Industrial IoT (IIoT) is a subset of the Internet of Things (IoT) revolution that refers to the application of IoT principles, technology, and approaches, specifically in industry, manufacturing, energy and similar sectors. For all industries, IIoT ultimately aims first at gathering and analyzing data from factory sensors and devices, and then secondly to make intelligent responses based on data-driven insights. Automated real-time responses can be implemented to significantly streamline performance.
IIoT concepts are similar to other IoT concepts, in particular the networking together of numerous small devices, sensors, instruments, and actuators, to create the “internet of things”, a convergence of networking and device technology. However, IIoT differs from common IoT examples like smart homes, in both the degree and scale of technologies that are connected. Smart home sensors can monitor temperature, and send mobile device notifications in emergencies. Comparatively, in larger industrial settings, IIoT may orchestrate the operations and interactions of tens of thousands of devices, sensors, and robots. This difference requires more complex implementation methods, including using IIoT platforms, sophisticated device management software, and custom integrated automation tools.
Industrial applications have become the perfect testing ground for upcoming IoT technologies, including the following:
Digital Twins — Digital twins is an innovative way using computer modeling, the digital twin, to predict behaviors and anticipate problems through the life of a real world object, process, or ecosystem. Simulations using real-world data can be applied to the digital twin model and reveal predictive insights, which can be used in pre-designs, or to monitor existing machine performance, and for example, suggest maintenance schedules to maximize ROI.
Electronic Logging Device (ELD) — IIoT extends beyond factory settings, demonstrated by vehicle sensors like electronic logging devices (ELD) used in commercial fleets. ELDs connect a series of on-vehicle sensors to monitor trip mileage, time, gas usage, and even brake deterioration. Firstly, these devices help replace error-prone paper logs, and secondly capture essential vehicle, trip, and driving data that can be used to optimize fleet operations.
Intelligent Edge — “The Edge” is a frame of reference where compute and storage resources are positioned in relation to the data generated by a IoT and mobile devices. The general IoT analytics model is that when data is generated from sensors it is communicated to a central server for processing and insights. Today, sensors are generating huge volumes of data, but require an almost instantaneous computation response to that data. In many instances, a central server, even if located on-premises in the next building, may introduce lag times that hamper performance. To solve this problem, computational and storage resources are placed closer to the sensor devices, or closer to “the edge”. For example, on-board computers work with networked GPS systems in self-driving trucks to not only control the vehicle in the moment, but to coordinate with others on the road. Now, sensor data can be processed instantly where the data is created, at an intelligent edge.
Radio-Frequency Identification (RFID) — RFID technology significantly enables the tracking and tagging of items with identification and detailed object information. RFID is a commonly known technology, with a long existence of over 70 years, but as the technologies in the RFID ecosystem have become cheaper, standard, and widely adopted, it has become a way for entire value chains to integrate and provide greater efficiencies, and innovative new services such as omnichannel customer engagements.
IIOT has found many applications, most notable in smart factories that can monitor and control hundreds of variables on the factory floor. The following examples illustrate the variety of uses for IIoT.
Industrial Production Lines — The most common application for IIoT is production and manufacturing where IIoT machines and devices can self-monitor, analyze, optimize, and prevent problems to achieve greater throughput and less downtime.
Supply Chain Integrations — IIoT combined with RFID technologies enables a new revolution of global supply chain cooperation. For participants, IIoT tracking at the item-level allows for waste reduction and time savings. Supply chain participants can now integrate and machine level between each other, and reap significant efficiencies.
Smart Building Management — IIoT can combine with building management software to unify, simplify, and automate building management. With data from various system sensors, like climate controls, energy, water, the building can optimize systems to reduce consumption during down times. By layering data, like current building occupancy data drawn from security entry points, building usage can be mapped with granularity.
Healthcare Patient and Inventory Management — Healthcare efficiency is a key concern especially in modern times when demand continues to challenge healthcare capacity. IIoT helps to alleviate pressure by providing a platform for monitoring nearly everything within a clinical environment from tools, inventory, patients, healthcare staff, operating rooms, etc. With a precise picture of all these elements, managing for efficiency becomes a possible game-changer.
In-store Retail Management and Omnichannel Marketing — Retail IIoT supply chains coupled with in-store IoT applications has opened up fertile fields of innovation in customer omnichannel engagements. Because of the exceptional ability to track products and coordinate how they are presented throughout the retail value chain, customers now can expect to engage with customers in a seamless manner from any touchpoint, whether that is digital, mobile, in-store, etc.
