty, and resilience in the event of wide area network failure. In IIoT contexts, 39% of data is initially analyzed and stored in the cloud, off-site enterprise or third-party datacenters, according to respondents to 451 Research’s Voice of the Enterprise, Internet of Things, Workloads and Key Projects 2020 survey respondents. The remaining 61% is analyzed and stored at the edge, or near edge, on venues from the device itself (if compute-capable enough), gateways, dedicated computers, or in micro-datacenters near the source of data. This diversity of venues introduces orchestration, security and management challenges that were, while still complex, simpler in a cloud-first IT environment.
Industrial organizations are censoring data more and more, and leveraging their sensor data will continue to reap them benefits. There are common data requirements: real-time, edge-access, core-access, and reliance upon artificial intelligence and machine learning, making the future of Industrial IoT smarter and smarter.
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