Background: Emerging economies face a strategic imperative to transform their supply chains, aiming for economic resilience, environmental responsibility, and social inclusivity. Methods: This study, employing the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, identified and prioritized key drivers for this sustainable transformation. Results: Expert input from supply chain professionals in various industrial sectors revealed five critical factors: top management commitment, stakeholder engagement, regulatory alignment, technological capability, and internal integration. Notably, technological capability and internal integration emerged as the most influential causal drivers, acting as crucial enablers for deeper, systemic change. These findings offer actionable insights for decision-makers in designing more effective transition strategies for logistics and supply chain systems, especially amidst rapid industrialization and escalating sustainability pressures. Conclusions: This research significantly contributes to the growing literature on strategic supply chain redesign, providing practical guidance for firms navigating sustainability transitions within complex, resource-constrained environments.

Effective project management depends on a deep understanding of the human attributes that influence project success. This study aims to quantify the causal relationships between human resource variables in order to establish a prioritisation criterion for workforce allocation in industrial projects. Traditional statistical models often overlook the multidimensional nature of these factors, limiting their effectiveness in complex planning contexts. To address this, the Decision-Making Trial and Evaluation Laboratory method is used to assess and prioritise the key competencies required of project personnel. The analysis is based on an extensive literature review of management and industrial project studies, combined with data collected from experienced managers through structured questionnaires. Respondents assessed how different human resource attributes interact and influence each other. The results show that personal motivation, innovation, education, work–life balance, flexibility and adaptability are dominant causal factors. Stakeholder relations, conflict management, negotiation skills, objectivity and impartiality are more reactive. This study is differentiated in that it analyses a complex network of 400 influence relationships, providing a more comprehensive perspective than previous research. By integrating a structured decision-making approach, the results contribute to both the academic literature and practical applications, supporting more effective workforce planning and improved performance in industrial projects.

This study presents a comparative analysis of software platforms and computational methods used in the design of three-dimensional lattice structures and functionally graded materials (FGMs). Through systematic evaluation of 31 computational platforms across seven critical criteria (lattice type support, parametric control, conformal generation, multi-material capabilities, ease of use, FEA integration, and AM compatibility), this review identifies that specialized platforms significantly outperform general-purpose CAD tools, with scores exceeding 30/35 points compared to 15–20/35 for conventional systems. The analysis reveals that implicit and voxel-based representations dominate high-performance applications, while traditional boundary-representation methods approach fundamental limitations for complex lattice generation. Emerging machine learning-driven frameworks demonstrate 82% reduction in optimization iterations through Bayesian optimization and achieve property prediction speedups of nearly 100× compared to computational homogenization, enabling rapid inverse design workflows previously computationally infeasible. These insights provide researchers with evidence-based guidance for selecting computational approaches aligned with specific manufacturing capabilities and design objectives.

This study addresses a key challenge in Additive Manufacturing (AM): while it promises sustainable production, manufacturing defects often lead to significant material and energy waste. The purpose of this research is to apply the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify and map the cause-and-effect relationships among common AM defects. By doing this, the goal is to pinpoint the most influential ‘root’ causes, allowing for more targeted and effective quality improvements. The methodology is based on a qualitative approach using the expert judgment of a panel of six professionals. The DEMATEL analysis successfully sorted the defects into two categories: those that are primary causes and those that are symptoms or effects. The main findings show that contamination is the most significant causal factor, meaning that it strongly influences other defects. In contrast, dimensional inaccuracy is the most affected factor, acting as a symptom of other underlying issues. In conclusion, the study finds that focusing on mitigating root causes like contamination, warping, and porosity is crucial for achieving improvements across the process chain. This framework allows engineers to prioritize quality control efforts on the fundamental problems, rather than on superficial defects, thereby maximizing efficiency and waste reduction. Ultimately, this research provides a clear, actionable framework for improving quality control and promoting more sustainable manufacturing practices.