How is smart manufacturing different from Industry 4.0?

Infineon / Mitsubishi / Fuji / Semikron / Eupec / IXYS

How is smart manufacturing different from Industry 4.0?

Posted Date: 2024-01-25

Author: Claire Fallon, ISA; Jeff Winter

In the process of transforming to smart manufacturing and digitalization, how can companies judge whether they are moving fast enough? Are your Industry 4.0 projects paying off?

Advances in automation technology are changing the landscape of manufacturing. Broadly known as "Industry 4.0," these smart manufacturing initiatives bring together physical and cyber systems to achieve greater performance, efficiency, sustainability, safety, and competitive advantage.

As a term, Industry 4.0 originated in 2011 – widely attributed to a strategy unveiled by the German government at that year’s Hannover Messe. Twelve years have passed and manufacturing has changed a lot. In many cases, the underlying principles of Industry 4.0 have changed and been reprioritized in line with industry needs. For example, artificial intelligence and machine learning may play a much larger role in people's daily work than we imagined in 2011.

▲Figure 1: The digital transformation framework requires changes in the way people, processes and technology use data. Key elements include updated digital infrastructure, system integration, digital skills and training, and appropriate leadership and vision.


Difficulties in digital transformation of manufacturing companies

Although the terms Industry 4.0 and digital transformation are often confused, there are some important differences between them.

Industry 4.0 is about using digital platforms, products and services to gain a competitive advantage, just as steam engines, assembly lines and industrial robots did in previous industrial revolutions. These are effective new tools, but the focus is not necessarily on changing the behavior of individuals using them.

Digital transformation is a more substantial change that affects the way business is done – it’s as much a shift in mindset as it is the adoption of new technologies. Digital transformation involves integrating digital technologies into all aspects of a business or organization, while industrial digital transformation specifically focuses on leveraging digital technologies to optimize and transform operations in the manufacturing and industrial sectors.

Both may involve the use of IoT devices, data analytics and artificial intelligence. In the context of industrial digital transformation, these technologies can be used to monitor production equipment, predict maintenance needs or optimize energy consumption in factory environments. For manufacturing companies, some interactions make industrial digital transformation more challenging than other industries:

· Manufacturing process complexity: Manufacturing involves multiple stages such as product design, raw material procurement, production assembly, quality control tools and equipment manufacturing, packaging and distribution. Each stage has its own set of processes, making it challenging to implement digital technologies in a way that effectively addresses the entire value chain.

· Integration with physical systems: Unlike the banking industry, which primarily deals with digital data and transactions, manufacturing involves converting physical materials into finished products. This requires connecting these physical assets with digital technologies, such as IoT devices, sensors and actuators. This integration may require advanced engineering solutions, unique cybersecurity measures and real-time data processing capabilities that are not available in other industries.

· Professional talent and skilled labor: Successfully implementing digital transformation in manufacturing requires a workforce versed in both traditional manufacturing processes and advanced digital technologies. The need for a “dual-skilled” workforce may create unique challenges in training and upskilling employees. The talent pool for the required skills is much lower than in other industries. Typically, manufacturing is not the first choice for people with newer skills in data analytics, AI, or application development. Not only will manufacturing companies need to focus on cultural and brand changes to attract the right talent, but they will likely have to spend more time and resources upgrading and retraining employees than other industries to fill this gap.


Three stages of implementing digital transformation

Many people may be confused about what fully implementing digital transformation really means, so it’s important to consider the different steps along the way. Claire Fallon, executive director of the International Society of Automation (ISA), believes that the digital transformation process can usually be divided into the following three stages.

Digitizing and sharing analog files across the organization is a clear first step, and many businesses are already doing this. In fact, many organizations started doing this before we even understood the concept of Industry 4.0. This is easy to implement but has relatively little impact on the overall digital transformation journey.

The intermediate stage is the digitization of existing workflows. Think of existing workflows, such as walking around a factory floor holding a clipboard to perform daily inspections. If we digitize this process, inspectors might use tablets to update data or even wear augmented reality technology like smart glasses. This makes it easier for engineers and inspectors to access documentation and record measurements, readings and observations in the form of notes, photos and videos. All of this information can be associated with a specific location, making reporting easier to process. But we still have to walk the shop floor, record data, and create reports. The job of an engineer is not much different from before.

