Efficient management of production processes in modern manufacturing depends on the timely identification of their most critical phases, as such recognition directly enhances process reliability, productivity, and product quality. To address this need, an objective multi-attribute decision-making (MADM) framework has been developed by integrating the Criteria Importance Through Inter-criteria Correlation (CRITIC) method with Pareto analysis, a well-established approach also referred to as ABC classification. Within this framework, a comprehensive set of evaluation criteria was determined in collaboration with a Process Failure Mode and Effects Analysis (PFMEA) team from a Tier-1 automotive manufacturer. The decision matrix was constructed from data extracted from PFMEA reports that had been subjected to preliminary statistical processing to ensure robustness and comparability. The relative importance of the criteria was then established using the CRITIC method, which objectively derives weights from statistical indicators such as the arithmetic mean, standard deviation, and inter-criteria correlation coefficients. The framework was subsequently applied to the PFMEA report for a rear axle assembly process, encompassing 16 discrete production phases. Pareto analysis was employed to classify the phases according to their criticality, thereby enabling a systematic prioritization of process risks. The resulting classification demonstrated strong consistency with expert evaluations and was confirmed to reflect real-world production conditions accurately. Beyond confirming methodological validity, the findings underscore the advantages of employing a fully objective weighting mechanism combined with a widely recognized prioritization tool, thereby offering a transparent and replicable basis for decision-making in complex manufacturing contexts. This integration not only supports continuous improvement and risk mitigation but also provides a scalable framework applicable to a broad range of industrial processes where critical phase identification is essential.
Remote sensing plays a crucial role in disaster management. Moreover, its effectiveness is severely limited due to operational, technological and environmental challenges. Data acquisition can be disrupted by sensor limitations and by extreme events or natural factors, such as cloud cover. In fact, high-resolution imagery often requires significant processing time, specialized expertise and expensive infrastructure. Therefore, ensuring timely, accurate and accessible remote sensing data at all stages—preparedness, response, recovery and mitigation—is a major challenge. This study explores the application of multi-criteria decision making (MCDM) techniques using bipolar fuzzy numbers (BFNs) to evaluate this. We apply the weighted and ranking MCDM method, i.e., Method Based on the Removal Effects of Criteria (MEREC) and Multi-Attributive Border Approximation Area Comparison (MABAC), respectively, in this paper. The decisions of multiple decision makers (DMs) are considered when collecting this problem related data and BFNs are utilised as mathematical tools to handle uncertainty. In order to address the ambiguity and inconsistency of the system, we finally conclude to conduct the comparative and sensitivity analyses here with the final result.
This study explores the macroeconomic impact of the Zero Over-Dimension Over-Load (ODOL) policy in Indonesia, especially its influence on logistics costs, inflation, and economic growth. The policy is not discussed here only as a matter of transport compliance, but also as a structural change in logistics governance that may affect the wider economy. A mixed-methods approach was used, based on primary survey data collected in 2025 from logistics stakeholders in DKI Jakarta and West Java. For the analysis, the Leontief Price Model was applied to estimate price transmission effects, while the dynamic Computable General Equilibrium (CGE) IndoTERM model was used to simulate cost shocks, investment adjustment, and fiscal reallocation. The findings show that the policy increases national logistics costs by 4.58% in the short term, which raises the logistics cost-to-GDP ratio to 14.94%. However, the longer-term results are more positive. The simulation suggests a 0.05% increase in GDP, equivalent to a net output gain of IDR 14.3 trillion. This result is associated with a 6.74% increase in fleet investment, estimated at IDR 42.4 trillion, as well as fiscal savings caused by lower infrastructure damage. These results suggest that stricter logistics regulation may bring broader economic benefits when the analysis goes beyond the immediate rise in transport costs. In practical terms, the policy should be supported by fiscal incentives for fleet modernization and by careful timing of enforcement, especially to limit inflationary pressure in food and construction-related sectors.
