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.

The strategic transformation of increasing data volumes into managerial decisions is critical for organisational performance and sustainability; yet, it faces hurdles like poor data quality, technological deficiencies, and skill gaps. This study investigates the causal interdependencies among key factors influencing data-driven decision-making within data-driven organisations. Utilising the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, a robust structural and Multi-Attribute Decision-Making (MADM) technique, expert judgments from five management-level professionals were analysed to construct direct and total-relation matrices. The results classify Data Analytics Literacy (DAL) and Business-Strategy Alignment (BSA) as primary causal factors, while Data Quality (DQ), Data Infrastructure & Technology (DIT), and Data Culture & Governance (DCG) emerge as effect factors. These findings provide a structured framework for prioritising managerial interventions, suggesting that strengthening foundational elements (DAL and BSA) will significantly enhance analytical capabilities and strategic alignment. A limitation is the small, expert-based sample, indicating the potential for future validation with larger, more diverse panels or Fuzzy-DEMATEL applications.

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.

The increasing volume of data necessitates its effective transformation into managerial decisions for organizational performance and sustainability within Business Process Management. However, challenges like poor data quality, technological deficiencies, cultural resistance, and skill gaps often hinder this crucial process; thus, a profound understanding of the causal relationships among influencing factors is essential to address these impediments. This study utilizes the Decision-Making Trial and Evaluation Laboratory method, a robust structural and multi-criteria analysis technique, to analyze complex causal interdependencies. DEMATEL quantifies expert judgments and maps cause-and-effect relationships, offering a systemic perspective distinct from other Multi-Attribute Decision-Making methods. We examine five key factors: data quality, data infrastructure & technology, data culture & governance, data analytics literacy, and business-strategy alignment. Expert data from five management-level professionals were used to construct direct and total-relation matrices, deriving influence and causality scores. The DEMATEL application will provide comprehensive factor interdependencies via direct and total-relation matrices. D-R indicators will classify cause and effect factors, culminating in a causal map illustrating the system's structure and each factor's role. This study aims to offer a structured framework for effective data-driven culture and business process optimization.