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Industrial Internet of Things, IIoT

Internet of Things is the industry's key technology.

An Industrial Internet of Things (IIoT) is the component of the Internet of Things and its main technology at this stage in the development of technology.

The Industrial Internet of Things is a system of integrated computer networks and connected industrial (manufacturing) objects with built-in sensors and software for collecting and exchanging data, with the possibility of remote control and control in an automated mode, without human engagement.

How Industrial Internet of Things works

At the first stage of IIoT implementation sensors, actuators, controllers and man-machine interfaces are being installed on industrial equipment. As a result, it is possible to collect information that allows management to receive accurate data on the state of production. Processed data is provided to all departments of the enterprise. It helps to establish interaction between employees of different departments and make informed decisions.

The information obtained can be used to prevent unscheduled downtime, equipment failures, to reduce unscheduled maintenance and failures in the supply chain, thereby allowing the company to function more efficiently.

When processing a huge array of unstructured data that come from sensors, filtering, and adequate interpretation becomes a priority task. Therefore, the importance of obtaining information in the form of a user is understandable. For this purpose, advanced analytical platforms are used to collect, store and analyze data on technological processes and events occurring in real-time.

The Industrial Internet of Things allows you to create a production that is cost-effective, flexible, and more efficient. Wireless devices that support IP protocol, including smartphones, tablets, and sensors, are already actively used in production. Available wire networks of sensors in the coming years will be expanded and supplemented by wireless networks, which will significantly expand the areas of application of monitoring and control systems at enterprises. The next stage of production processes optimization will be characterized by increasingly dense convergence of the best information and operating technologies.

As the digital ecosystem becomes established and mature, industrial enterprises from isolated systems that independently carry out all production and business processes will be transformed into open systems that unite different market participants. Cloud services will manage production means in these systems. The ultimate goal of all these transformations — not to produce products, but to provide services to the consumer.

Examples of IIoT implementation

It is believed that IIoT solutions can increase the efficiency of production several times, and the payback period of such projects in most cases does not exceed several months.

For example, the equipment of the Philips Shaving Plant (The Netherlands) works in an unlit building where 128 robots are installed. The entire staff of the plant consists of nine employees.

A striking example of the use of the Industrial Internet of Things is the project of Harley Davidson, which produces famous motorcycles. The main problem faced by the company was a slow response to consumer inquiries with growing competition and limited ability to customize dealers of the five models. In the period from 2009 to 2011, the company carried out a large-scale reconstruction of its production sites. As a result, a single assembly platform was created, producing motorcycles of all five models customizable, with the customer being offered a choice of more than 1300 variants.

During the whole production process, used sensors controlled by the MES system. Each machine tool, each part has a tag, which uniquely identifies the product and its production cycle. Sensor data is transmitted to the data processing platform, which acts as an integration bus for collecting data from sensors and various information systems, both Harley Davidson's internal production and business systems, as well as company counterparts information systems.

As a result, Harley Davidson has achieved very impressive results:

  • the production cycle was reduced from 21 days to 6 hours (every 89 seconds a fully customized motorcycle comes off the assembly line);
  • implemented end-to-end product control throughout its life cycle;
  • the value of the company's shares increased by more than 7 times: from 10 dollars in 2009 to 70 dollars in 2015.


Economic Perspectives

In 2015, Accenture conducted an extensive study "Winning with the Industrial Internet of Things". As part of this study, a survey was conducted of 1,400 senior executives in many countries of the world (of whom 736 were heads of companies). A report released on the results of the study states that the contribution of the Industrial Internet of Things to world production by 2030 could be about $14.2 trillion. But this potential increase is under threat, since neither the company nor the state has yet taken sufficient efforts to create the necessary conditions for the wide distribution of new digital technologies.

As indicated in the report, by 2030, the results of the IIoT implementation could be as follows.

