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Positioning Industrial Engineering in the Era of Industry 4.0, 5.0, and Beyond: Pathways to Innovation and Sustainability
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Abstract
Industrial Engineering (IE) has continually evolved to optimize systems and processes, addressing the demands of an ever-changing industrial landscape. From its historical roots in work organization to its current role in Industry 4.0 and the emerging Industry 5.0 paradigm, IE has remained central to fostering innovation, efficiency, and sustainability. Industry 4.0 has revolutionized industrial systems through the integration of Cyber-Physical Systems (CPS), the Industrial Internet of Things (IIoT), and advanced data analytics, enabling real-time decision-making and resource optimization. Building on this foundation, Industry 5.0 shifts the focus to human-centric, ethical, and sustainable practices, leveraging advanced technologies such as cognitive digital twins, collaborative robots, and resilient systems to enhance human-machine collaboration and environmental responsibility. This study explores the evolution of IE, its foundational principles, and its critical role in addressing modern industrial challenges. It highlights strategies for advancing the IE profession and academic programs, ensuring their relevance in the digital era. Additionally, it identifies six future research directions, including Human-AI collaboration, Adaptive and resilient systems design, advanced sustainability models, ethical and inclusive systems design, digital twin integration, and quantum computing, as key enablers for driving innovation and achieving global sustainability goals. By bridging the technological advancements of Industry 4.0 with the human-centric and sustainable objectives of Industry 5.0, IE is positioned to lead the transformation of industrial systems, fostering a resilient, inclusive, and sustainable future.
... There is significant relationship between Continuous Improvement and Organizational Performance 2.6 Hospital Management Information System (HMIS) and Organizational Performance Integrating Total Quality Management (TQM) and Hospital Management Information Systems (HMIS) has significantly enhanced healthcare quality and operational efficiency (Amer et al., 2022;Sahoo et al., 2024). TQM emphasizes continuous improvement through systematic, datadriven approaches, while HMIS provides the essential infrastructure for data collection, management, and analysis (Bongomin, 2025;Gupta, 2024). Studies have shown that this integration improves patient outcomes, enhances operational efficiency, and increases employee satisfaction (Alzoubi, 2023;Azam, 2011). ...
This study investigates the mediating role of Quality Consciousness between Total Quality Management (TQM) practices and organizational performance (OP) in healthcare settings within the Union Territory of Jammu and Kashmir. Utilizing a quantitative cross-sectional research design, 271 respondents, including doctors, nurses, paramedics, and administrative staff from NABH and non-NABH accredited public hospitals. Stratified random sampling ensured a representative sample. The study employed a five-point Likert scale to measure constructs such as Top Management Commitment (TMC), Customer Focus (CF), Employee Focus (EF), Continuous Improvement (CI), Quality Awareness (QA), Hospital Management Information System (HMIS), Quality Consciousness (QC) and Organizational Performance (OP). Data analysis was conducted using Partial Least Square Structural Equation Modeling (PLS-SEM) in Smart-PLS. The findings indicate that Quality Consciousness has a mediating effect on Organizational Performance. EF emerged as the most influential factor, with the highest beta value, followed by CF and TMC. Indirect effects demonstrated that variables like CF, CI, and HMIS also significantly impact OP through mediator, confirming the multifaceted nature of these relationships. Limitations include the context-specific focus on healthcare, sample size, potential measurement biases, and the cross-sectional design, which limits causal inferences. Future research directions suggest expanding studies to different contexts, employing longitudinal designs, refining measurement scales, and exploring additional mediators and moderators. These insights underscore the need for a holistic approach to understanding the dynamics between TQM practices and organizational performance in healthcare settings.
