If manufacturers can remove barriers and governments can set standards, the rewards will be huge.

Digitalization offers a wide range of advantages, including predictive maintenance to reduce downtime by creating digital twins, enhanced quality control, demand driven production, inventory optimization, reduced energy and material costs, and improved safety and environmental performance.

Many forecasts attempt to quantify value propositions. McKinsey, a consultancy, said the economic impact of the Internet of things could be between $1.2 and $3.7 trillion by 2025. A recent U.S. Department of Commerce survey of U.S. manufacturers and smart manufacturers found that costs fell by $57 billion a year.

Of course, there is one problem. There are actually several. The manufacturing industry has a long investment cycle. Powerful processes and equipment will not appear overnight. It is essential that the required technologies, such as artificial intelligence, have not been fully developed.

Artificial intelligence (AI) as catalyst

Smart factories take advantage of the industrial Internet of things (iiot), big data and advanced analytics, as well as the integration of information technology (it) and operational technology (OT). In addition, devices that communicate with each other lead to real-time decision making, which optimizes value creation.

What factors hindered the revolution of intelligent factory

It occurs not only in the factory, but also in the whole value chain, from raw material purchase to order delivery and customer service.

The potential catalyst for this shift is artificial intelligence (AI). At present, most of the interest of AI is related to machine learning, which is a set of technology that combines real-world data and experience with statistical analysis to draw conclusions and predict results.

Machine learning is not a new field of artificial intelligence, but the development of the Internet, the proliferation of a large number of data and the continuous improvement of computer processing ability greatly improve the depth, breadth and accuracy of its prediction ability.

Although artificial intelligence is obviously improving, it also has its limitations. The design of the underlying algorithm is very difficult, which may lead to loopholes and unexpected deviations; the training steps usually need a lot of data and practical experience which may be difficult to obtain; the neural network usually needs a long time to train. When the decision to enable artificial intelligence (AI) goes wrong, it is usually difficult to determine the cause, which is a major problem in safety critical systems.

Why is artificial intelligence now used in factory environments? Of course, technology is a driver: the availability of large amounts of data, the development of machine learning, the emergence of cloud computing (for monitoring and optimization of network scope) and edge computing (to provide machine learning for real-time decision-making), and the combination of information technology (it) systems and operational technology (OT) systems.

But current social trends are also important, including the increasing complexity of global supply chains and the continuing challenges in attracting skilled production workers. In other words, the emergence of smart factories is the result of technology promotion and market pull.

If all the AI problems are solved – and eventually will be solved. However, without the best information governance, smart factories will not develop rapidly.

Three such governance issues include technical standards, network security / privacy and spectrum allocation.

technical standard

Smart factories rely on information flow and system responsiveness, which cannot be achieved without standards, basically specifications or requirements related to technical systems.

Hundreds or even thousands of standards are used in the manufacturing process, and many new standards are needed to realize the intelligent factory. According to a February 2016 report from the National Institute of standards and Technology (NIST), the intelligent manufacturing ecosystem can be regarded as a pyramid composed of four levels of progress: equipment level, supervisory control and data acquisition (SCADA) level, manufacturing operation management (MOM) level and enterprise level. Information must flow within and between each level, and dozens of standards have been or are being developed to accelerate this collaboration.

According to NIST, “within the manufacturing pyramid, communication standards have been established, but interoperability between systems is limited, which means manufacturers are often locked into a single vendor solution. Throughout the business cycle, there are several sound standards, but the extent to which information can be connected to production systems is very limited. “

In addition to developing standards to fill these gaps, the report points to two other standards related barriers for smart factories:

(1) Lack of tracking of standards and their adoption;

(2) Overlap and redundancy between standards.

In order to remove these obstacles, coordination and cooperation among various organizations are necessary, some of which are in progress.

Standards are also being developed to promote the application of blockchain technology. Blockchain is a digital ledger that can record transactions in a verifiable and secure way. The U.S. Department of Homeland Security (DHS) is piloting blockchain with industry to see if the technology can stop counterfeiting and intellectual property theft. Security and defined interoperability standards will be required to facilitate the application of the technology.

Cybersecurity / privacy

Smart factories need to interconnect devices and devices within the factory and throughout the value chain. This connection increases the risk of cyber attacks, espionage and data theft for manufacturers.

These are not hypothetical issues, such as the destruction of a German steel plant in 2014 when hackers gained access through phishing emails. A recent survey in the UK found that 50% of manufacturers admitted to being hacked, and half of the manufacturers under attack suffered as a result. According to the Department of homeland security, manufacturers are the primary target of cyber attacks on critical infrastructure.

In view of the increasing goal of intelligent factories for traditional factories, security issues become more and more important. Security objectives include maintaining production (no downtime or delays), preventing system failures that result in property or personal injury / death, preventing espionage, and protecting the privacy of customers and employees.

Achieving these goals is neither easy nor easy. In order to protect the intelligent factory, many methods and systems are needed, including the security architecture of the network physical system, the process of verifying the software integrity (the process of detecting malware or unexpected code) by proof, and the safe device management.

Providers of smart manufacturing equipment and services are clearly involved in these security developments, as is the government. In cooperation with industry, the U.S. government has developed a risk-based and voluntary network security framework for key infrastructure, which is widely applicable to a range of enterprises, including manufacturers. NIST has also released a smart city framework related to smart factories.

Another growing problem concerns the privacy of personal information. The EU’s general data protection regulation (gdpr) is a legal framework that sets guidelines for the collection and use of personal information. The new law also has implications for smart factories, for example, where technology to measure production line output may collect data from individual workers. Manufacturers need to update their privacy statements and ensure that they comply with gdpr requirements, while ensuring that they are transparent about the personal information collected using these technologies.

Finally, smart factories will drive the transformation of the insurance industry, which will face the need to build solutions to manage risk changes.

Spectrum allocation

The number of devices needed to achieve the smart factory commitment is an important consideration in information governance. These devices are expected to operate through wireless communication. There are billions of wireless devices, which are expected to grow exponentially due to the Internet of things and the industrial Internet of things (iiot).

All these requirements for wireless communication need spectrum, which is a scarce public resource. For smart factories to succeed, the government must allocate enough spectrum to meet this demand growth. In the United States, the Federal Communications Commission (FCC) allocates spectrum for consumer and commercial purposes.

Last year, the US Government Accountability Office (GAO) investigated the issue. According to the GAO report, the Federal Communications Commission believes that the spectrum currently available is sufficient to meet the growth of the Internet of things in the near future, unless devices using a large number of spectrum proliferate. “As the number of wireless devices grows, managing interference becomes more and more challenging, especially in frequencies where wireless licensing is not required,” Gao said The Gao recommends that the FCC start tracking the growth of the Internet of things to ensure that there is sufficient spectrum available.

If additional spectrum is needed to support the smart factory, is it the licensed spectrum? Spectrum not licensed or shared? The FCC will decide how to allocate the available spectrum between each type and in which frequency band.

These government decisions will affect the spectrum supply and quality of smart factories in the United States. Other countries are also trying to solve the problem of how to allocate spectrum for industrial use. According to the GAO report, each country is taking a different approach, with at least one country, South Korea, devoting its spectrum to industrial use.

Editor in charge: CT

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