Summary: CIOs in industrial companies need to be careful about bottom-line investigations of the Internet of Things (IoT) platform. Adoption rates are growing, but the customer base for implementing complex IT/OT integration is small, with a focus on narrowly defined results. It should focus on integration and data and device management to ensure that the platform meets the increasing demand.
Strategic planning Assumption: By 2020, the local IoT platform that combines edge computing will account for as much as 60% of industrial IIoT (IoT) analytics technologies, which is now less than 10%.
By the end of 2022, the lack of excellent platforms in the market will prompt 15% of manufacturers to develop or collect shopping networking platforms, and today this proportion is less than 1%.
Market Definition/Description: Gartner defines the Industrial Internet of Things (IIoT) platform market as a series of integrated software functions. These capabilities include the ability to improve asset management decisions and the ability to improve the visibility and control of operations for plants, warehouses, infrastructure, and equipment in asset-intensive industries. These initiatives also appear in the relevant operating environments of those industries. The IIoT platform may be used as a technology suite, an open universal application platform, or a combination of both. The platform is designed to support the security and mission-critical requirements related to industrial assets and their operating environment. The IIoT platform software runs on controllers, routers, access points, gateways, and edge computing systems and is considered part of a distributed IIoT platform.
The IIoT platform includes the following technical features:
Device Management - This feature includes software that supports both manual and automated tasks to create, configure, troubleshoot, and manage large numbers of IoT devices and gateways in batches or individually and securely.
Integration - This functionality includes software, tools, and technologies such as communication protocols, APIs, and application adapters. This capability allows end-to-end IIoT solutions to meet the requirements for data, processes, enterprise applications, and IIoT ecosystem integration across cloud and local environments. These IIoT solutions include: IIoT devices (such as communication modules and controllers), IIoT gateways, IIoT edge, and IIoT platforms.
Data Management - This feature includes support for the following features:
Ingesting IoT Endpoint and Edge Device Data
Store data from the edge to the enterprise platform
Provides data accessibility (accessible by equipment, IT, operation and maintenance technology [OT] systems and external parties when required)
Track data lineage and data flow
Implement data and analysis governance strategies to ensure data quality, security, privacy and timeliness
Analysis - This feature includes processing data flows, such as device data, enterprise data, and context data, to gain insight into asset status by monitoring usage, providing indicators, tracking patterns, and optimizing asset usage. Many techniques can be applied such as rule engine, event flow processing, data visualization, and machine learning.
Application Support and Management - This feature includes software that enables business applications using any deployment model to analyze data and execute IoT related business functions. The core software components manage the operating system, standard input and output, or file system to support other software components of the platform. Application platforms (such as Application Platform as a Service [aPaaS]) include support for application infrastructure components, application development, runtime environment management, and digital hygiene. The platform allows users to achieve "cloud-scale" scalability and reliability, and quickly and seamlessly deploy and deliver IoT solutions.
Security - This functionality includes software, tools, and practices that facilitate review and compliance, and it also facilitates the development and implementation of preventative, detective, and corrective controls to ensure data privacy and security throughout the IIoT solution. .
The IIoT platform differs from the traditional OT used in industrial environments because it can:
Collect more high-speed, complex machine data more cost-effectively from networked IoT terminals.
Consolidate and coordinate previously isolated data sources for industrial assets in industrial environments (such as historical archives and enterprise asset management [EAM]). This can increase the accessibility of data for use within and between companies.
Through specialized analysis of centralized data, insight and mobility are enhanced across heterogeneous asset portfolios.
Improved application support and data visualization for legacy systems.
The IIoT platform monitors IoT terminals and event streams and supports and/or converts numerous manufacturers and industry-specific protocols. The IIoT platform also analyzes data on the edge of the IoT (near assets) and cloud and data centers. The IIoT platform also integrates and mobilizes IT systems and OT systems when sharing and using data. It also supports application development and deployment. The IIoT platform is increasingly used to enrich and complement OT capabilities to improve asset management lifecycle strategies and processes. In some emerging applications, the IIoT platform does not require some OT functionality.