Climate change is pushing manufacturing companies to adopt sustainable solutions for reducing their carbon emissions. Circular Economy emerged as a suitable strategy for the decarbonization of industrial organizations, offering the potential to decouple economic growth from natural resource extraction and waste generation. However, achieving circularity requires significant transformation in several areas, and some Circular Economy actions may be more effective than others in reducing carbon emissions, depending on the product and company carbon footprint structure. Facing low awareness and limited resources, manufacturing companies frequently fail in understanding where to start in approaching such a systemic transition. Despite these challenges, the literature overlooks the linkages between Circular Economy initiatives and their potential for reducing carbon emissions, and in particular how Circular Economy actions can be prioritized for decarbonization purposes. To fill these gaps, this paper develops an original and systemic tool (C-Readiness) for assessing the readiness of manufacturing companies for the Circular Economy, and for prioritizing Circular Economy actions for decarbonization. The tool is developed based on a literature review and critical comparison of existing tools for assessing circularity readiness at the micro level. The tool has been applied to four manufacturing companies, to showcase its potential in designing Circular Economy-based decarbonization paths. This paper contributes to the literature on strategic Circular Economy implementation in manufacturing companies by integrating circularity readiness evaluations with quantitative carbon footprint assessments. It provides a structured approach and a simple yet effective tool to help industrial organizations reduce their environmental impact through Circular Economy practices.
The Fourth Industrial Revolution (Industry 4.0), characterized by the integration of advanced digital technologies and intelligent systems, presents a transformative opportunity for achieving sustainable manufacturing. This paper provides a comprehensive analysis of how Industry 4.0 technologies, such as artificial intelligence, the Industrial Internet of Things, blockchain, digital twins, big data analytics, advanced robotics, and additive manufacturing, can be leveraged to foster environmentally responsible, socially equitable, and economically viable industrial practices. The study examines the impact of these technologies across diverse industrial sectors and evaluates their effects on the three pillars of sustainability: environmental, economic, and social. Specifically, the study identifies the manufacturing processes that most benefit from Industry 4.0, including resource efficiency, waste minimization, and supply chain optimization. It identifies key areas of manufacturing that stand to benefit, such as resource efficiency, waste reduction, and supply chain optimization. The analysis reveals that the enhanced connectivity and real-time data processing capabilities of Industry 4.0 technologies - such as real-time data acquisition, predictive maintenance and closed-loop manufacturing - improve supply chain transparency, enhance operational efficiency, reduce energy consumption, and promote the circular economy - leading to positive economic, and environmental outcomes. These advancements drive positive social outcomes, including improved workforce safety, the creation of new skill-based jobs, and more equitable access to digital infrastructure. However, the study also highlights that while these technologies can improve resource efficiency and reduce environmental impacts, their widespread implementation may also intensify social challenges, such as job displacement and inequality, and exacerbate environmental trade-offs, particularly in energy consumption. To navigate these complexities, the study proposes an integrative framework that encompasses technological, cultural, and policy dimensions, providing a valuable roadmap for aligning Industry 4.0 advancements with robust governance mechanisms and fostering a culture of sustainability within organizations and society at large.
Purpose: This research aims to explore the intricate dynamics influencing the adoption of AI-driven smart tourism technologies within the specific context of the United Arab Emirates. It seeks to address the following key questions: How do ethical AI practices and personalization impact tourist behavior in the adoption of these technologies? What role does trust dynamics play in mediating these relationships? Additionally, how does the perceived value of smart technologies in the tourism experience moderate these associations? Method: By employing a quantitative approach, data was collected from a sample comprising 319 tourists in the UAE. The research methodology relied on Stata SEM for analysis. This method facilitated the assessment of intricate and interdependent relationships between multiple constructs. Utilizing validated scales, the present study ensured a comprehensive evaluation of the proposed model, encompassing ethical AI practices, person-alization, trust dynamics, perceived value, and tourist behavior in the adoption of AI-driven smart tourism technologies. Findings: The findings are aligned with the hypotheses posited in the study. Ethical AI practices exhibit a significant impact on influencing tourist behavior in the adoption of AI-driven smart tourism technologies. Similarly , personalization was identified as a significant determinant in shaping tourist behavior in technology adoption within the tourism domain. Moreover, the study found that trust dynamics play a pivotal mediating role in the relationships between ethical AI practices/personalization and subsequent tourist behavior. Additionally, the perceived value of smart technology in the tourism experience plays a crucial moderating role, influencing the strength and direction of the aforementioned relationships. Originality/Implications: This research uncovers the complex dynamics of AI-driven smart tourism technology to improve tourism theory and practice. The findings emphasize the importance of ethical concerns and user-centric design principles in technology development and implementation, as well as trust dynamics and perceived value in technology adoption. These findings affect tourism service providers, governments, and technology developers looking to improve user experiences and innovate.
