Smart Manufacturing 5.0

Part I: Smart manufacturing 5.0 foundations

1. Smart manufacturing 5.0 architecture and human-centred perspectives
Scope: This chapter introduces and presents the main scope of this book. For this it will describe the main relations between Smart Manufacturing 5.0 architecture and the human-centred perspective. The aim of this chapter is to give a clear and concrete framework for the whole book.


2. Smart manufacturing key enabling technologies
Scope: Smart Manufacturing is supported by digital technologies that enable smart decision-making processes, as well as smart control systems. This chapter reviews the technologies that underpins this paradigm, providing new insights of the latest technologies developments that support the “smartness” of the systems.


3. Smart manufacturing potential towards sustainability
Scope: Manufacturing, as an industrial activity, has a great impact in the three lines of sustainability, i.e. environmental, social and economical. This chapter would revise the links and synergies that the smart manufacturing paradigm provides to sustainable issues.


4. Industry 4.0 and 5.0 contribution to smart manufacturing
Scope: Important technologies and system development have occurred in the last years in terms of industrial activity. These developments have mainly two great parts, from one side the technology leveraging manufacturing capabilities in terms of hard-meaning (mainly, improving productivity ), but on the other side and more recently, there have appeared new developments making more user friendly technologies. The first set of developments is known as Industry 4.0, meanwhile the latter is called Industry 5.0. In this chapter both approaches will be explored in terms of Smart Manufacturing perspective.


5. The role of humans in smart manufacturing
Scope: Humans are a key part in manufacturing activities, not only as labour, but also as decision makers. The advent of all these manufacturing technologies provide a great opportunity to reach new manufacturing capabilities, but for that they should be integrated with the whole manufacturing ecosystem, and that comprehends humans. In this chapter, it will be described how humans relate to technologies, and how they relate to the whole Manufacturing System.


6. The human-machine relationship in Intelligent Systems
Scope: Intelligent systems are modifying all things that can be systematised. Manufacturing enters in that category of “things”. However, for having full advantage of intelligent systems in Manufacturing, humans must be considered as part of it. This chapter will present guidelines and surveys on how this relationship between humans and intelligent systems can be implemented in Smart Manufacturing.


7. Smart Manufacturing applications
Scope: This Chapter will analyse different particular cases of Smart Manufacturing developments and implementations. These cases will provide a more practical perspective on Smart Manufacturing, coming up with different kinds of solutions and obstacles that have been addressed on these Smart Manufacturing Applications.


8. Implementation challenges of Smart manufacturing 5.0
Scope: This chapter will point out what are the main barriers as well as challenges to carry out a Smart Manufacturing 5.0 project. Also, it will consider the potential challenges that are still not addressed by this new paradigm and require further research.


9. Resilient Smart manufacturing 5.0
Scope: This chapter will study the resilience of manufacturing systems and how companies can better navigate uncertainties, mitigate risks, seize opportunities, and ultimately enhance their competitiveness and sustainability in a rapidly evolving global landscape.

Part II: The role of decision making in smart manufacturing

10. Decision Making Processes in a manufacturing system
Scope: This chapter will introduce this part by presenting how decision-making processes are done in Manufacturing Systems, and the impacts of these processes in the Manufacturing performance. It will try to indicate the relation of the data that is required to decision making processes and the output that are expected of each process. Also the relation between the different business functions that affect manufacturing and how these relationships affect decision-making processes.


11. Types of Decision Making methods and level of automation 0 decision making Processes
Scope: This chapter will identify the main decision-making methods and approaches that are used in Manufacturing, revising different temporal horizons as well as criteria involved in the decision processes and how digital technologies can leverage these processes. Digital technologies enhance the accessibility to data and information that, in old manufacturing technologies, used to be stuck in the physical assets. Also, Digitalization allows associated AI tools to harness this accessibility and give a more elaborated outcome. This chapter will analyse the relation between these new tools and how decision-making processes are automated.


12. Decision making contribution towards sustainable manufacturing
Scope: Sustainable issues are of vital importance for future manufacturing systems and for the society as a whole. Then, it is important to align the manufacturing developments of future years to this end. For this, this chapter will consider how decision making processes are focused on this issue, and how are the next steps for fostering sustainability through manufacturing systems.


13. Challenges in the implementation data driven decision making
Scope: Data-driven approaches to decision making processes harness the potential of digital technologies for improving decision making processes. However, for having a streamline data-driven implementation, it is necessary to deal with several technical issues, such as frequency of feeding the data, compatibility, interoperability, and so on. This chapter will analyse all these challenges and propose how to overcome them.

