How to stay competitive during the Fourth Industrial Revolution
Author: Carlo Schildermans, Account Director, PDM
It is strategically important for companies to remain competitive and profitable by improving, innovating and redirecting their business, now and in the future. This requires them to have their engineering, asset management and manufacturing processes in order. According to PDM’s Industrial Excellence philosophy, companies can only start implementing Industry 4.0 technologies when these business processes are properly set up.
Industry 4.0 technologies can help improve the company’s competitiveness by increasing the reliability of industrial facilities, reducing maintenance costs, and improving safety, thus having a substantial impact on competitiveness. Promises like these will only be fulfilled if Industry 4.0 is fully adopted, the basic processes are in order and there is strong leadership in the company.
An Industry 4.0 production facility is characterized by the application of technologies such as 3D scanning, additive manufacturing, the digital twin, autonomous systems, Internet of Things (IoT), augmented and virtual reality, artificial intelligence and machine learning. The combined application of these technologies yields the Smart Factory. The goal of such a factory is to create a flexible, responsive, and autonomous production environment that can adapt to changing market conditions, customer needs and supply chain disruptions.
The promises of Industry 4.0 can only be fulfilled if the basic processes are in order and there is strong leadership in the company.
Adoption of technologies
The adoption of Industry 4.0 technologies depends on several factors, including a company’s size, type and maturity. In general, companies that are well established, have a large asset base and operate in a highly regulated industry are more likely to adopt these technologies. However, smaller companies and those operating in less regulated industries can also benefit from adopting these technologies, but may need to do so in a step-by-step and more phased manner.
To fully reap the benefits of these technologies, companies must have a mature and well-developed asset management system, with the necessary processes, systems and personnel to support their implementation. In addition, companies must have the necessary financial resources, IT infrastructure and data management to support the implementation and operation of these technologies.
It is also important to note that the implementation of these technologies requires a significant investment of time, money and resources and should only be undertaken after a thorough assessment of the costs, benefits and risks. Companies should carefully consider their needs, goals and capabilities and develop a well-thought-out implementation plan that takes into account the resources required, timelines and expected results.
It is crucial that these technologies are connected, interoperable and scalable. Standards have been developed for Industry 4.0 technologies in general and for specific technologies to ensure that systems are designed, installed and maintained in a manner that is safe, reliable and consistent with industry best practices. A well-known standard for getting more value from assets is ISO 55000 for asset management, a related standard is ISO 31000 for risk management. A standard that describes the virtual representation of assets is IEC PAS 63088, which outlines the reference architecture model Industry 4.0 (RAMI 4.0), which in fact a bundle of open standards for smart manufacturing. A related standard is IEC 62890 for lifecycle management of systems and components. In addition to these general standards, there are standards and best practices for specific technologies, such as the IoT World Forum Reference Model for IoT solutions.
Speed of adoption
The speed of adoption of various models and technologies in the industry depends on multiple factors such as the maturity of the technology, the availability of qualified personnel and infrastructure, the level of awareness and understanding of the technology, the potential benefits, the level of investment required, the level of risk involved, the level of competition and the regulatory environment. Some technologies and models are adopted quickly due to their potential benefits and ease of implementation, while others require more time due to their complexity or the need for a change in industry practices and regulations.
The use of an external expert can be helpful in defining the needs and implementing technological innovations in asset management. Experts bring a wealth of knowledge and experience to the table, having worked with a range of clients and technologies in the past. This can be valuable in helping a company understand what solutions are available and which are best suited to their needs.
A clear and well-defined vision on asset management helps to align the organization’s efforts and resources towards maximizing the performance, efficiency, and effectiveness of its assets. As new applications emerge every day and existing applications evolve, it is important for companies to keep abreast of new technologies and continually evaluate and update their strategies and processes to stay ahead of the game. Companies that can effectively adopt and implement Industry 4.0 technologies are likely to have a competitive advantage, provided they have their basic processes in order and there is strong leadership in the company.
The speed of adoption of Industry 4.0 technologies depends on multiple factors.
Benefits of Industry 4.0 technologies
Implementing Industry 4.0 solutions must consider the need for specialized expertise and resources, lack of data quality and standardization, the challenge of processing large amounts of data, and regulatory and ethical issues. Companies that manage to overcome these hurdles and adopt the latest technologies can reap the rewards. Smart Factories can increase productivity, reduce downtime and improve product quality, all while minimizing waste and maximizing resource utilization.
Predictive maintenance enables companies to identify and resolve maintenance issues before they lead to costly breakdowns, helping to increase the reliability of industrial facilities and reduce maintenance costs. Predictive maintenance is based on data from IoT devices about equipment performance and condition. It also uses advanced analytics and machine learning algorithms to predict when equipment is likely to fail or begin to deteriorate.
General operational methods
With 3D scanning digital representations (3D models) can be made of physical objects, such as machinery, buildings and infrastructure. These 3D models can be used for preparing maintenance work. This can help reduce downtime, increase safety and extend asset life. Virtual reality environments for training and simulation purposes can be created with 3D models. Engineers can use 3D models to analyze an asset’s stress points and identify areas where improvements can be made to extend its life. And the use of these models has been adopted in the field of testing, inspection and certification.
Additive manufacturing (3D printing) allows the creation of complex shapes and structures, offering greater design flexibility. This technology is widely used for prototyping and low volume production. Production can be done on demand, reducing the need for large stock. The use of 3D printing opens up new opportunities for decentralized production, enabling closer collaboration between suppliers and customers along the supply chain.
A digital twin is a virtual representation of a physical asset, system or process, which can be used to simulate and analyze its behavior, performance, and maintenance needs. A digital twin is more powerful when it uses real-time data from different sources, such as sensors. By testing and evaluating different scenarios, the digital twin can be used to identify areas for improvement.
Robots and other autonomous systems can perform routine maintenance tasks, reducing the need for human intervention and improving efficiency and safety. Maintenance tasks can be performed more accurately and consistently than by human operators. In addition, robots can function in hazardous environments, reducing the risk of injury to human operators. In addition to these applications in the process industry, robots are used in manufacturing facilities to assemble machinery and components and for internal logistics (AGVs).
IoT devices, such as sensors, collect and analyze real-time data. IoT devices are combined with monitoring and communication systems, such as 5G networks. By integrating these devices into industrial systems, companies can make data-driven decisions about maintenance and operations. IoT devices provide the data that enable predictive maintenance. Sensors can also be used to measure the presence of substances in a confined space to determine whether it is safe to enter.
Augmented and virtual reality (AR/VR) can be used to support maintenance operations in training and education, equipment visualization, process simulation, collaboration and teamwork. Remote assistance can be provided to operators in a factory in real time by experts from anywhere in the world.
Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that would normally require human intelligence, such as recognizing patterns, making predictions, and learning from experience. Machine learning (ML) is a subset of AI that involves the use of algorithms to enable systems to learn and improve from experience without being explicitly programmed. AI and ML can be used to analyze data from the supply chain, such as production data, shipping data and inventory data. Connected systems can provide visibility into inventory levels, delivery schedules, and demand forecasts, helping manufacturers optimize production planning and reduce waste. AI and ML can also be used to analyze data from industrial assets and the wider market to identify and assess potential risks and to make informed decisions about risk mitigation strategies. In combination with a digital twin, this data can also be used for decision-making, such as when to shut down a production line for maintenance or when to switch to a backup generator during a power outage.
With thanks to: Klaas Bos, Technical Writer, PDM