The final step is true digital transformation, where digital tools fundamentally change or even improve the way an organization does business. For example, one oil and gas operator began relying on machine learning to identify corrosion on its offshore production platforms through image analysis. The machine learning engine uses images captured throughout the facility for engineering and operations to identify potential areas of corrosion, reducing the need for traditional manual inspections through its analysis of potential deviations. In this case, machine vision algorithms can also identify potential problems that might have been missed in large-scale manual work. When teams of inspectors and engineers no longer need to manually tour the entire facility, time and energy are freed up to manage the most effective ways to troubleshoot and resolve issues initially discovered by the machines.

▲Figure 2: According to the American Industrial Internet Alliance, the application of industrial AI needs to consider how to digitize, extract and transform data; analyze, detect and diagnose; optimize; generate; prescribe; predict and take action.


Is progress happening fast enough?

How do companies know if they are progressing fast enough with Industry 4.0? Jeff Winter, an Industry 4.0 industry expert and senior director of manufacturing strategy at Hitachi Solutions, believes that judging whether it is "fast enough" really depends on two things: 1) How does your business compare to your competitors? 2) How to achieve your Industry 4.0 goals?

For the first question, adopting a maturity model can help assess an enterprise's digital maturity and the progress of Industry 4.0 (such as Acatech's Industry 4.0 Maturity Index, INCIT's Smart Industry Readiness Index, etc.). These models allow businesses to benchmark performance against industry standards and best practices, often including an overall assessment of the business.

For the second question, companies can measure progress by tracking the key performance indicators (KPIs) of their Industry 4.0 initiatives. Common metrics include overall equipment effectiveness (OEE), quality, flexibility and even innovation. The Lighthouse Network program, run by the World Economic Forum in partnership with McKinsey, has been running for several years and aims to identify top factories that are leading the way in the application of technology and are implementing advanced manufacturing at scale and achieving significant results. The results of these projects are publicly available and serve as a realistic benchmark for improving key performance indicators.

Take, for example, the International Center for Industrial Transformation’s (INCIT) Smart Industry Readiness Index (SIRI). The Index is ideally suited to assessing progress towards Industry 4.0 as it provides a comprehensive, structured and systematic approach to assessing an organization’s digital transformation progress. The index is developed by the Singapore Economic Development Council, which partners with TÜV SUD to certify evaluators. The SIRI Index is designed to help manufacturers assess and advance their readiness for digitalization. Winter believes that the SIRI framework has two advantages: 1) there are courses available to obtain SIRI appraiser certification; 2) they also provide a prioritization matrix and framework to help develop a feasible plan after the results of the appraisal are available.


How is smart manufacturing different from Industry 4.0?

The terms Industry 4.0 and smart manufacturing are often used interchangeably, but Jeff Winter believes that they are different concepts with different focus areas.

Smart manufacturing is an advanced industrial production method that uses cutting-edge technology, data analytics, and automation to optimize manufacturing processes, increase efficiency, and enable more flexible and faster systems. It represents a paradigm shift in the way products are designed, produced and distributed. The main goal of smart manufacturing is to increase productivity, efficiency and flexibility while reducing waste, energy consumption and operating costs. The scope typically spans the entire manufacturing value chain, including product design and development, production planning, supply chain management, production, quality control, and distribution.

Industry 4.0 is a broader concept that describes the current era, the fourth industrial revolution. This represents the ongoing transformation of traditional industries due to the increasing adoption of digital technologies. Industry 4.0 not only includes smart manufacturing, but also extends to other functions such as logistics, supply chain, transportation, energy, and even healthcare and retail.

What does Industry 4.0 and smart manufacturing mean to end users, original equipment manufacturers (OEMs) and system integrators? The main beneficiaries of smart manufacturing are obviously manufacturers. From a control and automation perspective, manufacturers can gain the following benefits:

· increase productivity:Advanced automation and control systems can optimize production processes, thereby increasing output and resource utilization.

· Enhanced process control:End users can monitor and adjust production processes in real time, ensuring consistent product quality and reducing the possibility of defects or waste.

· Reduce downtime:Advanced automation enables predictive maintenance, helping to prevent unexpected equipment failure and minimizing production downtime.

· Better data-driven decisions:Real-time data from automated systems can help end users make more informed decisions on process improvements, resource allocation, and other aspects of production.

As part of Industry 4.0, the business and operating models of OEMs and system integrators will undergo different transformations. Several ways they can adapt include:

· Value model transformation: As customers demand smarter, more connected machines, the value model of OEMs and system integrators shifts from simply providing hardware to providing smarter, data-driven solutions. This requires the development of new capabilities in software, analytics and connectivity.