Part III: The effects of smart manufacturing to production organisations

14. Smart manufacturing and Production scheduling
Scope: Scheduling is a key decision-making process since it is the last planning decision before production starts. Shop-floor utilisation rates, delivering times, material handling and many other issues are directly affected by scheduling decisions. Since Smart Manufacturing propose a redesign of all decision making processes, this chapter will provide new cases and studies on how Scheduling decisions


15. Smart manufacturing and Zero defect & waste manufacturing
Scope: Smart manufacturing aligns seamlessly with the pursuit of zero defect and waste manufacturing, aiming to maximize efficiency and product quality while minimizing resource consumption and waste generation. Through the integration of real-time monitoring, predictive analytics, and automation technologies, manufacturers can identify and address defects at every stage of the production process, from design to assembly. By leveraging data-driven insights, businesses can optimize parameters, improve process control, and enhance product consistency, thereby reducing the likelihood of defects and waste. Moreover, smart manufacturing enables proactive maintenance and asset optimization, ensuring equipment reliability and minimizing downtime, further contributing to the goal of zero defect and waste manufacturing.


16. The benefits of smart manufacturing in quality management
Scope: Smart manufacturing offers a multitude of benefits in quality management, transforming traditional production processes. By integrating sensors, IoT devices, and advanced analytics, smart manufacturing provides real-time monitoring and control over production parameters, enabling immediate identification and rectification of quality deviations. Zero Defect Manufacturing is the latest approach for advanced quality management. Predictive maintenance ensures continuous operation of equipment, reducing downtime and preventing quality defects caused by machinery failures. Automated quality control systems, powered by machine vision and AI, enhance inspection accuracy, minimizing defects and rework. The extensive data collected throughout the production process enables data-driven decision-making, leading to optimized processes and improved product quality. Ultimately, smart manufacturing fosters a culture of continuous improvement by providing insights into production performance, facilitating agility, and ensuring that quality standards evolve to meet changing market demands.


17. The use of smart manufacturing for virtual metrology
Scope: The implementation of smart manufacturing for virtual metrology revolutionizes quality inspection by offering real-time insights and predictive capabilities without the need for physical measurements. Through advanced modelling, simulation, and data analytics, virtual metrology accurately predicts product dimensions and characteristics based on process parameters and historical data. This approach reduces reliance on traditional, time-consuming metrology methods, enabling faster decision-making and greater production efficiency. By continuously analyzing and optimizing production processes, virtual metrology ensures that quality standards are consistently met while minimizing costs associated with physical measurements and inspection. Furthermore, virtual metrology enhances flexibility by allowing manufacturers to adapt processes quickly to changing product specifications or market demands, ultimately driving improved product quality and competitiveness.


18. The use of predictive analytics for zero defects and zero waste
Scope: Predictive analytics plays a pivotal role in achieving the ambitious goals of zero defects and zero waste in manufacturing. By analyzing historical data and real-time inputs from sensors and IoT devices, predictive analytics can anticipate potential defects or deviations in the production process before they occur. This proactive approach allows manufacturers to take corrective actions swiftly, preventing defects and minimizing waste. Additionally, predictive analytics helps optimize production processes to operate within optimal parameters, reducing variability and enhancing product consistency. By continuously improving processes based on predictive insights, manufacturers can move closer to the ideal of zero defects and zero waste, ensuring high-quality products while maximizing resource efficiency and sustainability.


19. The support of smart manufacturing for Zero Defect remanufacturing
Scope: Smart manufacturing provides essential support for achieving Zero Defect remanufacturing, a process aimed at refurbishing used products to a condition equal to or better than new while eliminating any defects. Through the integration of advanced technologies such as IoT devices, sensors, and data analytics, smart manufacturing enables precise monitoring and control of remanufacturing processes. This ensures that every step, from disassembly to reassembly, meets stringent quality standards, minimizing the risk of defects. Additionally, smart manufacturing facilitates the identification and tracking of each component's history and condition, enabling selective replacement of parts and ensuring optimal performance. By leveraging automation and real-time data insights, manufacturers can streamline remanufacturing processes, reducing costs, waste, and environmental impact while delivering products of the highest quality.


20. Smart manufacturing and Circularity
Scope: In the context of smart manufacturing, circularity emerges as a fundamental principle driving sustainability and resource efficiency. Through the implementation of advanced technologies such as IoT sensors, data analytics, and machine learning algorithms, manufacturers can optimize material usage, minimize waste generation, and promote product lifecycle extension. By integrating circularity principles into production processes, businesses can design products for durability, reuse, and recycling, thereby reducing environmental impact and fostering a more sustainable manufacturing ecosystem. Additionally, smart manufacturing enables the tracking and tracing of materials throughout their lifecycle, facilitating closed-loop systems and circular supply chains. As a result, the convergence of smart manufacturing and circularity not only enhances environmental stewardship but also drives economic value and resilience in the long term.