This study proposes a fuzzy-based adaptive temperature management system for hydroponic cultivation in tropical climates. Unlike conventional fixed-setpoint controllers, the proposed Mamdani fuzzy system utilizes pH and electrical conductivity (EC) as contextual inputs to dynamically adjust temperature control strategies. The underlying hypothesis is that maintaining lower root-zone temperatures (RZTs) during suboptimal pH/EC conditions may increase dissolved oxygen availability, partially compensating for nutrient stress. The Internet of Things (IoT)-enabled system employs Long Range wireless protocol (LoRa) communication for long-range, low-power data transmission, with fuzzy inference executed at the gateway for offline resilience. A five-month field validation (April–August 2024) in Ho Chi Minh City demonstrated effective temperature regulation, maintaining solution temperature within the 18–28 °C operational target range for 88.7% of the trial period, with zero exceedance of the 35 °C critical threshold. The system maintained pH at 5.72 ± 0.32 (86.4% time in optimal range) and EC at 1.87 ± 0.28 mS/cm (81.3% time in optimal range). Retrospective simulation comparing the proposed controller against On/Off, proportional-integral (PI) baselines, and temperature-only FLC baselines, demonstrated a 15–16% reduction in chiller runtime while maintaining equivalent thermal safety. Operational crop assessment across three cultivation cycles indicated commercially viable lettuce production. A dedicated system engineering analysis addresses architecture trade-offs, reliability, scalability, and cost-effectiveness for practical deployment in tropical commercial operations.
Assessing sustainable development performance is essential for understanding national progress. This study evaluates the sustainable development performance of G20 member countries (excluding the EU) using an integrated set of economic, social, and environmental indicators. Economic performance is captured by GDP per capita and the unemployment rate, while social performance is assessed through the education index, health expenditure, and income inequality (Gini coefficient). Environmental performance is represented by CO$_2$ emissions and the share of renewable energy in total energy consumption. Objective, data-driven weights were derived using the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and countries were subsequently ranked using two multi-criteria decision-making (MCDM) approaches: Additive Ratio Assessment (ARAS) and Grey Relational Analysis (GRA). Correlation analysis (Spearman, Kendall, and Pearson) was conducted to examine the consistency and reliability of the rankings. The findings provide a comparative assessment of sustainability performance across G20 countries, highlighting relative strengths and weaknesses across economic, social, and environmental dimensions. The results offer a structured reference for decision-makers in engineering management and policy design in formulating evidence-based strategies.
Electronics and mechatronics have transformed railway vehicles into complex cyber–physical systems integrating mechanical assemblies, electrical architectures, and embedded software across multi-decade lifecycles. Maintaining configuration integrity across these domains is particularly challenging under phased retrofits, supplier substitutions, and stringent safety requirements. This paper presents a structured narrative review of Product Lifecycle Management (PLM)-enabled approaches for governing electronics and mechatronics lifecycle data in railway manufacturing. Guided by explicit research questions, the review synthesizes literature from 2010–2025 using transparent search and inclusion criteria aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting principles. Drawing on systems engineering, lifecycle management, and configuration management theory, the analysis examines multi-domain BOM governance, hardware–software traceability, variant and effectivity control, digital thread continuity, and supplier collaboration. Cross-industry implementation archetypes are evaluated to clarify contextual boundary conditions and transferability limits. The findings indicate that configuration-centric governance—rather than tool integration alone—is the primary determinant of PLM effectiveness in long-lifecycle, safety-regulated environments. A structured future research agenda and explicit engineering management implications are proposed to strengthen digital continuity, fleet-level effectivity discipline, and safety-aligned lifecycle governance.
Hospital infrastructure systems represent one of the most complex categories of engineered systems, characterized by the tight integration of system configuration, technical subsystems, operational processes, and governance structures. Despite their structural durability, such systems—particularly in institutionally unstable environments—are prone to early functional and operational obsolescence, leading to performance degradation over the lifecycle. This challenge highlights the need to conceptualize hospitals not as static built assets, but as dynamic socio-technical systems requiring systematic performance-oriented management. This study develops a system-level analytical framework to examine future-proofing as an emergent outcome of interactions among institutional and contextual drivers, planning mechanisms, innovation, and design capabilities. The empirical analysis is conducted using data collected from professionals engaged in hospital infrastructure projects in Iraq. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach is employed to evaluate both direct and indirect relationships within the proposed system model. The results demonstrate that institutional and contextual drivers significantly influence planning mechanisms, which in turn act as a central structuring layer affecting both innovation and design capabilities. Innovation does not exhibit a statistically significant direct effect on long-term system adaptability, indicating that technological advancement alone is insufficient to ensure sustained performance. In contrast, design capabilities constitute the primary determinant of future-proofing, with a strong mediating effect on lifecycle system performance. The findings provide important implications for engineering management by emphasizing that long-term adaptability in hospital infrastructure systems depends on the alignment between planning structures and implementation-oriented design capabilities, rather than on innovation intensity alone.
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