Context:
IIoT, Industry 4.0 or CPPS software platforms are cornerstones of smart manufacturing production systems. Such platforms integrate machines, IIoT and edge devices, realize distributed (management) functionality and provide the basis for user-defined IIoT applications. Individual instances in research and industrial practice do share commonalities while they also differ significantly.
Objective:
A detailed overview of the platform landscape is fundamental for innovative research. However, actual surveys and literature reviews concentrate on specific aspects and usually focus only on the research works, neglecting specific aspects of the industrial use of IIoT platforms. We aim at a systematic overview of the functionalities and approaches of scientific and industrial IIoT platforms along 16 analysis dimensions and thereby exposing gaps between the focuses of research on IIoT platforms and actual industrial IIoT platforms in use. By doing so we are able to highlight future areas of interest to research as well as indicating potentially over-researched areas which are of less interest in actual industrial IIoT platforms.
Method:
We combine a systematic literature review of scientific IIoT platform research with a systematic analysis of industrial IIoT platforms.
Results:
We start off with 1620 research papers plus 70 from snowballing that we systematically filter down to 36 papers (plus 11 added by a SLR update) providing sufficient information for a data extraction, which we analyze along 16 topics to extract actual capabilities and differences of relevant platform approaches. In a second step, we contrast these results with an analysis of 21 industrial platforms.
Conclusion:
Similar approaches, differences and topics for future are exhibited. In comparison with 21 industrial platforms along the same analysis topics, we distill various commonalities, differences, trends and gaps.
Supply chain management (SCM) and total quality management (TQM) represent two parallel approaches to improving organizational performance. Previous studies have analyzed the SCM features impacting organizational performance independently of the design of TQM practices. Similarly, the recent literature on SCM overlooks TQM when incorporating lean and agile elements in SCM practices and strategies. Scattered attempts have been made to evaluate lean and agile SCM practices and TQM action plans, but no concrete integrated approach has been developed. In particular, no study has presented a structured analysis to quantify the influence of the SCM components on achieving predetermined TQM goals. We propose a comprehensive, four-phase, integrated procedure with a total score to quantitatively assess lean and agile SCM to fill this research gap. This comprehensive score selects the most suitable TQM action plan for increasing productivity and sustainability. The four phases of the proposed procedure include an initial qualitative analysis for defining indicators, sub-indicators, and characteristics of the lean and agile approach (Phase 1), the formulation of a mathematical model for computing the total score of the whole SCM approach (Phase 2), the selection of an action plan to achieve TQM goals (Phase 3), and the validation of the proposed framework and the results obtained (Phase 4). We show the applicability of the proposed evaluation procedure in a real-life case study and demonstrate that the general formulation of the mathematical model allows for extensions of the proposed method to other evaluation contexts.