21. Smart manufacturing and supply chain
Scope: Every manufacturing system is embedded on a larger supply chain system. Within supply chain systems material flows are of vital importance, but information flows are also vital. Then, this chapter will analyse how digital transformation of the manufacturing information system impacts on the supply chain information flows.


22. Smart manufacturing and business processes and decisions
Scope: In smart manufacturing, the integration of advanced technologies like Internet of Things (IoT), artificial intelligence (AI), and big data analytics revolutionizes traditional business processes and decision-making. Through real-time data collection and analysis, manufacturers gain valuable insights into various aspects of their operations, from production efficiency to supply chain management. This enhanced visibility enables proactive decision-making, allowing businesses to respond swiftly to changing market demands, optimize resource allocation, and mitigate risks. Moreover, smart manufacturing facilitates the automation of routine tasks, freeing up valuable human capital to focus on strategic initiatives and innovation. By aligning business processes with technological advancements, smart manufacturing drives agility, resilience, and competitiveness in today's dynamic marketplace.

Part IV: Enabling technologies and methods for Smart manufacturing

23. AI and ML in smart manufacturing era
Scope: Artificial Intelligence and Machine learning tools have emerged to deal with the great amounts of data that digital systems generate. These tools are capable of handling enormous datasets and come up with valuable and synthesised information for decision makers. This chapter will study these tools in the Smart Manufacturing environments. For making a proper use of the data provided by cyber-physical systems in Manufacturing it is necessary to process that data to learn from the system evolution. This learning capability enables to incorporate many of the environmental factors that are really hard to include by other means . This chapter will cover these developments describing the importance to deepen on this line for future research.


24. Behavioural considerations of AI in Smart Manufacturing operations
Scope: Human behaviour is usually hard to analyse and to forecast. This is also true in Smart Manufacturing operations and relations to humans. However, AI provides a proper framework to incorporate this issue in operations management. AI is claimed to be the next step in competitiveness for operations management. This chapter will explore and present cases of AI impact on Operations management.


25. Digital twins and smart manufacturing
Scope: Digital twins serve as the backbone of smart manufacturing, offering a virtual representation of physical assets, processes, and systems. By creating digital replicas that mirror real-world counterparts, manufacturers gain unprecedented visibility and control over their operations. These twins enable continuous monitoring, simulation, and optimization of manufacturing processes in a virtual environment, facilitating predictive maintenance, performance analysis, and scenario planning. Through real-time synchronization with physical assets, digital twins empower manufacturers to anticipate potential issues, optimize production workflows, and enhance overall efficiency. As a result, digital twins play a pivotal role in driving innovation, agility, and competitiveness in the realm of smart manufacturing.


26. Data analytics and smart manufacturing
Scope: The potential of collecting data all along the shop-floor and integrating it with other sources of information provides the foundations to develop a comprehensive dashboard to reflect in real time the status of the Manufacturing system. However, to represent this status in a clear and friendly manner, all that data must be analyzed and presented in a manageable manner. Then, this chapter will revise the main analytics procedures and methods existing in the literature, as well as which are the main indicators feeded with that data.


27. Data structuring in smart manufacturing
Scope: In Manufacturing the sources of data are very different. These types of sources can provide visual information from a camera recording the movements in the shop-floor, but also can be associated with other machines parameters like temperature, velocity, forces, etc. All these sources provide information in a different format and frequency (some may provide information per seconds and other by mili-seconds). Then, to take advantage of this data it is necessary to structure and organize it. This chapter will study different approaches and methods to this end.


28. Knowledge extraction in smart manufacturing
Scope: Knowledge extraction in smart manufacturing involves the systematic extraction of valuable insights from vast amounts of data generated across various manufacturing processes. Through advanced analytics, machine learning algorithms, and artificial intelligence techniques, relevant information is distilled from sensor readings, production logs, and other sources. This extracted knowledge enables manufacturers to optimize production efficiency, predict equipment failures, enhance product quality, and streamline operations. By harnessing these insights, smart manufacturing systems empower decision-makers to make data-driven choices, driving continuous improvement and innovation in the manufacturing industry.


29. AI applications to Smart Machining for leveraging Smart Manufacturing
Scope: Smart machining gather a set of developments that contribute to improve machine parametrization in machining operations. The possibility of parametrized these operations in an automatic manner reduce considerably the cicle time and also paves the way to improve quality of finished products. This chapter will delve into AI applications for Smart Machining and machines operations in general.


30. Technological challenges for smart manufacturing implementation