The Future of Manufacturing and Industry 4.0

1. Introduction

Industry and its associated elements are an important part of modern society [1]. In addition to technological developments, all industrial revolutions have involved important social changes and shifts in other areas of life, too [2]. The pace of change is increasing, and the emergence of new trends is accelerating [3]. Everyone is right to ask the following question: what is the future of manufacturing and Industry 4.0? Industry 4.0 (I4.0) [4], also known as the Fourth Industrial Revolution or smart manufacturing, involves the integration of advanced digital technologies into industrial and manufacturing processes, as in connected factories. It is a critical and highly relevant topic, as rapid technological advancements are fundamentally reshaping production processes. This paradigm incorporates technologies such as the Internet of Things (IoT) [5,6,7,8] and artificial intelligence (AI) [9,10,11,12] to enable real-time decision-making and enhance productivity. These technologies are fostering the development of smart factories [13], where automation and AI-driven decision-making optimize efficiency, precision, and quality control while reducing costs [14,15,16]. A major advantage of I4.0 is its influence on global competitiveness and economic growth. Companies and nations that integrate these technologies can boost productivity, minimize waste, and drive innovation, ensuring they remain competitive in the global market. Furthermore, sustainability [17,18,19,20,21] has become a vital factor in modern manufacturing. I4.0 supports energy-efficient production [22,23], reduces waste [24], and promotes the development of eco-friendly materials [25,26], contributing to net-zero carbon goals [27,28,29]. Despite all of this, I4.0 is not perfect, and various crises and future projections have emphasized the need to reassess certain aspects of the I4.0 framework. The evolution of I4.0 into Industry 5.0 (I5.0) [30,31,32,33] introduces three fundamental pillars: resilience, sustainability, and human centricity. Recent global crises, such as the COVID-19 pandemic, have demonstrated that previous industrial production and logistics models assumed an idealized world [34,35,36,37,38]. However, since real-world conditions are far from perfect, it is crucial to develop industrial ecosystems that can withstand adversity. Additionally, growing evidence suggests that the current growth-driven economic model may be unsustainable [39,40], potentially leading to a global-scale collapse if industrial and environmental sustainability are not prioritized. Although societal systems are designed to serve humanity, the drive for efficiency often overlooks the human element. Therefore, human-centered thinking [41,42,43,44] must become a focal point in industrial development. Moreover, recent global disruptions have underscored the importance of building resilient supply chains [45,46,47]. Intelligent supply chain management, powered by real-time data and AI [48,49], improves efficiency and mitigates risks, allowing businesses to adapt to unforeseen challenges. However, as industries become increasingly interconnected, cybersecurity threats also grow. Protecting industrial networks and sensitive production data requires robust security measures to prevent potential cyberattacks [50]. The current iteration of I5.0 may not fully address all future challenges and opportunities associated with I4.0. Thus, it is essential to examine the trajectory of I4.0 from multiple perspectives to guide the evolution of I5.0 and, ultimately, benefit humanity. With this vision in mind, several aspects of future manufacturing processes may need to be revised or refined. Future research on I4.0 should explore its risks, technological advancements, societal impacts, inconsistencies, and any other relevant topics related to this industrial transformation. As I4.0 progresses and transitions into I5.0, manufacturing will become more intelligent, interconnected, and sustainable. Businesses and governments that embrace these advancements will secure their place in the evolving industrial landscape, while those that resist change may struggle to remain competitive.

2. An Overview of Published Articles

This section presents a comprehensive review of the Special Issue, The Future of Manufacturing and Industry 4.0, summarizing the key contributions of the published articles. The first paper provides an overview of the advances and challenges in evaluating safety and assessing exposure in the I4.0 landscape. Machine learning (ML), AI, the IoT, digital twins (DTs), and cloud computing are transforming occupational health and safety. These technologies enable real-time monitoring, predictive analytics, and automated hazard detection. Wearable devices, smart sensors, and augmented reality (AR) applications are essential tools for enhancing risk management, worker training, and accident prevention. Nevertheless, the extensive implementation of these technologies raises significant issues concerning data privacy, cybersecurity, and ethical considerations. Furthermore, maintaining the precision of exposure evaluations continues to pose a considerable difficulty, especially for airborne substances like bioaerosols, particulate matter (PM), and volatile organic compounds (VOCs). Future studies ought to concentrate on overcoming these limitations by enhancing regulatory frameworks, refining predictive models, and creating ethical standards to facilitate the safe and effective utilization of I4.0 technologies in industrial settings [51].