Achieving a balance between reducing environmental impact and ensuring economic viability is essential for Originals Equipment Manufacturers adopting circular product strategies. A critical aspect of this process is determining the optimal time to take a product out of operation to facilitate the implementing of an appropriate circular strategy. However, decision-makers often lack the tools and methods to effectively answer this question, making it difficult to define and implement a strategy that is both ecologically sound and economically viable. This article presents a methodology that uses the State of Health of a product as a key indicator to identify the most advantageous year for taking back the product and applying a specific circular process (i.e., reuse, remanufacturing or recycling). Based on the State-of-Health, the environmental impact and total cost of the product are calculated annually over its lifetime using defined parameters. A circular strategy is proposed for each take-back year, determined by State of Health thresholds for various circular processes. To determine the optimum take-back year, an algorithm is applied, using a weighted sum as the objective function. This methodology is implemented within a dynamic circular strategy simulation tool to support decision-making for stakeholders. The approach is demonstrated using a case study on a circuit breaker in Genset and hospital applications. The results show environmental and economic benefits of up to 7 % and 18 %, respectively, highlighting the potential value of using this circular strategy simulation tool.
Human Digital Twins (HDTs) are an emerging concept with the potential to create human-centric systems for Industry 5.0. The concept has rapidly spread to new application domains, most notably Healthcare, leading to diverging conceptual interpretations. This Systematic Literature Review analyses the conceptual understanding of HDTs across all application domains to clarify the conceptual foundation. Our review reveals a consensus that an HDT’s twinned entity is a human individual. However, there is little agreement on the data flows between the individual and their HDT. We address this shortcoming by proposing three categories based on the level of data integration: Human Digital Models, Human Digital Shadows, and Human Digital Twins. Finally, we synthesise our findings in a domain-agnostic general definition for HDT. We highlight an edge case where the twinned entity is a human individual alongside a strongly coupled technical system, and name it augmented Human Digital Twin (aHDT). The definition and categorisation scheme provide the needed conceptual clarity for interdisciplinary collaboration to address open challenges. Notable challenges are sensing human data, reliable data transfers and modelling, especially behavioural modelling. Additional ethical issues concerning security, privacy and consent are central to successful HDT adoption. We call for cross-disciplinary efforts to establish a standardised framework and ethical guidelines to enable future developments.
Modular construction is an innovative approach that shifts much construction work offsite for manufacture in a factory. By balancing flexibility and productivity, human–robot collaboration (HRC) offers significant advantages in modular construction manufacturing. To ensure human wellbeing in this scenario, it is essential to conduct holistic ergonomic evaluations of HRC workstations. This research, therefore, introduces a novel approach for evaluating ergonomics using virtual reality (VR) during the HRC workstation design phase. To demonstrate feasibility and effectiveness of the approach, we present a case study involving workstation design for building module unit window installation. The results show that the designed HRC workstations can contain both physical and cognitive ergonomic risks within acceptable ranges. This study enriches the literature on modular construction manufacturing by providing a methodological framework that integrates VR into ergonomic HRC workstation design and assesses it through a series of measures. It also provides practitioners with clear guidance for mitigating ergonomic risks during the HRC workstation design stage, thereby reducing injuries and increasing the well-being of workers.
Navigating the complexities and uncertainties of the modern business landscape requires resilient supply chains to ensure long-term viability and success. Referring to Industry 5.0 principles, Supply Chain Resilience must accommodate the characteristics of adaptability, sustainability, customer centricity, and innovativeness to accommodate the ambidextrous characteristics. The present study aims to address its objectives by examining current trends and themes in Supply Chain Ambidexterity (SCA) and exploring the intricate relationship among Supply Chain Ambidexterity, Resilience, Agility, and Digital Transformation while also identifying possible directions for future research. Through a bibliometric analysis of 290 articles from the Web of Science (WoS) and Scopus databases until August 2024, complemented with a systematic literature review of 77 articles, this research proposed an integrated research framework. It reveals a significant relationship between Digital Transformation, Dynamic capabilities, and their role in enhancing SCA, with Digital Transformation as an influencing factor. The research highlights the influence of SCA on resilience and agility empowered by Digital Transformation. This research also proposes ten propositions based on the systematic literature review and identifies research gaps for each proposition. This study is positioned at the nexus of bibliometric analysis and systematic literature review to identify research trends and provide a s an overall comprehensive overview of SCA in the context of Dynamic Capabilities, Digital Transformation, Supply Chain Resilience, and Agility. Its findings present the current state of research in SCA and, therefore, guide future academic endeavors in the dynamic arena. This research serves as a significant resource by pinpointing existing research gaps, delineating future directions, and providing practical recommendations for implementation within the industry.
