Abstract
The rapid advancement of warehouse automation has increased the need for intelligent intralogistics solutions that enhance material handling efficiency and optimize space utilization. This research presents a simulation-based methodology that integrates Autonomous Mobile Robots (AMRs) with container loading optimization in a unified decision-support framework that dynamically synchronizes AMR routing with optimized truckload configurations, a feature not commonly addressed jointly in existing literature to improve warehouse operations. By leveraging a hybrid approach combining discrete event and agent-based simulation in FlexSim, the study evaluates the impact of AMR fleet size, routing strategies, and truckload configurations on overall logistics performance. A proof-of-concept industrial case study illustrates how different scenarios influence key performance metrics, such as total operation time and resource utilization. The findings demonstrate that synchronized AMR deployment and optimized container loading strategies contribute to increased throughput, reduced handling time, and enhanced logistics unit utilization. This work provides a framework for dynamic logistics planning, offering valuable insights for companies seeking to enhance warehouse efficiency and sustainability through simulation-driven decision support. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The adoption of Autonomous Mobile Robots (AMRs) has emerged as a promising solution to enhance efficiency and reduce operational costs for industrial companies. Given the significant cost of AMRs, it is crucial to determine the optimal number and characteristics before making significant investments. This study proposes a decision-support framework based on simulation to assess the impact of integrating AMR robots in a complex distribution center. Additionally, this framework aids decision-makers in determining the optimal fleet size of AMR robots and corresponding charging stations. A simulation model was developed using data from a leading retail company, focusing on pallet movement within the facility, comparing scenarios combining AMRs with other intralogistics implementations. This methodology incorporates uncertainty, variability (statistical distributions to create transportation orders, acceleration, demand and offer fluctuations) and implements fleet management, transportation capacity, demand matching, and resource utilization according to real case scenarios. The proposed model replicates accurate robot coordination and actual deployment environments, ensuring that the tested scenarios approximate the real-world conditions as much as possible. Preliminary findings show results supporting the decision-making for a fleet size to meet weekly production targets, optimize robot utilization, and coordinate charging instances to prevent production stops. Conclusions suggest that the proposed simulation approach is an effective tool for planning and implementing logistics solutions, enabling users to make informed decisions before investing.

Abstract
Circular Economy is based on implementation of R-strategies to narrow or close the loop of material flows and to minimize raw material consumption by extending the life cycle of materials. Since this approach is expanding from individual organizational actions to a collaborative approach, the objective of this paper is to analyse the role of digital technologies such as AI and cloud platforms in facilitating and changing the collaboration between stakeholders to improve sustainability. This study adopts a qualitative multi case study methodology, using surveys, interviews and document analysis from 10 new ventures in the agri-food ecosystem supported by the cascade funding programme. The results show that collaboration among actors is changed by the different technologies and strategic drivers of circular economy in the considered ecosystem.

Abstract
This study investigates the antifragility of organizations, especially in strategic sectors highly exposed to disruptive events. Based on a qualitative approach with case studies in the wine and textile sectors in Portugal, the findings indicate that financial and market strength, as resilience capabilities, operate interdependently and are reinforced by digital maturity and supply chain integration. Companies with financial robustness and strong market intelligence tend to be more agile in strategically investing and reallocating resources during crises. The research adopts an expanded definition of antifragility, which incorporates resilience, innovation, and strategic reconfiguration in the face of disruptions. It concludes that organizational antifragility results from the articulation of financial resources, market intelligence, and digital collaboration, offering a sustainable competitive advantage in the face of uncertainty. The study contributes to theoretical debates and provides practical recommendations for managers and policymakers.

Abstract
DIn the digital transformation of industrial sectors, data is a high-value business asset. How companies manage data between systems within the organization or through networks of business partners impacts their competitive factor. Technological maturity may imply several adversities, such as the lack of interoperability standards for simple and transparent data exchange. This paper presents an architecture that enables secure exchanges of supply chain orders between textile and clothing companies. This architecture is based on Electronic Business (eBIZ) 4.0 and International Data Spaces (IDS) frameworks, fostering trust and widespread adoption of platforms in the industry sector, particularly when handling sensitive supply chain information. The architecture was implemented and validated in 3 use cases with Enterprise Resource Plannings (ERPs) from the same vendor, different vendors, and communication from a ERP to a Web portal. Implementing the proposed architecture impacted efficiency, transparency, and accountability within the supply chain network. The lead times for purchases, provisioning, and the number of additional information requests in the ordering were reduced. In subcontracting, a reduction in non-conformities and an overall improvement in delivery times were verified. Moreover, logistics operations and communication with subcontractors were optimized, leading to faster order reception and reducing informal contacts.

Abstract
This project was developed to improve the cost estimation process of new products within the Product Development Department of a furniture manufacturer. This work involved developing a methodology using Machine Learning (ML) models trained on products' existing data to predict the cost of new innovative ones based on similarities and given data. The ML models used were Linear Regression (LR), Light Gradient-Boosting Machine (LGBM), Random Forest (RF), and Support Vector Machine (SVM). The proposed methodology considers the estimation of the total cost of producing a product, which encompasses both material and operational costs. Throughout this project, several analyses were developed to identify and evaluate different independent variables that could explain the behaviour of these two cost components. The suitability of the different variables was studied by applying several ML models, and a set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The proposed approach, which incorporates ML models into more complex variables to predict, resulted in a 19.29% reduction in estimation error.