Additionally, a different study explores the development of DT technology, tracing its journey from a theoretical construct to a complex and extensively used instrument. DTs, which serve as virtual representations of physical objects, enable ongoing surveillance, modeling, and anticipatory analysis, thus significantly contributing to the enhancement of processes in various sectors. This study systematically classifies DTs according to their architectural design, functional characteristics, and integration levels. It highlights their transformative impact across sectors such as manufacturing, healthcare, and smart city development. Recent advancements in DT technology integrate AI, ML, and IoT to enhance their capabilities. The review also examines key challenges, including cybersecurity vulnerabilities, data privacy issues, and interoperability limitations. Future research directions should prioritize the refinement of DT models, the enhancement of data security measures, and the establishment of standardized frameworks to fully harness the potential of DT technology across various domains [52].

Another recent study introduces an industrial design decision-making framework intended to improve the competitiveness of machine tool enterprises. This approach combines Product Family Architecture (PFA) with Structural Equation Modeling (SEM) to systematically evaluate and optimize industrial design processes. The study uses both subjective and objective weighting methods, such as the Analytical Hierarchy Process (AHP) and Grey Relational Analysis (GRA), to pinpoint the key design elements that drive product competitiveness. The proposed methodology is implemented in the design of Heatking induction vertical hardening machines. This approach enhances industrial styling, ergonomics, and human–machine interaction. The findings definitively highlight the critical role of design aesthetics and modularization in machine tool manufacturing, emphasizing the need for structured evaluation models to support industrial design decision-making. Future research should refine these methodologies by incorporating intelligent and digital design strategies aligned with I4.0 advancements [53].

Human involvement remains vital in the industrial sector. The next study introduces a Human-Centered Knowledge Graph (HCKG) framework, which is aimed at advancing collaborative manufacturing within the I5.0 paradigm. In contrast to I4.0, which centers on automation, I5.0 prioritizes human–machine collaboration. It uses semantic technologies, ontologies, and knowledge graphs (KGs) to effectively support human operators. The proposed framework uses KGs to model essential operator-related factors, such as movement tracking, working conditions, ergonomics, and interactions with robotic systems. It incorporates semantic reasoning and ontology-based modeling, enabling real-time data exchange and adaptive decision-making within cyber–physical manufacturing environments. A case study on wire harness assembly definitively demonstrates the practical application of the HCKG framework in an industrial environment. The findings clearly show how knowledge graphs enhance resource allocation, optimize workflows, and provide real-time operator support through the integration of sensors, AI-driven analysis, and KPI-based insights. The study’s key contributions are threefold: extending existing automation standards (ISA-95, B2MML, and AutomationML) to integrate human-centric processes, using graph-based data analysis to assess operator–machine collaboration and performance metrics, and semantically integrating monitoring systems, human activity recognition, and digital assistance tools. The research also validates the HCKG concept through a replicable industrial case study. It emphasizes the importance of human factors in cyber–physical systems and advocates for a balanced integration of human expertise and automation in future manufacturing ecosystems. Future research will focus on integrating DT technology, automating data retrieval processes, and enhancing decision-support mechanisms to further advance I5.0 applications [54].

Reconfigurable mechanisms are essential in complex manufacturing systems. They enhance adaptability and efficiency. The next study examines the impact of multi-spindle reconfigurable machines (MRMTs) within reconfigurable manufacturing systems (RMSs), focusing on various reconfiguration policies. The primary aim is to assess how modular and adaptive systems affect manufacturing performance under different conditions, such as machine failures, process time variability, and fluctuations in part inter-arrival times. Using discrete event simulation models, the study evaluates multiple system configurations, comparing single-spindle and multi-spindle machine setups under both periodic and continuous reconfiguration policies. The analysis considers key performance metrics like throughput, system stability, machine utilization, and production delays. The results are clear: integrating well-balanced multi-spindle machines significantly improves system performance and stability compared to setups with a single multi-spindle machine. Additionally, continuous reconfiguration policies offer greater flexibility and resilience to failures compared to periodic approaches. The study also reveals that failures and variability in processing times are critical factors influencing overall performance, with multi-spindle machines showing greater robustness in handling these disruptions. The research emphasizes the crucial role of simulation-based decision-making in optimizing RMS configurations, providing valuable insights for industry leaders considering investments in multi-spindle machines. Future research will focus on refining module allocation strategies, conducting economic feasibility analyses, and exploring integration with DT technologies to further enhance system adaptability and efficiency [55].

