An Internet of Things (IoT) platform is used to unify the monitoring and management of IoT endpoints within a business unit. Applications can be developed on top of the platform adding features as needed. IoT platforms can come in the form of on-premise software packages, or as cloud services. These applications benefit organizations by streamlining operations, lowering costs, and accelerating production.
By extension, an Industrial IoT (IIoT) platform aggregates the real-time data from industrial sensors, machines, and device endpoints within a factory under a unified system of control and management. These systems are designed with the capacity to manage thousands of devices while providing data-driven analytical insights about performance.
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.
IIoT platforms are responsible for managing devices, collecting and managing data, integrating with complimentary systems, performing advanced analytics, and keeping systems secure. To fulfill these responsibilities, IIoT platforms have 7 main components:
Device Management — Industrial settings can have numerous IoT devices, sometimes numbering in the millions. In order to streamline such swarms of connected machines, an IIoT platform is equipped with device management features that allow the creation, configuration, management and maintenance of IoT devices.
Application Enablement & Management — Platforms offer more than administrative features, they also, as the name implies, provide a springboard for custom application development. App development capabilities allows organizations to optimize operations and reduce errors, but also develop novel apps to meet unknown challenges.
Digital Twins — Digital twins are virtual models of physical systems, used for simulated predictions that help to improve operations. In IIoT, for example, a digital twin of the factory can be used to test new hardware before it is integrated with the production system. By connecting the new device to the digital twin, teams can analyze and anticipate how that introduction will impact the whole system.
Integrations — IIoT platforms almost universally promote hardware and software integrations, it is a vital aspect of these software packages. To be sure, they should be selling agnostic end-to-end integrations and APIs as a component of their platform.
Security & Compliance — Data security and compliance capabilities are key elements in IIoT platforms. IIoT enabled manufactures take on a much wider threat surface than traditional factories due to numerous network nodes.
Data Management — Data management somewhat overlaps security and compliance, and deals with managing the massive volumes of data generated in IIoT systems. These responsibilities include the ingestion, persistence, organization and governance of data.
Advanced Analytics — IIoT platforms earn their value through advanced analytics engines that turn data into valuable and actionable insight. This provides operations the ability to make data driven decisions, and support automated management.
Organizations deploy IIoT platforms when they are looking to gain more insight into factors that affect production throughput, factory performance, resource utilization, and quality assurance. Data aggregated from sensors and devices throughout the factory can help present a fully transparent view into all aspects of operations. Ultimately, IIoT platforms are decision-making applications used to automate industrial environments through connectivity, data analysis, forecasting, and controls.
IIoT platforms have become foundational in successfully implementing large-scale industrial IoT deployments. The best-in-class IIoT platforms deliver many benefits:
Reduces Costs — Using software to centralize the management of large numbers of devices saves time, and eventually costs. Furthermore, automations emancipates time for IT staff by assuming repetitive and mundane tasks.
Improves Operational Performance — Real-time monitoring of both equipment and people helps to identify bottlenecks and streamline business processes and workflows. These efficient workflows can then be further integrated with upstream and downstream supply chain actors, supporting coordinated supply chains for even greater efficiencies.
Improves Productivity Throughput — The insights from platform analytics, AI, digital twins, and other innovative approaches helps to improve productivity throughput by better understanding product production and use lifecycle. Usage data from products in the field can supply a whole new layer of behavior insights that can contribute to feature and production improvements.
Improved IoT Security — IIoT platforms provide umbrella security for the thousands of devices with weak enterprise-strength security. IIoT uses identity management, secure authentication and authorization, and endpoint hardening to protect against cyberthreats.
Leverage IoT Data — Data generation is one characteristic of IoT systems that organizations are leveraging into better lifecycle management. Data can help to map new services to each stage of the product life and usage, finding new value offers and revenue streams.
IIoT platforms can be built from scratch, purchased as a package, or a service in the cloud. When building your own platform, three levels must be considered, infrastructure, platform and applications. Because handling all three levels in-house comes at considerable cost, but maximum control and customizability, organizations will often only take responsibility for one or two of these levels. For the other levels, an IoT technology provider, like AWS, Google, or Microsoft, offer platform and other IoT services.
Unless there is concern for proprietary configurations, there are platform providers that offer ready to build upon frameworks so that IIoT operations can be set up quickly. Below are 4 common platforms that allow users to easily add marketplace apps, as well as build to spec.
End-to-end Platforms — Also known as application enablement IoT platforms, end-to-end platforms provide the core components for product development, including data analysis and management. These platforms are made for rapid development, typically within a specific domain, for example, industrial platforms target manufacturing and heavy industry, while consumer platforms are geared for smaller projects.
Cloud Platforms — Similar to End-to-end platforms, in that they provide the basic building blocks and functionality to rapidly set up and manage an IoT network, cloud platforms provide the important cloud advantage of accessible scalability. Start small, and grow without bounds.
Connectivity Platforms — Connectivity platforms act as communication backbones for IIoT spaces by connecting all the devices together and potentially to the internet. They provide users with the software, connectivity, and data management, including the ability to administer these resources in real-time.
Analytics Platform — IIoT products deliver data to analytics engines to be turned into actionable insight. While most platforms include analysis tools, there are many that specialized analytics platforms provide that go beyond basic analysis, like advanced analytics visualizations and data processing, digital twins, AI and machine learning.
Selecting the right IIoT platform begins with understanding the end industrial application. To support that end application, below are several factors to consider based on requirements. At a bare minimum, IIoT platforms should provide:
Security & Compliance
User Support & Access
More specifically, consider these factors:
Connectivity methods — How will equipment connect to the network, cable, WiFi, cellular, other methods?
Geographic coverage and performance — Can the provider give secondary location options to improve latency and performance, or support disaster recovery?
Hardware & edge intelligence — IIoT can extend beyond the core network, to the edge where devices are working but distant from compute resources.
Integrations & API access — If a platform is closed, organizations will not be able to integrate their systems with others, curtailing upstream and downstream integrations. Ensure that platforms support integrations and APIs.
IoT platform — Some IoT platforms are better suited for specific situations. What are providers offering in terms of data analytics, storage, connectivity, cloud features? Do they align with requirements?
Platform lifetime — Simply, is the provider reputable, has the vendor been in business long enough to demonstrate its effectiveness?
OTA firmware update — The ability to apply firmware updates over the air (OTA) can save hundreds of hours of valuable staff time. What process is there for updated devices?
Pricing models — Does the pricing model scale with usage?
Scalability & flexibility — Think about how many new devices will be added in the future. Will data storage and bandwidth be able to support this?
Cloud migration services are good for easily overcoming the data migration learning curve. However, there are cloud migrations tools if IT teams find themselves in a situation where they plan on migrating without third-party support. Assessment tools help teams evaluate current IT infrastructure in preparation for cloud migration, and cloud migration software assists in relocating files.
Cloud Migration Assessment Tools — Migrations are complex processes where assessment tools can help evaluate the viability of such a move. Assessment tools analyze company IT resources and infrastructure to produce readiness reports highlighting cost benefits, risk assessments, and security before migrating to the cloud. For enterprises, sophisticated analytics and visualizations can help in determining areas of less or more readiness.
Cloud Migration Software — After completing a cloud migration feasibility and readiness plan, companies can use cloud migration software to backup, encrypt, and document the company’s data ready for migration. Many leading cloud providers have cloud migration software packages that integrate with their entire cloud suite making it exceptionally convenient to migrate volumes of calendars, contacts, data, documents, and email to a cloud provider. Without these tools, significant time would be spent conducting a cloud migration.
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