The manufacturing industry is undergoing a profound transformation toward smart, digital, and flexible production systems under the Industry 4.0 framework. Within this paradigm, Digital Twin (DT) serves as a key enabler, bridging physical and digital domains to simulate, analyse, and optimise manufacturing operations. Concurrently, robotic systems, enhanced by smart sensor perception, Industrial Internet of Things connectivity, and adaptive control mechanisms, are increasingly deployed to handle complex and dynamic tasks. However, the evolving demands of the modern manufacturing industry require a high degree of flexibility and responsiveness, necessitating more intelligent solutions. The Robot Digital Twin (RDT) has emerged as a transformative approach, facilitating dynamic adaptation and continuous operational improvement. This review offers a comprehensive examination of the literature on RDT in manufacturing from both technology and application perspectives, aiming to provide insight for researchers and practitioners in Industry 4.0. The paper introduces a four-layer RDT system architecture and summarises how Industry 4.0 technologies, e.g., the Industrial Internet of Things, Cloud/Edge Computing, 5 G, Virtual Reality, Modelling and Simulation, and Artificial Intelligence, converge and influence the RDT system based on this architecture. Furthermore, the review covers domain-specific and system-level applications, such as assembly, machining, grasping, material handling, human-robot interaction, predictive maintenance, and additive manufacturing systems, with an analysis of their development status. Finally, the trends, practical challenges, and future research directions for RDT systems in manufacturing are summarised at different levels.

Digital services play a crucial role in enhancing manufacturing competitiveness by enabling differentiation, flexibility, customisation, and improved performance through advanced technologies such as the Industrial Internet of Things and Cyber-Physical Systems. While data-driven production logistics (PL) increasingly adopts these technologies to optimise operations, challenges persist in effectively leveraging digital services to enhance performance. This study proposes a value-oriented digital service framework for data-driven PL, integrating principles of value proposition development and value creation. Complemented by a system architecture, the framework identifies the defining characteristics of value-oriented digital services and provides a blueprint for effective performance monitoring. Empirical results from a case study on material handling in commercial vehicle manufacturing demonstrate significant improvements in PL performance metrics, including enhanced on-time delivery and energy efficiency. This study offers practical guidance for managers seeking to design and implement digital services that enhance operational monitoring and decision-making in data-driven PL.

Digital technologies are critical in advancing a human-centered approach to warehouses that account for productivity and staff well-being. These technologies generate data addressing the negative conditions affecting the well-being of staff during order picking (OP), a labour intensive activity. This study analyzes artificial intelligence (AI)-enabled vision systems to enhance human-centricity and improve the generation and analysis of information about tasks executed by staff in OP. The study presents results from a pilot study in automotive manufacturing applying a design science research approach. The results show that AI-enabled vision systems enhance task identification, analysis, and efficiency in OP. The study suggests five actions including staff information, data acquisition, access restriction, data storage, and protection addressing the privacy concerns of these systems. The study discusses how these systems can integrate staff well-being by identifying human factors and outcomes. It offers three contributions: (1) an overview of activities for collecting task information through AI-enabled vision systems in human-centered OP; (2) evidence that existing architectures for human-centered manufacturing are essential for managing privacy implications; and (3) a discussion of the systems’ impact on human factors and performance, and guidelines for developing and implementing these systems in future studies and operational environments.

This study investigates standardization of data acquisition, analysis and integration according to ISO 23247 for real-time visualization and interoperability in manual order picking processes within the automotive industry. This study addresses fragmented and inconsistent production logistics (PL) data and enhances real-time performance visualization by establishing a structured PL information model based on the ISO 23247 framework. The proposed approach integrates manufacturing data such as order, product, and operator data with PL specific elements including transportation, time, and material. This integrated information model ensures consistent, scalable, and reliable information data flow in PL systems. The practical applicability and validity of the proposed information model are demonstrated through a case study on manual order picking, highlighting its ability to integrate PL activities and support synchronized data exchange within dynamic production environments. The findings contribute to the broader goal of digital transformation in production and logistics by addressing key challenges in data consistency, system integration, and real-time monitoring. Furthermore, the standardized information model developed in this study can be extended to other industrial sectors, such as warehouse automation, to improve production and logistics integration through a unified information model.