  • In the US, aggregate GDP could increase by $6.1 trillion. If the United States invested 50% more in IIoT technologies and improved the factors affecting this area (such as the development of professional skills, the spread of broadband networks, etc.), by 2030 the increase could have reached $7.1 trillion, thus increasing the country's GDP by 2.3% compared with baseline projections;
  • Germany, taking similar additional measures, could increase total GDP by $700 billion, or 1.7%;
  • UK, compared with baseline projections, could increase total GDP by $531 million, or 1.8%;
  • For China, the economic benefits of IIoT are expected to be greater than for Russia, India or Brazil. Thanks to measures to support and develop the Industrial Internet of Things, China could increase its total GDP by $1.8 trillion by 2030, increasing it by 1.3% compared with initial forecasts.

However, according to Accenture, 73% of the companies surveyed do not yet have specific plans for using the Industrial Internet of Things. Only 7% of respondents have developed a holistic strategy for its implementation and provided for the appropriate investment.

Trends and technologies

Craig Resnick, a leading analyst at ARC Advisory Group, a well-known research firm, believes that there were six major trends in the development of the Industrial Internet of Things at the beginning of 2017. A brief overview is given below.

  • The main components of IIoT are advanced analytical tools, artificial intelligence, and machine learning.
    Many enterprises have used business intelligence (BI) platforms and intelligent manufacturing tools. Now, thanks to IIoT, manufacturers can use advanced analytic tools, artificial intelligence, and machine learning for operational management with anticipation and decision-making based on in-depth analytics. Thanks to this, the Industrial Internet of Things is becoming a strategic tool aimed at improving production performance.
  • More and more smart devices appear "on the border"
    Moving analytics to the "cutting edge" of the network and thus closer to data sources will help improve the quality of production and products. With the advent of low-cost sensors and processors, it is possible to collect and process more and more data on production at the frontier. Boundary (foggy) calculations with embedded analytics are becoming an acceptable alternative in cases where it is not safe to run analytics in the cloud or have refused a cloud solution for some reason.
  • The appearance of digital twins
    Thanks to the introduction of IIoT technologies, it becomes possible to create a digital copy of a physical object, which is sometimes called the "digital twin". This copy is used to simulate, test, and optimize a given physical object in a virtual environment before using it in a real environment. Similarly, real-time data from sensors that are integrated into physical objects or from other sources can be used to solve analytical problems, such as condition monitoring, failure diagnostics, and predictive analytics. The resulting knowledge can increase the value of the productive assets of an enterprise by: 
    • increase the efficiency of their use;
    • reduced downtime;
    • failure prevention;
    • ensuring continuous product improvements in the design and production process.
  • IIoT helps develop augmented and virtual reality technology (AR/VR)
    Training new staff with simulators can be an effective way to learn. The technologies used in IIoT, such as games, augmented/virtual reality and 3D immersion using wearable devices, can with a high degree of accuracy simulate the real situation in the enterprise, the functions of workers, controls, and physical objects

  • MQTT as the main messaging protocol in IIoT
    MQTT (Message Queue Telemetry Transport) is a simplified data exchange protocol that runs on top of TCP/IP. It is well suited for use in controllers and sensors where small code size is required and there are restrictions on the channel capacity. This situation is typical for IIoT, so MQTT is considered as the main protocol of the Industrial Internet of Things.

  • Cybersecurity first
    The article "Internet of Things" said that one of the most serious problems facing the developers of smart things, homes, and cities is security. For the development of IIoT, the problem of ensuring an adequate level of cybersecurity remains, perhaps, the only significant obstacle. Improved technologies and approaches to cybersecurity, such as end-device certification according to ISA SecureDevice and Wurldtech’s Achilles Communication Certification Achilles standards, are designed to help overcome it.

But the main hopes are for the IEC 62433 standard currently being developed. It will provide a very extensive set of requirements in the field of industrial IT security, including standards for organizations, control systems, components, implementation, and support processes.

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