ABSTRACT. The large product variety of industrial make-to-order products presents challenges for supply chain management. Product configuration systems are software tools used for managing external variety with a pre-defined set of rules, and translating the customer requirements to bill-of-materials, production orders and purchase orders needed for the supply chain management. This paper outlines how artificial intelligence can be used to manage product variety in industrial supply chains. A prototype implementation of a product configuration model is presented.

ABSTRACT. The purpose of this manuscript is to examine the myths that exist in supply chain management (SCM) literature on sustainability that may be disserving the field, especially in the world of Industry 5.0, where economic structures are shifting rapidly. Through this examination, we identify three areas where SCM researchers can focus their efforts. Based on the literature review, in alignment with today's needs, we suggest building a research agenda by; (1) expanding the triple bottom line perspective, (2) extending research efforts to elevate the social aspect of sustainability, and (3) expanding the type and scope of entities we study. The paper provides deep insights about themes emerging in sustainability in SCM research, and the way TBL is defined is refined to be more inclusive of organization types (i.e., nonprofit organizations) as well as entities studied (i.e., service organizations). Research opportunities are identified to help carry sustainability research in SCM forward.

ABSTRACT. This study investigates the comprehensive transformation of Dacia's production and logistics since 1995, analyzing its evolution from a struggling local manufacturer to a global automotive powerhouse. We combine both qualitative analysis and quantitative methods to provide a holistic view of the company's journey, examining how the Logan X90 project, conceived as a simple and robust car, reshaped the entire operations’ modernization, whilst also serving as the catalyst for Dacia’s future models. By examining key production, capacity, and logistical data, we use descriptive statistics and linear regression models to empirically demonstrate the impact of Renault’s vision and strategic investments in Romania. Our findings reveal that Renault's modernization efforts were highly effective, leading to a significant increase in production capacity and output with high efficiency, as evidenced by a strong positive correlation between capacity and production. We show that the implementation of lean management principles and a renewed focus on supply chain management were critical for overcoming chronic quality issues and achieving operational excellence. The conclusion highlights how these foundational changes enabled Dacia to maintain remarkably stable production levels, even amid the more recent global crises. This paper offers a detailed case study on how a legacy manufacturer can achieve resilience and competitive advantage through a combination of strategic capital investment, cultural shifts, and operational rigor, combined with synergic effects of a leading automotive industry alliance’s logistics capabilities.

ABSTRACT. Simulation models are widely employed to evaluate complex systems and test new operational strategies with lower capital investment. Among emerg-ing technologies, Digital Twins (DTs) stand out by integrating simulation with real-time data to support dynamic decision-making. In oil and gas produc-tion, particularly within Intelligent Completion Systems (ICS), DTs enable advanced monitoring, predictive analysis, and proactive maintenance. The objective of this article is to present the initial results of a larger research pro-ject aimed at developing a Digital Twin model for the ICS to analyze well control operations for simulated scenarios. The first findings show the poten-tial of DTs as a strategic tool for increasing efficiency, reducing costs, and supporting supply chain resilience in high-risk industrial sectors.

ABSTRACT. Industry 5.0 is reshaping industrial ecosystems through human–machine collaboration, advanced analytics, and sustainable, human-centric practices. As skilled personnel are the main asset of this transformation, higher education plays a pivotal role in preparing graduates who combine technical expertise with adaptability, ethical awareness, and innovation. Artificial Intelligence, as a disruptive technology, is transforming both industrial operations and educational processes. This paper examines the integration of AI into learning activities in engineering programs at the Holon Institute of Technology (Israel) within a structured institutional research and development initiative. Using a large-scale student survey grounded in validated theoretical frameworks, we analyzed how such integration influences AI-specific, general cognitive, and specialized professional capabilities, as well as self-perceived employability. Subgroup comparisons, mediation analysis, and regression modeling mapped the interrelationships between AI integration, capability development, and employability, and identified the effects of moderating factors such as AI proficiency, program level, employment status, and workplace AI use. Findings show that employability gains are driven mainly by reinforcing feedback loops linking the three capability domains, with their strength shaped by key background conditions. The weak direct link between AI integration and employability underscores the importance of aligning AI-integrated pedagogy with applied, real-world engagement to sustain capability growth. The study addresses a gap in the literature on AI integration in education by modeling it as a dynamic, complex adaptive system and provides a framework for designing effective, scalable, and contextually grounded AI implementation in higher education—supporting the alignment of graduate skills with evolving Industry 5.0 workforce needs.