· Servitization:Industry 4.0 enables all businesses, especially OEMs and system integrators, to leverage remote connectivity to deliver digital services and deliver exceptional value from all data insights gained. This includes remote monitoring, predictive maintenance, over-the-air updates and optimization services. This could even lead to entirely new business models, with subscription-based services replacing more costly capital expenditure purchases and output-based contracts replacing traditional scope-of-work-based contracts.

· Cooperation and partnerships:The increasing complexity and interdisciplinarity of Industry 4.0 technologies encourages OEMs and system integrators to form strategic partnerships with other technology providers, such as software developers, data analytics companies and IoT platform providers. Fewer companies are continuing to be "one-stop shops" and are mostly turning to demonstrating that they are part of an end-to-end ecosystem that can comprehensively help manufacturers transform their organizations.

▲Figure 3: Success factors for digital transformation include creating a new vision through new knowledge, skills and perspectives.


New requirements for automation engineers in the Industry 4.0 era

The transition from Industry 3.0 to Industry 4.0 has resulted in significant changes in the knowledge and skills required to be a successful control and automation engineer. The biggest differences include:

· Growth mindset of continuous learning:Technological innovation and market conditions change so rapidly that failure to respond quickly can be catastrophic for an organization. It also makes current knowledge/skills obsolete in a short period of time. It is essential to take the time to understand the latest developments in automation, control and Industry 4.0 technologies.

· Enter new disciplines:The lines between engineering and organizational departments have become very blurry. Build multidisciplinary thinking by understanding the interdependencies between various technologies such as mechanical, electrical, software engineering, IT, networking, and other fields. This will help you gain a more complete understanding of the systems you use and identify opportunities for improvement.

· Pay attention to network security:Since Industry 4.0 relies heavily on interconnected systems and data exchange, cybersecurity is crucial. Familiarity with cybersecurity best practices such as secure communications protocols, encryption, and access control. Also, make sure they are implemented in the project.

· Think like an analyst:Those companies that can better capture and utilize the value of data will be the winners in Industry 4.0. Develop data analysis and visualization skills to leverage all data generated by smart manufacturing systems. This will enable users to identify trends, detect anomalies and make data-driven decisions to optimize processes and increase overall efficiency.


Don’t ignore cybersecurity

As smart manufacturing technology is implemented and matures, in addition to device-level IT risks, new cybersecurity challenges have emerged. This field is called operational technology (OT) cybersecurity, which refers to the security and safety of industrial environments and is critical in protecting infrastructure, supply chains, and more.

One of the biggest challenges in OT cybersecurity is the legacy equipment found in much of the world’s critical infrastructure. To address this challenge, OT-specific security devices and platforms are becoming increasingly common in the market, but not all devices and platforms are created equal or adopted uniformly.

It is expected that in the coming years, enterprises involved in critical infrastructure will require more rigorous solutions that provide the highest levels of security and interoperability and will seek compliance with recognized international standards such as ISA/IEC 62443. Workforce development will be another area of ​​focus, with plant managers paying more attention to OT cybersecurity training and certification programs in the coming years.

Digitalization enabled by the Industrial Internet of Things (IIoT) has increased over the past decade, with advances in sensor technology providing more contextual information about equipment, processes and operations. New applications and advancements can also mean greater vulnerabilities, which is why risk management and mitigation strategies have become a key part of digital transformation.


Has Industry 5.0 begun?

Industry 4.0 is not a destination, it is an ongoing journey. Manufacturers that can best optimize their cyber-physical systems (CPS) will be the winners in their markets. Our advice is to do what you can today. It's never too late to start.

The concept of Industry 5.0 has aroused heated discussions, and it aims to clarify the role and contribution of industry to the entire society. Industry 4.0 is about improvements in safety, processes, efficiency and profitability, while Industry 5.0 is about resilience, people-centredness and sustainable business strategies. Industry 5.0 envisions a future scenario in which humans and advanced technologies such as artificial intelligence, robots, and automation will work together in a more harmonious and efficient manner, combining the creativity, empathy, and judgment that humans are best at with advanced technologies. combines accuracy, speed and scalability.

As with Industry 4.0, these ideas may be things businesses are already doing. For example, many companies have committed to sustainable development and environmentally friendly practices, and some have gone further to pursue science-based goals. As with Industry 4.0, the meaningful changes required to transform businesses also require a change in mindset. In any case, Industry 5.0 will indeed become a strategic direction for future industrial development.

Review Editor: Huang Fei

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