Electronic waste (e-waste) is the fastest-growing type of solid waste. According to the United Nations (UN), e-waste costs the global economy around $37 billion annually. Indeed, e-waste impedes UN Sustainable Development Goals (SDGs). Toward sustainable e-waste management, Industry 5.0 (I5.0) can offer a comprehensive solution by integrating the core principles of sustainability, resilience, and human-centricity into the technological advancements of Industry 4.0 (I4.0). However, no earlier study has systematically investigated the link between e-waste mitigation and I5.0. To bridge this crucial gap, an evaluation of e-waste Mitigation Strategies (MSs) grounded on I5.0 enablers is conducted in this research endeavor. Initially, ten e-waste MSs and fifteen I5.0 enablers are formulated, with a foundation on pertinent policies and existing literature. Then, an integrated scenario-based Best-Worst Method (scenario-based BWM) and Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje (F-VIKOR) framework is proposed for decision-making. By harmonizing fuzzy and scenario-based methodologies, this novel framework proficiently navigates both epistemic uncertainties and randomness. The scenario-based BWM analyzes the I5.0 enablers to determine their respective weights, while the F-VIKOR method is deployed to assess the e-waste MSs through the lens of the I5.0 enablers' relative significance. A case study conducted in India applied the proposed approach, showing ‘sustainability’ and ‘control emission’ as paramount I5.0 dimension and enabler, respectively. Notably, the MS 'adoption of take-back practices at top management' emerges as a linchpin for fostering sustainable e-waste management practices. The research findings are elucidated through an economic lens and juxtaposed with existing studies, with sensitivity analysis reinforcing the credibility of the results. The implications of this study offer practical insights and policy considerations crucial for all key stakeholders within the e-waste management ecosystem.
Additive manufacturing (AM), particularly the fused filament fabrication (FFF) process, enables the production of personalized products with unique features. However, the FFF process is prone to issues such as nozzle clogging, which can degrade print quality or cause print failure. Data-driven approaches present viable solutions for real-time monitoring and defect identification in AM, enhancing both the precision and reliability of the FFF process. Despite these advantages, practical deployment faces obstacles including limited availability of high-quality data, significant labeling costs, and the rarity of anomalous data. While similar data may exist across other AM manufacturers or machines, data centralization and sharing are often constrained by privacy and competition concerns. This paper introduces FULAM, a personalized federated unsupervised learning method designed to detect anomalies in FFF machine vibration data. The framework addresses critical challenges such as data privacy, heterogeneity, and l
Mobile manipulators are increasingly deployed in industrial settings, such as material handling and workpiece loading, where they must safely interact with humans while efficiently completing tasks. Existing motion planning methods for mobile manipulators often struggle to ensure both safety and efficiency in dynamic human-robot interaction environments. This paper proposes a Safety Posture Field framework that addresses these limitations by firstly predicting human motion trends using the improved Long Short-Term Memory neural network and applying these predictions to potential field calculations for both the mobile platform and the
robotic arm. During different stages of human-robot interaction, the mobile manipulator places varying emphasis on safety and efficiency while in motion. Additionally, when the robotic arm executes operations, a platform-arm coupling motion strategy is introduced when the potential field detects risks of singularity or local optima, preventing the robotic arm from becoming unstable or failing to reach the target pose in time. This strategy enhances the system’s flexibility and operational stability. Comparative experiments in simulation and real-world settings confirm the ability of the framework to maintain high safety standards while improving task efficiency, making it suitable for industrial Human-Robot Interaction applications.
