We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before. We do not yet know just how it will unfold, but one thing is clear: the response to it must be integrated and comprehensive, involving all stakeholders of the global polity, from the public and private sectors to academia and civil society, writes Klaus Schwab Founder and Executive Chairman, World Economic Forum.
This form of manufacturing, called Industry 4.0 is a collection of technologies and concepts for defining and operating ‘Smart Factories’, where the machinery of manufacturing – machine tools, the sensors monitoring them and such like – can communicate with each other, with the systems overseeing the factory and the people who work in it to fine-tune the manufacturing process and enable such things as product customisation, while increasing productivity and flexibility. These intelligent and connected machines don’t only work; they take decisions and optimize processes intelligently and semi-autonomously.
According to the new market research report published by MarketsandMarkets™, the Smart Manufacturing Market is expected to be worth USD 214.7 billion in 2020 and USD 384.8 billion by 2025, growing at a CAGR of 12.4% from 2020 to 2025. Factors that drive the growth of the market include the growing adoption of Industry 4.0, rising emphasis on industrial automation in manufacturing processes, increasing government involvement in supporting industrial automation, rising emphasis on regulatory compliances, increasing complexities in the supply chain, and surging demand for software systems that reduce time and cost.
Major Enabling Technologies of Industry 4.0
Technology has always been the underlying factor behind previous industrial revolutions. Similarly, technology still remain as a critical factor for Industry 4.0 emerging technologies such as cloud computing, automation, Artificial Intelligence (AI), and IoT are forming an interconnected industrial landscape where physical assets and equipment are integrated with systems to enable contents and dynamic exchange and data analysis. The Industrial Internet of Things (IIoT) architecture is made of numerous elements from sensors, connectivity and gateways to device management and application platforms.
For companies to achieve their Industry 4.0 objectives, automation, ubiquitous connectivity and intelligent systems are necessary. The advent of low power processors, disruptive capabilities of the IoT, intelligent wireless networks and low power sensors, when combined with ‘Big Data’ analytics, has led to a booming interest in the Industrial IoT.
IIoT is expected to hold the largest share of the smart manufacturing market for enabling technology. Various technologies are using IIoT to improve the functioning of the process. These technologies comprise of sensors, RFID, industrial robotics, distributed control system, condition monitoring, smart meter, electronic shelf label, camera, smart beacon, interface board, yield monitor, guidance and steering, GPS/GNSS, flow and application control device, and networking technology. Use of IIoT in these technologies helps to analyze the data collected via various devices and enables effective decision making.
Industrial communications is expected to hold the largest share of the smart manufacturing market for information technology. Industrial communications is a combination of components, software, and standard protocols that allows man-to-machine and machine-to-machine communication across various industries. Efficient, reliable, and secure industrial communications help in improving operational efficiency and reducing overall operational costs of organizations. Industrial communications plays a significant role in industries such as oil & gas, electronics, automotive, and energy & power.
The adoption of 5G will revolutionize connectivity for IoT-enabled industries. This nascent connection technology is tailored for IoT’s connectivity needs and is expected to catalyze its productivity. Long-range low-power wide-area network (LPWAN) technologies like NB-IoT, LTE-M, LoRa and Sigfox are also driving innovation for IIoT connectivity. LPWANs for IoT sensors allow low powered devices to stream packets of data wirelessly but with a wider area.
An average factory operates with legacy industrial systems that are nowhere near being connected. While these systems employ a number of proprietary communication protocols for automation purposes, data is captive within discrete control loops, creating numerous data silos on the factory floor. The lack of interoperability among these protocols further hinders the implementation of a factory-wide monitoring and control network.
Emerging retrofit wireless connectivity now enables manufacturers to connect and acquire data from their legacy assets and systems in a simple and cost-effective manner – without costly production downtime and invasive hardware changes. Through the use of an integration platform, operational data can be fetched from controllers through wired-based serial and other industrial protocols then forwarded to a remote control center using long-range wireless connectivity.
IEEE is also developing a new Wi-Fi standard, called 802.11ax, which uses both 2.4Ghz and 5.0Ghz wireless frequencies. This gives IoT devices better access points with boosted capacity and bandwidth speeds as well as improved energy efficiency over previous Wi-Fi standards.
As no wireless solution is use-case agnostic, a typical IIoT architecture is likely to incorporate multiple radio protocols and standards.SDR refers to a radio communication method where the majority of signal processing is done using software, as opposed to the traditional hardware-driven approach. IoT gateways leveraging SDR can incorporate and decode different protocols concurrently to reduce infrastructure cost and complexity. What’s more, adjustments or additions of new wireless solutions to the architecture can be achieved with simple software updates. This allows companies to dynamically adapt to future operational and technological changes while continuing to support legacy wireless devices in the field.