Recent publications underscore the critical implications of adapting to dynamic environments forenhancing the performance of material and information flows. This study presents a systematicreview of literature that explores the technological capabilities of smart production logistics (SPL)when applying machine learning (ML) to enhance logistics capabilities in dynamic environments.This study applies inductive theory building and extends existing knowledge about SPL in threeways. First, it describes the role of ML in advancing the logistics capabilities of SPL across variousdimensions, such as time, quality, sustainability, and cost. Second, this study demonstrates the applicationof the component technologies of ML (i.e. scanning, storing, interpreting, executing, andlearning) to attain superior performance in SPL. Third, it outlines how manufacturing companiescan cultivate the technological capabilities of SPL to effectively apply ML. In particular, the studyintroduces a comprehensive framework that establishes the technological foundations of SPL, thusfacilitating the successful integration of ML, and the improvement of logistics capabilities. Finally,the study outlines practical implications for managers and staff responsible for the planning andexecution of tasks, including the movement of materials and information in factories.

The management of assembly tasks within manufacturing, which traditionally relies on using stopwatches and video review, is both labour-intensive and prone to errors. This paper explores an approach utilizing machine learning (ML) and human pose estimation technologies to automate and enhance the classification and management of time blocks for manual assembly tasks in manufacturing environments. We developed and tested ML models capable of classifying manual assembly actions by converting video clips into a time series coordinate dataset via a human pose estimation library. The research highlights the potential of these technologies to significantly reduce the reliance on manual methods by providing a more adaptable, efficient, and scalable system for time data management. Our findings demonstrate accuracy variances across different actions, underscoring the challenges and potential of integrating ML in real-world manufacturing settings. This study provides a promising direction towards revolutionizing traditional practices and enhancing operational efficiencies in manufacturing.

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer agents. All agents are self-interested and rational with the aim of maximizing their own objectives, resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private information. Within the context, a centralized scheduling approach is unfeasible, and a decentralized approach is considered to deal with the targeted problem. This study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents' preferences under incomplete information. For this purpose, a dynamic iterative auction-based approach based on a decentralized decision-making procedure is developed. In the proposed approach, a dynamic auction procedure is established for dynamic jobs participating in a realtime auction, and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid determination. In addition, an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently. A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting bids. Extensive computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on largescale problems with numerous consumer agents and jobs. A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

This study applies an intersectional approach to address concerns aboutdiversity of data acquisition when applying computer vision systems in humanorder picking. The study draws empirical data from a single case study conductedat an automotive manufacturer. It identifies critical factors of intersectionality forthe use of vision systems to enrich data collection in human order picking at fourlevels including form and function, experience and services, systems and infrastructure,and paradigm and purpose. These findings are helpful for mitigating biasand ensuring accurate representation of the target population in training datasets.The results of our study are indispensable for enhancing human-centricity whenapplying computer vision systems, and facilitating the acquisition of unstructureddata in human order picking. The study contributes to enhancing diversity in humanorder picking, a situation that is highly relevant because of the variations in age,gender, cultural background, and language of staff. The study discusses theoreticalandmanagerial implications of findings, alongside suggestions for future research.

This paper introduces a framework that integrates smart production logistics (SPL) with network diagrams. This integration enhances visibility in the material and information flow within the manufacturing sector, thereby adding value through data visualization. Drawing from a detailed case study in the automotive industry, we outline the essential components of network diagrams that are tailored to depict spatial-temporal data linked with material handling processes in an SPL context. This integrated approach presents managers with a new tool for optimizing planning and executing tasks related to the transport of materials and information. Furthermore, while the framework brings about significant technological progress, it also emphasizes the managerial implications of SPL data visualization. In particular, it highlights its potential to foster informed decision-making, resource optimization, and strategic forecasting. The paper also discusses prospective research avenues, stressing the importance of dynamic diagrams that decode complex patterns from digital data and the incorporation of sustainability metrics in SPL assessments.

This paper introduces a framework that integrates smart production logistics(SPL) with network diagrams. This integration enhances visibility in the materialand information flow within the manufacturing sector, thereby adding valuethrough data visualization. Drawing from a detailed case study in the automotiveindustry, we outline the essential components of network diagrams that are tailoredto depict spatial-temporal data linked with material handling processes in an SPLcontext. This integrated approach presents managers with a new tool for optimizingplanning and executing tasks related to the transport of materials and information.Furthermore, while the framework brings about significant technological progress,it also emphasizes the managerial implications of SPL data visualization. In particular,it highlights its potential to foster informed decision-making, resource optimization,and strategic forecasting. The paper also discusses prospective researchavenues, stressing the importance of dynamic diagrams that decode complex patternsfrom digital data and the incorporation of sustainability metrics in SPL assessments.