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Volume 5, Issue 1, 2026

Abstract

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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.

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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.

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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.

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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.

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