Big Data, AI and Analytics
Companies collect data to improve their operational processes. Big Data and Analytics is the collection of data comprising equipment and
systems and customer managements system help assist companies to identify trends, patterns and relationships between inputs, processes and outputs, enabling real-time decision making.
Another emerging AI trend is leveraging deep learning and computer vision in AI visual inspection systems for detection and quality control.
Made popular via the gaming community such as Pokemon Go, augmented reality allows augmented imagery to be placed in front of the real-world. This presents businesses the opportunities to showcase their products to the market without having to bear the expense of creating a physical copy.
Additive manufacturing such as 3D design printing is highly useful especially in the making of prototype and production of individual
components. This technology enables manufacturers to focus on producing small batches of customised products which offer construction advantages such as complex, lightweight designs.
Industrial 3D Printing
Industrial 3D printing is used in various applications such as tooling, robotics, and special machinery. Robotics forms an important part of industries such as automotive, aerospace & defense, food & beverages, printed electronics, and foundry and forging. Combining industrial 3D printing with robotics allows creating well-designed, lightweight, and less expensive components. Industrial 3D printing simplifies the expensive and time-consuming process of manufacturing tools, eliminating assembly lines and thereby, reducing labor costs as well. Industrial 3D printing is also used for developing special machinery such as heavy equipment and machinery components; it also allows for customizations according to customer needs. The special machinery also includes high-quality metal and plastic parts of highly complex designs.
The use of robotics arms gifted to us in the Third Industrial Revolution has enabled the industry to leapfrog. Industry 4.0 will see the further rise of autonomous system namely robotics working side by side with humans (Collaborative Robot – COBOT) but with a greater range of capabilities all helping to contribute to a company’s competitiveness, productivity and profitability.
Collaborative robots, or co-bots, are robots that are designed to work alongside humans in a workspace to provide enhanced process efficiency. These robots are different from industrial robots in a number of ways, such as the absence of “safety fence” while working alongside humans, simplified programming and reduced setup time, integration of auto-speed reduction and distance monitoring via proximity sensors, and ability to reduce motor power and force during application to avoid harm to a human coworker.
Collaborative robots are used to perform autonomous or semiautonomous tasks for a variety of applications such as assembly, pick and place, handling, packaging and palletizing, quality testing, machine tending, gluing and welding, lab analysis, painting and polishing, screw driving, and injection molding.
Cloud Computing involves a network of remote servers to store, manage and process data which helps especially in production-related sharing across sites and company boundaries. This is advantageous for businesses as it permits the company to bring forward their savviness in all sales situations. Depending on criteria like security, reliability, data ownership and costs, companies need to choose among an on-premise, public or private cloud deployment, or even a hybrid approach. As the IIoT use cases and architecture scale, the decision on the deployment model and/or cloud vendor is subject to change as well.
In this context, an IIoT platform, typically a device management platform, that comes with a portable, container-based design renders industrial users with full flexibility in selecting their preferred backend environment. At the same time, it enables a simple migration to another server as needed without compromising the consistency or functionality of the application. The idea of a container-based design is that individual applications are packaged and delivered within discrete, standardized containers called Docker. With this modular architecture, users can decide which specific platform functions/ applications they want to use and where to deploy them. Thanks to its flexibility and portability, the container-based design facilitates an interoperable and future-proof IIoT architecture that keeps up with the industry’s dynamic needs.
IIoT companies are now shifting implementation models towards edge computing. This technology allows data to be processed near the IoT devices, which reduces latency and the use of bandwidth. Edge computing enables the viability of everything-as-a-service business models and microservices—both of which rely on lightning speed computing capabilities and responsiveness.
The OpenFog Consortium which includes CISCO is committed to push fog computing to overcome the current challenges in edge computing—device management, scalability, and cybersecurity. As opposed to edge, fog computing can still process data in conditions where bandwidth is unavailable. This means manufacturing devices connected through the fog can process data locally while transmitting only pertinent data with very small power consumption. Collision warning technology is also very compatible to fog computing. Vehicles with fog computing devices can also communicate with each other more efficiently and faster. However, IEEE senior member and professor of cybersecurity at Ulster University Kevin Curran notes that fog computing introduces more cybersecurity risks to devices and virtual machines. Addressing these threats will be crucial for wider scale adoption.
Machine Condition Monitoring, Predictive maintenance
Machine condition monitoring is the process of determining operational state and condition of a machine for detecting potential breakdowns with the help of automation. The process comprises periodical or continuous data collection, analyses, interpretation, and diagnoses. This approach is different from traditional methods wherein processes are manual.