The IIoT platform combines the IoT edge and leverages enterprise IT and OT integration to prepare the asset-intensive industry to become a digital enterprise. If necessary, this transformation can be achieved if data availability and access rights are improved for production and business stakeholders as well as external business partners and customers.
Horizontal and vertical business applications are not in this Magic Quadrant. Examples include:
Enterprise Asset Management (EAM)/Computerized Maintenance Management System (CMMS)
Fleet management
Manufacturing Execution System (MES)
Maintenance, Repair and Operation (MRO)
Product Lifecycle Management (PLM)
Asset Performance Management (APM)/Condition Based Maintenance (CBM)
Field Service Management (FSM)
Building Management System [BMS]
However, platform providers must demonstrate proven value in integration and interoperability with such applications.
Targeted industrial companies
For this market assessment, Gartner focuses on three asset-intensive industries:
The manufacturing and natural resources industries include sub-sectors such as automotive, consumer non-durable products, energy and processing, heavy industry, IT hardware, life sciences and medical products, and natural resources and materials.
Transportation industry, including air transportation, automobile transportation, oil and gas pipelines, railway and water transportation, warehousing, express delivery and support services and other sub-sectors.
The utility industry includes sub-sectors such as electricity, natural gas, and water supply.
Differentiated IIoT platform
The difference between industrial IoT and general IoT is that industrial IoT technologies are specifically used in asset-intensive industries and related environments (usually regulated). IIoT's integration, scalability and impact cover IT and OT systems. The IIoT solution collects, summarizes, coordinates, and analyzes data to:
Promote asset management decisions
Improve operational visibility and reduce the automation and control costs of assets, infrastructure and equipment
Some features of the IIoT platform include the following:
The IIoT platform must be expandable through the integration of OT and enterprise IT applications. Integration must be safe and reliable.
Reliability and resiliency are the cornerstones of most IIoT solutions, primarily because they also involve regulated security factors. Reliability and resiliency include monitoring and managing key equipment and services that require 100% availability. Therefore, the IIoT solution usually focuses on fault identification and fault recovery capabilities. These factors have increased the challenges of the architecture.
Deployment requirements in the IIoT are complex and often regulated. This situation has led to major integration challenges to ensure life safety, the mission-critical nature of the system, and data security and privacy. Major enterprise applications (such as MES, ERP, APM/CBM, and EAM/CMMS) drive solutions and IoT services run in cloud, on-premises, or hybrid environments. Today, IIoT must be able to meet both local deployment and cloud deployment needs.
Due to the entrusted services from cloud and Internet of Things terminal devices, IIoT has requirements for edge computing. Many sensors of these terminal devices generate large amounts of data and are often generated at high speed. Edge computing includes edge platforms and edge gateways that operate primarily locally. Internet of Things and OT devices with a large number of different protocols (standard and proprietary) are connected through powerful computing gateways and edge platforms. IIoT is primarily a five-tier architecture model: devices, gateways, edge computing, platforms, and enterprise application integration.
It is worth mentioning that, in enterprise application software, industrial enterprises use and increasingly rely on third-party data services. These services may include data that is critical to operations and production planning, such as weather, current prices for bulk goods/goods/services, customized demand, forward and reverse logistics, and other considerations in the supply chain.
The IIoT solution has fewer endpoints (thousands or tens of thousands) compared to consumer-centric commercial IoT solutions. The amount of data generated by the endpoints, as well as the frequency and speed of the data, can be very high. Sensors often transmit data every few milliseconds. The IIoT solution is characterized by a small number of devices but a large amount of data.
The data generated by the IIoT sensor is often critical to the operation of the terminal equipment and also helps to enhance the safety of the environment. Therefore, the processing and analysis at the edge of the IIoT solution is more important to solve security problems. It is also important to emphasize uptime through complex, segmented network designs and minimize data loss. The data also greatly contributes to the goals of efficiency and availability, resulting in significant cost savings.
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