ABSTRACT. The forestry sector plays a crucial role in sustainable resource management, providing essential raw materials while striving to minimize environmental impact. In timber logistics, heavy-duty trucks transport logs through forested and mountainous regions are predominantly powered by fossil fuels, consuming significant amounts of energy and contributing to CO2 emissions. Given the industry's growing commitment to sustainability, powertrain electrification offers a promising way to enhance environmental responsibility while optimizing energy consumption. To analyse this potential, vehicle and GPS data are recorded from a truck transporting logs in Austrian forests. Based on this data, its energy consumption and annual emissions are calculated. Since this truck frequently traverses significant elevation changes, a simulation is conducted using an electric truck to evaluate energy recuperation potential. Finally, the ecological and economic benefits of electrification are assessed, comparing the electric truck's advantages to those of its diesel-powered counterpart.

ABSTRACT. Optimising in-plant logistics operations presents a considerable problem for current industrial facilities, with the objective of reducing material transit costs and duration. This research examines the implementation of a bio-inspired algorithm derived from the behavioural patterns of the slime mould Physarum polycephalum to address path optimisation challenges in the Calucem industrial process environment. The methodology involves virtual laboratory tests utilising Physarum polycephalum within a model derived from a documented real-world industrial setting, combined with comparative in silico optimisation and path visualisation. This approach primarily involves the creation and utilisation of a visualisation tool within the open-source Godot Engine. Initial literature findings suggest that Physarum polycephalum may serve as an effective instrument for identifying optimal logistic routes, whereas VR visualisation greatly enhances the comprehension, validation, and presentation of sophisticated optimisation processes.

ABSTRACT. Order picking is a labor-intensive process in warehouse operations, where even small efficiency gains can yield substantial productivity benefits. This study in-vestigates the effect of weight labeling on manual picking performance through two controlled experiments. In the first experiment, participant completed the tasks first with weight-labeled boxes and then without labeling. Weight labels used a traffic-light color scheme to indicate light, medium, and heavy categories. The labeled condition resulted in a shorter mean picking time compared to condi-tion without labeling, representing an 8.39% improvement. In the second experi-ment, the sequence was reversed to include both the benefit of labeling and prior task familiarity. Across four participants and 45 box configurations, labeling re-duced mean picking time by 15%. A detailed condition-level analysis revealed the greatest improvements for lightweight items located on high or low shelves, with gains exceeding 30% in some configurations. Minimal or negative effects were observed for certain heavy-item scenarios. These results indicate that weight la-beling is a low-cost, easily implementable intervention that can significantly im-prove picking speed, particularly for items in ergonomically challenging loca-tions, and may complement other warehouse efficiency strategies.

ABSTRACT. This study investigates the role of integrated digital technologies and structured, multi-layered decision-making in enhancing warehouse operations and productivity. By comparing two warehouses with different levels of technological adoption—focusing on Shuttle-Based Storage and Retrieval Systems (SBS/RS), Supervisory Control and Data Acquisition (SCADA), Warehouse Management Systems (WMS), Track-Pod, and the Trevio Warehouse platform—the research examines impacts on workflows, labor productivity, and process stability. A key focus is how strategic, tactical, and operational decision-making layers interact to support effective technology adoption. Using a two-stage methodology—qualitative case study analysis and quantitative factorial evaluation—the study provides a comprehensive assessment of technology impacts. Results show that warehouses using integrated digital technologies with data-driven, multi-level decision-making achieve significantly greater labor productivity, operational stability, and scalability. These findings underscore the importance of aligning technological investments with decision frameworks to maintain long-term competitiveness in complex logistics environments.

ABSTRACT. In recent years, Micro and Nano Fulfilment Centres have emerged in densely populated urban centres, which have extremely high volumetric efficiency and are highly automated. Micro Fulfilment Centres (from approx. 900 m2 to 4000 m2) and Nano Fulfilment Centres (approx. 75 m2) are designed to ensure the fulfilment of pre-prepared orders from Urban Consolidation Centres (UCCs) to end consumers. Orders can be fulfilled using conventional or automated delivery methods, with a focus on operational efficiency and sustainability. This paper presents a conceptual model for order fulfilment to end consumers using Nano Fulfilment Centres (NFCs). The main challenges related to the design and operation of NFCs for Smart Cities are presented. The study identifies various technologies that can be used in NFCs on an operational level (fully automated, semi-automated). Finally, it explains how these technologies influence the operational efficiency of NFCs.