Another study explores data-driven business model innovation (DDBMI) and its significance for incumbent manufacturers. The study focuses on assessing organizational readiness to integrate data-driven technologies into business models, identifying key enablers, value creators, and expected outcomes that influence successful implementation. Nine topics central to DDBMI are identified through topic modeling and thematic synthesis: digitalization, the IoT, AI, big data, mobile networks, platformization, servitization, the sharing economy, and sustainability. They are organized into three hierarchical levels. The first level, enablers, includes foundational technologies such as digitalization, IoT, AI, big data, and mobile networks, which facilitate data collection and analysis. The second level, value creators, comprises strategic business approaches like platformization, servitization, and the sharing economy, which provide competitive advantages. The third level, outcomes, highlights sustainability as a key factor for ensuring long-term innovation and economic resilience. The study develops a knowledge framework based on five key dimensions to assist manufacturers in evaluating their readiness for DDBMI adoption: infrastructure and technology, organizational capabilities, theoretical foundations, strategic solutions, and evaluation metrics. By underscoring the interconnectedness of these elements, the study advocates for an evidence-based approach in decision-making, strategy formulation, and technology deployment. Its findings contribute to the theoretical understanding of organizational preparedness for digital transformation while providing industry leaders with practical insights for aligning their business models with emerging technological advancements. Future research should focus on empirical validation, industry-specific case studies, and the optimization of DDBMI strategies to enhance their applicability and effectiveness in diverse manufacturing contexts [56].

Cost-effective solutions are essential in industrial applications, leading to the development of a low-cost stationary 3D scanning system for reverse engineering, engineering metrology, and machine diagnostics. This study presents a scanning system that seamlessly integrates with IoT networks, in line with the I4.0 paradigm, to enhance connectivity and automation in manufacturing environments. The proposed system uses an infrared distance sensor based on optical triangulation, controlled by a 32-bit microcontroller. The generated point cloud data are processed and transmitted through a wireless communication module, enabling real-time integration with external devices such as cloud storage platforms and diagnostic controllers. Designed with affordability and efficiency in mind, the system incorporates 3D-printed components and microcontroller-based control to reduce costs while maintaining functionality. In addition, the scanner provides high-speed digitization of mechanical parts, with an average measurement uncertainty of 3 mm. Its modular design enables easy integration into IoT-enabled environments, and real-time data processing is enhanced with a moving average filtering algorithm that reduces noise and improves measurement accuracy. Experimental validation demonstrates the system’s effectiveness in reconstructing 3D models, although reflective surfaces present challenges due to infrared light distortion. The study suggests that future enhancements should focus on integrating time-of-flight (ToF) sensors, improving scanning resolution, and expanding the system’s capacity for larger objects. This research contributes to the development of affordable, high-precision 3D scanning solutions that advance smart manufacturing and 3D printing applications [57].

ML techniques are increasingly used in manufacturing, providing new opportunities for defect prediction and quality control. The next study presents an ML-based approach for predicting short-shot defects in injection molding using transfer learning. While traditional computer-aided engineering (CAE) methods have helped reduce manufacturing defects, they often show discrepancies when compared to real-world conditions. To improve defect prediction accuracy, this research combines neural networks with real-time process data, bridging the gap between simulated and actual manufacturing environments. A Back Propagation Neural Network (BPNN) model was developed and initially trained with CAE simulation data, and then refined with real process data to improve its predictive capabilities. Two transfer learning strategies were evaluated: the first transferred knowledge from CAE simulations to real process data, fine-tuning the model for actual manufacturing conditions; the second involved cross-product transfer, adapting a model trained on one product (LT60) to another (LT100). The results show that transfer learning effectively reduces overfitting and improves prediction accuracy even with limited training data. The models showed strong generalization, achieving 90.2% accuracy for LT60 and 94.4% for LT100. In addition, the approach enables real-time defect prediction, allowing production personnel to identify potential problems before the mold is opened, thereby improving process efficiency and minimizing waste. This study highlights the potential of transfer learning for defect prediction in smart manufacturing and underscores the need for further research on real-time integration with production systems and DT technologies to optimize industrial defect prevention strategies [58].

These studies collectively highlight the future of manufacturing and I4.0. Given the breadth of this topic, this collection includes papers from specific research to overviews. It is clear that many factors, from cost-effectiveness and the role of people to the importance of different models, will influence the future of this field.

3. Conclusions

I4.0 has revolutionized manufacturing through AI, IoT, and digitalization, enhancing efficiency, sustainability, and global competitiveness. However, challenges such as cybersecurity risks, workforce adaptation, and regulatory gaps remain critical. The transition to I5.0 aims to address these limitations by prioritizing resilience, sustainability, and human-centric innovation.

Future research must focus on risk mitigation, predictive modeling, and the balance between automation and human expertise. The studies in this collection highlight key advancements in intelligent manufacturing, from machine learning-driven quality control to adaptive industrial ecosystems. A multidisciplinary approach will be essential to ensuring sustainable and competitive industrial development.