Digital Technologies Are the Backbone of an Energy-efficient Industry 4.0

Industry 4.0 is the concept where digital technologies integrate into industrial processes. The continuous innovation in technology within the framework provides opportunities for the development of smart manufacturing solutions.

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Predictive maintenance [ 6 ]  is a proactive strategy that uses data, analytics, and technology to predict when equipment or machinery will likely fail so that maintenance can be performed just in time to prevent the failure. The key elements of predictive maintenance involve monitoring, analyzing, and acting on various aspects of machinery and equipment to optimize maintenance activities.

Sensors deployed within the system can measure parameters such as temperature, vibration, and pressure, providing insights into the health of the machinery. By analyzing the data, the equipment can be regularly monitored to detect any deviations from normal operating conditions. The collected data can help identify trends or patterns that may indicate potential issues.

Developing and deploying predictive algorithms can forecast equipment failures based on patterns identified in historical and real-time data and can provide insights into the remaining useful life of equipment or predict the optimal time for maintenance.

Industry 4.0 [ 1 ] is a transformative concept that represents the integration of digital technologies into industrial processes to create “smart factories” and more efficient, interconnected intelligent systems [ 2 ] . Industry 4.0 leverages technologies like the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and advanced robotics to enhance productivity, efficiency, and flexibility in manufacturing and other industrial sectors. Alongside this, digital transformation plays a key role in leveraging advanced technologies to create more efficient and energy-saving processes as well as reducing waste.

Coal-powered steam propelled the first industrial revolution (starting in the 1760s), electricity powered the second (starting in the 1870s), and automation and machinery drove the third (starting in the 1960s). The fourth industrial revolution is shaped by intelligent systems.

Industry 4.0 emphasizes the implementation of smart manufacturing processes, which are the basis for creating smart factories where production processes are flexible and can be adapted to meet changing market demands. Smart factories are characterized by modular production systems, real-time monitoring, and the ability to customize products efficiently.

Sustainability holds a key position in contemporary business strategies, as noted in a recent Forbes magazine article [ 3 ] . Software solutions empower organizations to focus on enhancing low-carbon practices while optimizing efficiency. These digital tools serve as a foundation for improving productivity and reducing waste in a variety of industries, ranging from energy to food and beverage. With a dedication to innovation, businesses can provide technological solutions that foster sustainable industrial practices and drive positive impact.

Yuhan Zheng, a member of the IEEE Young Professionals Climate and Sustainability Task Force, is a Ph.D. student studying the global governance of climate change and energy transition, specifically examining how energy innovation disproportionately empowers local communities. She says that “digitalization and automation in Industry 4.0 can optimize production processes, leading to reduced material waste and more efficient resource utilization. For instance, predictive maintenance enabled by IoT sensors can prevent machinery breakdowns, reducing downtime and material waste.”

Machines, devices, sensors, and people are interconnected within the industrial ecosystem, forming a network where information can be shared in real time. This connectivity enables seamless communication and collaboration across various components of the production process. In their article, “Advanced Industrial Communication Systems: A Sneak Peak to the Ecosystem of Next Generation Industrial Communications [ 4 ] ,” published in IEEE Transactions on Industrial Informatics, the authors present an approach to the ever-increasing amount of data and information gathered from these systems and the ability to drive improved communications. 

These connected devices generate vast amounts of data that need to be processed and analyzed. This data-driven approach enables better decision making for optimizing processes, reducing energy consumption, and improving overall productivity. Taken together, systems engineers can optimize resources in real-time, including the efficient use of raw materials and water. Zheng says, “Smart sensors integrated into machinery and infrastructure can continuously monitor energy usage patterns. Data analytics algorithms can then analyze this data to identify opportunities for energy optimization, such as adjusting production schedules to leverage off-peak energy periods or optimizing equipment settings for energy efficiency.”

A key component of Industry 4.0 involves the concept of digital twins [ 5 ] . By creating virtual replicas of physical assets or processes, the digital twin, engineers and designers can see a process before it is fully developed. This is especially useful for predictive maintenance where potential issues can be identified and addressed before they result in process or equipment failures. Predictive maintenance minimizes downtime and reduces the need for emergency repairs. 

Industry 4.0 fosters collaborative ecosystems where suppliers, manufacturers, and customers are interconnected. This collaboration allows for more efficient resource planning, inventory management, and coordination throughout the supply chain. In fact, digital twins can also facilitate the development of modular production systems, or more localized and customized manufacturing, which can reduce the need for extensive transportation of goods. Zheng points out that “connected systems and IoT technologies enable real-time data collection and analysis, facilitating the implementation of sustainable practices like energy-efficient manufacturing processes and intelligent supply-chain management.”

Incorporating tools like additive manufacturing, such as 3D printing, allows for more precise and efficient production of components. This technology minimizes material waste by utilizing only the necessary amount of material to create a specific item.By combining these technological advancements, Industry 4.0 not only improves industrial efficiency and productivity but also helps to mitigate the impact of climate change by creating more sustainable industrial processes. IEEE is a rich resource for understanding the concepts of Industry 4.0 and its impact on society.

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[1] M. N. Hassan Reza, C. Agamudai Nambi Malarvizhi, S. Jayashree and M. Mohiuddin, “Industry 4.0–Technological Revolution and Sustainable Firm Performance,” 2021 Emerging Trends in Industry 4.0 (ETI 4.0), Raigarh, India, 2021, pp. 1-6, doi: 10.1109/ETI4.051663.2021.9619363.

[2] P. Patel, M. I. Ali and A. Sheth, “From Raw Data to Smart Manufacturing: AI and Semantic Web of Things for Industry 4.0,” in IEEE Intelligent Systems, vol. 33, no. 4, pp. 79-86, Jul./Aug. 2018, doi: 10.1109/MIS.2018.043741325.

[3] Sharma, G. (n.d.). Modeling Sustainable Industrial Ecosystems Is A ‘Great Business’ For AVEVA’s Boss. [online] Forbes. Available at: [Accessed 17 Mar. 2024].

[4] E. Sisini, T. Sauter, Z. Pang and H. -P. Bernhard, “Guest Editorial: Advanced Industrial Communication Systems: A Sneak Peak to the Ecosystem of Next Generation Industrial Communications,” in IEEE Transactions on Industrial Informatics, vol. 18, no. 10, pp. 7316-7320, Oct. 2022, doi: 10.1109/TII.2022.3167381.

[5] F. Tao, H. Zhang, A. Liu and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,” in IEEE Transactions on Industrial Informatics, vol. 15, no. 4, pp. 2405-2415, April 2019, doi: 10.1109/TII.2018.2873186.

[6]  J. Gama, R. P. Ribeiro and B. Veloso, “Data-Driven Predictive Maintenance,” in IEEE Intelligent Systems, vol. 37, no. 4, pp. 27-29, 1 July-Aug. 2022, doi: 10.1109/MIS.2022.3167561.