Abstract Ensuring consistent surface quality in friction stir welding (FSW) remains a challenge due to the complex interaction between process parameters such as axial force, rotational speed, and travel speed that govern heat generation and material flow. This study presents a data-driven and physically interpretable machine learning framework to predict weld surface quality directly from these parameters. Thirty robot-assisted FSW trials were performed on sand cast aluminium plates, varying axial force (4–7 kN), rotational speed (700–1700 rpm), and travel speed (180–300 mm min −1 ) at a constant tool tilt of 1°. The resulting welds were classified into four surface-quality categories: good weld, flash/burr, surface roughness, and surface void. Four models logistic regression, support vector machine, random forest, and artificial neural network were trained and validated using stratified five-fold cross validation. The support vector machine achieved the highest performance, with 81.8 % accuracy and a balanced precision recall score of approximately 0.77. Decision-boundary maps and calibrated probability predictions identified an optimal defect-free process window (≈ 4–6 kN, 1200–1500 rpm, ≥ 300 mm min −1 ), consistent with known thermo-mechanical behaviour in FSW. The proposed framework integrates predictive analytics with process physics, offering a deployable foundation for real-time, data-driven quality assurance in automated FSW.

Abstract In this paper, we report the findings from an extensive case study in a manufacturing SME. We have followed their strategic work and organizational development since Autumn 2022 and we are continuously monitoring and investigating their digital transformation endeavour, with a particular interest in the importance of a digital strategy and how the management group is executing the digital strategy. In addition, we also account for the development activities they have initiated and how they use the strategy as a beacon for e.g., prioritizations activities and resource, as well as subsequent fundamental changes they implemented in their value creating and supportive processes. The case study includes a rich empirical material, originating from many site visits, where more than 25 interviews have been conducted alongside many meetings/workshops with the board and management group, detailed studies of strategic documents, product portfolios and Gemba walks. The paper also relates our findings and contextualizes findings from our case study to a set of important strategic management capabilities being identified as part of the work process. Finally, we also discuss some lessons learned in relation to the actual implementation of the digital strategy and their strategic management capabilities as well as propose some ideas for future work, including the importance of considering the internal tensions caused by the digital transformation endeavour and manifested via the digital strategy.

Abstract This paper examines the adoption of Digital Product Passports (DPPs) as enablers of transparency, traceability, and circularity across supply chains. It aims to clarify what Antecedents, Barriers, Challenges, Drivers, and Effects shape DPP deployment in light of emerging EU regulations such as the Green Deal and the Ecodesign for Sustainable Products Regulation (ESPR). A systematic literature review was conducted to synthesize insights from journal papers published between 2020 and 2025. The review integrates the Technology–Organization–Environment (TOE) lens with the ABCDE Model to examine DPP evolution and implementation across industries. Findings show that antecedents include regulatory mandates, circular economy ambitions, and technological advancements. Barriers and Challenges relate to data fragmentation, interoperability gaps, organizational resistance, and inconsistent legislation. Drivers emerge from sustainability priorities, compliance requirements, and digital innovation. The resulting Effects include enhanced traceability, transparency, and resilience, yet issues persist around governance complexity and SME readiness. Sectoral comparisons reveal divergent regulatory timelines and data priorities spanning from technical standardization, consumer transparency, to repairability and recyclability. This paper contributes a systematic synthesis of the fragmented DPP literature, proposing the ABCDE Model as a comprehensive model for assessing socio-technical and regulatory readiness. Hence, offering the first integrated application of the ABCDE and TOE frameworks to DPP research. It highlights research gaps in interoperability, data governance, and human-centric integration, guiding future policy and practice toward Industry 5.0 objectives of sustainability, inclusivity, and resilience.