IoT is an acronym for Internet of Things, and refers to appliances, sensors, and otherwise common devices, like thermostats, lights, heating and cooling, that have been network enabled. Further, these devices can communicate amongst themselves, machine-to-machine (M2M), to perform simple tasks, like adjusting lighting to conserve energy. IoT fits most consumer models, and aims to add some layer of “intelligence” to common “dumb” devices.
Industrial Internet of Things (IIoT) refers to IoT devices and machines within industries like manufacturing, energy, or transportation. In the industrial context, robots, vehicles, and industrial lines can all be IIoT enabled. Unlike consumer IoT, more sophisticated IIoT orchestrates device automations to ensure that production matches real-time demands.
The table below illustrates a comparison of common aspects shared by both IIoT and IoT.
Industrial IoT (IIoT)
Internet of Things (IoT)
Industrial applications, manufacturing, power and energy, etc.
General appliances, wearables, robots, sensors
Complex and wide area networks, with thousands of connected devices
Small networks, with a handful to many devices
Massives volumes and velocity of data generation requires special storage and compute setups
Low to high volumes of data dependent on application
Remote and on-site programming
Industrial sensitivity requires robust data security features
Personal data requires identity and access management
Industrial internet of things empower manufacturers to leverage streaming data from all their connected devices and use powerful analytics to operate smarter.
IIoT connected devices create a platform to be able to rapidly roll out new features. IIoT grants the ability to remotely monitor, manage, and analyze device data. From a product perspective, this allows designers to understand how products are being used, and engaged with, delivering incredible insight into new product features, and importantly product shortcomings. From a marketing perspective, this insight can be turned into innovative offers, bundles, and value-adding opportunities.
Real-time performance monitoring is key to optimizing manufacturing processes. Traditionally, KPIs are used to baseline performance, and analytics can determine when deviations are detected from normal operations. Newer approaches now exist, like creating a virtual model of a physical asset to simulate its performance, known as Digital Twins.
IIoT enables the integration of digital supply networks by connecting systems between suppliers, distributors, and eventually customers. Technologies like RFID, which enables item-level tracking throughout the supply chain, help extend the capabilities of IIoT.
Predictive analytics uses the help of AI to process the massive volume of IIoT data and deliver forecasts that can ensure optimal maintenance thereby extending the lifetime of devices. In business scenarios, like expedited shipping, data from delivery vehicles can be used to forecast safety maintenance and keep vehicles roadworthy.
Some confusion develops around the industrial internet of things (IIoT) and manufacturing execution systems. MES applications have been in use since the 1990s, and in the wave of modern industrial smart technology adoption, IIoT has become synonymous with industrial automation, and Industry 4.0, spurring the notion that MES has been replaced. To clarify, the Industrial Internet of Things (IIoT) and manufacturing execution systems (MES) work together to provide a complete picture of a manufacturer’s floor operations.
For many business benefits, data and its analysis has been recognized as a fundamental requirement in modern manufacturing. For instance, to perform tasks like root cause analysis, large quantities of data are needed. IIoT gathers much of this data, like contextual details from devices, sensors, machines and controllers. The MES combines the data from IIoT with its own context, like customer details, orders, products, recipes, billings, etc., to complete the total picture of operations within the wall of the factory.
While both IIoT and MES collect data, the MES, in conjunction with team members, owns analysis and determination of situational changes in the ongoing manufacturing process. This means, installing just an IIoT will provide a communications backbone between and among devices, but it will not include the analytical brain to compile the context.
IIoT Platforms are, like IIoT, also often confused with manufacturing execution systems (MES), however, they can both work together. In the sequence that leads data from where it is generated, gathered, analyzed, and eventually turned into actionable insights that leads to changes in the industrial process, IIoT platforms lie before the stage of turning insight into action. That stage belongs to the MES. The line is not clear cut though.
IIoT platforms manage edge device connectivity, collect data, validate data, and can also perform analytics on data to a degree, sometimes employing AI and machine learning techniques. The MES can overlap, or integrate with an IIoT platform to provide the additional layer of orchestrating multiple disparate systems into one unified and optimized factory floor.
An end-to-end IIoT platform is capable of the following:
App development support for custom applications
Data collection and aggregation capabilities of edge devices
IIoT data modeling, advanced analytics, data visualization
IIoT device management
Industrial security standards
Machine-to-machine communication (M2M)
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