Robots are taking on a prominent role in driving organizational evolution toward industrial revolutions. While research on the role of robots in human resource management (robot-HRM) is proliferating, the literature falls short in providing a state-of-the-art overview of the progress and ways forward for the field. Hence, this study aims to review and consolidate the extant literature on robot-HRM into a unified framework and provide pragmatic ways forward. To do so, this study conducts a framework-based systematic literature review by adopting the SPAR-4-SLR protocol to guide its assembling, arranging, and assessing of theories, contexts, characteristics, and methods (TCCM) of robot-HRM studies identified and retrieved from Scopus and Web of Science. In doing so, this study contributes a seminal overview of the research trends and ways forward for robot-HRM, as well as the implications for professionals to manage the embedding of robots and the interaction with employees in the workplace.
In 3D printing of critical structural components made from continuous carbon fiber-reinforced composites (CCFRC), mechanical performance and manufacturing efficiency are mutually constrained. This paper introduces a novel closed-loop iterative optimization method that swiftly identifies the optimal balance between performance and efficiency for the best overall results. It combines the forecasting capability of Convolutional Neural Networks (CNN) with the optimization strength of Non-dominated Sorting Genetic Algorithm II (NSGA-II). The study found that the optimal parameters as a layup angle of 0°, nozzle temperature of 260 °C, fiber filling density of 80 %, layer thickness of 0.6 mm, and fiber printing speed of 10 mm/s. The results of the optimized process parameters show a 53 % increase in mechanical performance and a 27 % improvement in manufacturing efficiency compared to the sampling experiment results. Therefore, the proposed parameter optimization strategy can quickly determine the optimal process parameters for the given conditions without requiring additional guidance.
In the context of human-centric smart manufacturing, human-robot collaboration (HRC) systems leverage the strengths of both humans and machines to achieve more flexible and efficient manufacturing. In particular, estimating and monitoring human motion status determines when and how the robots cooperate. However, the presence of occlusion in industrial settings seriously affects the performance of human pose estimation (HPE). Using more sensors can alleviate the occlusion issue, but it may cause additional computational costs and lower workers' comfort. To address this issue, this work proposes a visual-inertial fusion-based method for HPE in HRC, aiming to achieve accurate and robust estimation while minimizing the influence on human motion. A part-specific cross-modal fusion mechanism is designed to integrate spatial information provided by a monocular camera and six Inertial Measurement Units (IMUs). A multi-scale temporal module is developed to model the motion dependence between frames at different granularities. Our approach achieves 34.9 mm Mean Per Joint Positional Error (MPJPE) on the TotalCapture dataset and 53.9 mm on the 3DPW dataset, outperforming state-of-the-art visual-inertial fusion-based methods. Tests on a synthetic-occlusion dataset further validate the occlusion robustness of our network. Quantitative and qualitative experiments on a real assembly case verified the superiority and potential of our approach in HRC. It is expected that this work can be a reference for human motion perception in occluded HRC scenarios.
To increase the deposition efficiency and extend the working range of a single actuator, multi-robot collaborative wire arc additive manufacturing (MRC-WAAM) is proliferating in industry. Implementing an MRC-WAAM system should ensure precise transformations between a part frame and multiple robot work frames (RWFs) to facilitate fabrication quality. This paper proposes a novel calibration method for calculating the spatial correlation of RWFs by registering readings of multiple robot-carried 3D sensors. After formulating the influence of both translational and rotational errors, the principles and implementation are presented. To deal with the heterogeneity of cross-source point cloud models, an improved iterative closest point algorithm is articulated. The results of validation show that the proposed method can achieve high accuracy in orientation consistency of RWFs with an error of 0.091 deg, which is superior to the current state-of-the-art. This method can alleviate the consequences caused by the inconformity of multi-robot frames.