This article investigates the synergistic integration of Large Language Models (LLMs) and Digital Twins (DTs) to enhance production logistics and assembly operations in manufacturing. Grounded in the principles of Industry 5.0, we adopt a scenario-based qualitative methodology supported by a structured evaluation framework to explore how this fusion can drive new capabilities and value across industrial systems. We examine macro-level application areas, such as workforce training, human-machine interfaces, and quality assurance, and micro-level areas including adaptive scheduling and intra logistics coordination. The analysis highlights key opportunities, including semantic interpretability, real-time decision support, and context-aware automation, while also identifying critical challenges such as hallucinated outputs, cybersecurity risks, and computational constraints. To support comparative insights, we synthesize the capabilities and value propositions of LLMs across application areas in a structured summary table. By articulating the transformative potential and limitations of LLM-DT convergence, the article offers strategic guidance for phased implementation and emphasizes the need for hybrid, explainable, and human-centered AI systems in manufacturing.

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.

Circular economy has gained increased attention in both academia and industry. One of the main hurdles in implementing viable circular manufacturing systems in industry is the anticipation of future economic and environmental benefits in a circular supply setting, combined with product design decisions. Although circular practices, such as remanufacturing and reuse are often beneficial for original equipment manufacturers in closing the loop and realising business benefits, it is enormously challenging to match early design decisions at a single component level to future unknown recovery operations. Therefore, this paper presents a mixed agent-based and discrete-event simulation model to quantify economic and environmental impacts resulting from chosen circular design strategies. The feasibility of the model is tested using a case study product of an electric machine from the heavy-duty vehicle industry. Results show that an additional design investment of 10.6% can reduce the average cost of an electric machine by 18.6%, material demand by 14.7% and cradle-to-gate impact by 38.7% on average. In the context of electric machines, the most sensitive parameters of the circular supply chain are the success rate of remanufacturing and reuse operations, the lifetime length and the degradation speed.

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.

This study investigates the readiness and barriers to Artificial Intelligence (AI) adoption among manufacturing Small and Medium-sized Enterprises (SMEs) in Northern Thailand, aiming to support sustainable innovation and inclusive digital transformation. A mixed-method approach was employed, combining the Analytic Hierarchy Process with expert validation using the Content Validity Index to prioritize ten enabling factors and twelve key barriers to AI adoption. To capture heterogeneity among SMEs, K-Means clustering and Principal Component Analysis were applied, resulting in four distinct readiness profiles. The analysis reveals that business process maturity, strategic leadership, and technology alignment are the most influential enablers, while financial constraints, workforce skill shortages, and poor data quality represent major obstacles. Based on these insights, the study proposes a four-phase AI adoption roadmap—awareness, capacity-building, implementation, and innovation leadership—tailored to diverse SME profiles. These findings offer actionable implications for SME owners, policymakers, and development agencies seeking to promote responsible and scalable AI integration in regional economies.

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 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.

The integration of artificial intelligence (AI) into the manufacturing sector introduces new challenges and demands for the engineering workforce in the evolving European economy. This paper investigates how advancements in AI tools, especially in manufacturing, necessitate a shift in engineering education to equip graduates with relevant skills and ethical understanding. While AI is not new to manufacturing, its ongoing development and increased accessibility bring forth fresh challenges related to required competencies and ethical considerations. Furthermore, this work explores the potential of incorporating recent AI tools, such as ChatGPT and other generative adversarial networks, into engineering education. This is illustrated through a case study of a master’s level digitalization course. In this course, AI tools aimed to help students bridge their programming knowledge gaps and educate them on ethical AI use, providing a model adaptable to lifelong learning courses in the field. This inquiry also addresses the broader concerns related to AI misuse in academic settings and the subsequent difficulties in plagiarism detection and accurate learning outcome assessment. The discussion does not argue against AI adoption but emphasizes managing its inadvertent impacts on the industry and society. By integrating emerging technologies and their ethical use into the curriculum, the engineering education system can better align with the shifting demands of the workforce in an increasingly digitalized manufacturing landscape.