Machine condition monitoring optimizes equipment readiness and reduce maintenance and staffing requirements. It is used to prevent unscheduled outages, reduce downtime and maintenance cost, and optimize machine performance. This technique is primarily classified into preventive machine monitoring and predictive machine monitoring. Predictive monitoring allows companies to detect potential trouble, diagnose problems, and choose remedial actions before performance degrades or downtime occurs. Preventive monitoring, on the other hand, is performed while an equipment is in normal condition to avoid unexpected breakdowns and the associated downtime and costs. Deploying Machine Learning-based predictive maintenance capabilities can reduce downtime by 20-50% and costs by 5-10%.
Vast range of industries are applying simulation into their operational processes, enabling operators to test and optimise machines and systems. This is especially relevant for those working within a dangerous physical environment because it allows them to test the processes before they embark into the real situation. This would help in avoiding and improving machine downtime as well as increasing overall product and work quality.
Hardware Rapid Prototyping
In the industrial world, the challenge of IoT hardware design lies in the bewildering array of use case requirements. Hardware prototyping standards like mikroBUS allow you to build a customized IoT device prototype in a matter of a few hours and with efficient resources. From a broad portfolio of ready-to-use, compatible sensor, interface and wireless modules as well as compilers and development boards, you can create the optimal hardware mix-and-match that caters to your industrial use case. With rapid prototyping, companies can ratify the technical and business viability of their IIoT solution in a cost-effective and agile fashion, which lays the cornerstone for a successful roll-out.
Digital Trust Is The Key
The digitalisation aspect of Industry 4.0 can be compared to a huge wave sweeping the Earth. Adopting Industry 4.0 means businesses can expect massively growing information flow which requires the right analytics technique and infrastructure to support it. However, with
the massive data flow from various point of entry, businesses must take a rigorous, pro-active approach to data security and related issues to
work on building digital trust.
Digital trust addresses three major challenges of the digital era – cybersecurity, which involves making sure that data transferred across the
network cannot be hacked; transparency, which means making clear how data is processed, sent and stored; and personal data protection; so that sensitive information such as bank account details and personal records stay out of malevolent hands. Businesses must work with the relevant expertise in the industry. They need to develop digital competencies to overcome these challenges, and they also need to create the digital trust necessary to support data analytics that plays a major role in creating value to the customers. This, in turn will give businesses the competitive edge needed to thrive in changing business world.
When a company is able to apply technology alongside effective, well-designed processes, only then it can maximise value. The use of technology to digitise a poorly-designed process will only result in a poorly-designed digital process. Conversely, applying technology to a well-developed process will enhance its efficiency and enable the creation of new value. Previously, companies centred their efforts on improving the efficiency of individual processes but under Industry 4.0, the concept of process improvement has expanded to focus on the integration of processes within a company’s operation, supply chain and product lifecycle. As the processes within operations, supply chain
and product lifecycle become integrated, they will converge into a single unified system where data is shared, processed and integrated across the product management, production and enterprise layers of the organisation. These will then generate the next leap forward in flexibility and efficiency.
Despite technology becoming an increasingly important tool of trade in Industry 4.0, focusing on the technological aspect alone doesn’t amount to seamless digital transformation of a business. Business leaders must be able to recognise that digital transformation goes beyond adopting advanced technologies and for a company to digitally transform successfully, it is the people that matter most.
People are the third shift factor of Industry 4.0 as it plays an equally important role alongside technology and processes. To remain relevant in the face of increasing competition, companies must adapt their organisational structures and processes to allow their workforce to keep pace. Industry 4.0 highlights two key components that can effect businesses’ effectiveness. The first is the workforce which include both employees and top management and the second is the organisational system that governs how the company function. Both components are essential in order to reap the full benefits of Industry 4.0.
For instance, an experienced leadership team and workforce will be discouraged by inflexible structures, inconsistent practices, and siloed processes. On the other hand, open channels for cooperation and innovation will not be effective unless employees are informed and incentivised to use them. As such, the necessary enhancements must be made to people, before a company can implement
Industry 4.0 strategies effectively.
According to Boston Consulting Group “Man and Machine in Industry 4.0” report, in addition to the new jobs created superseding many traditional roles, Industry 4.0 will also require employees to be better problem solvers and display greater flexibility. Companies must look at new approaches to recruit people and focus more on capabilities rather than qualifications to find workers with the relevant skills for specific roles. Companies should collaborate with government agencies to be able to develop a set of requirements to fill these newly created roles as well as revise their skill sets to work effectively within this new environment.
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