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All Journal Techne: Journal of Engineering, Technology and Industrial Applications Oikonomia Review: Journal of Economics, Management, and Accounting
Lê Thị Hồng Nhung Lê Thị Hồng Nhung
Vinh University
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Semiconductor Wafer Fab Yield: Quantifying Defect Escape, Metrology Uncertainty, and Time-to-Containment Under Process Drift and Inspection Capacity Constraints Lê Thị Hồng Nhung Lê Thị Hồng Nhung
Techne: Journal of Engineering, Technology and Industrial Applications Vol. 1 No. 4 (2025): Techne: Journal of Engineering, Technology and Industrial Applications
Publisher : Kalam Practica Media

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Abstract

This article presents an engineering-oriented reliability framework for wafer fab yield management that models end-to-end uncertainty propagation from process drift and measurement uncertainty through sampling-based inspection, excursion detection, and containment decisions into distributional outcomes relevant to manufacturing performance, including probability of defect escape, expected affected wafers before containment, false containment probability, time-to-detection and time-to-containment distributions, and an economic yield-loss index. A scenario-based quantitative study is developed for a generic high-volume fab with multiple critical tools and a mix of in-line metrology and inspection, comparing four architectures: baseline control charts with fixed sampling, expanded inspection without governance, model-based excursion detection with limited capacity awareness, and a governance-optimized two-tier architecture that combines drift-aware metrology validation, dynamic sampling allocation based on risk and tool health, staged containment policies, and capacity-aware triage for engineering review. Results show that increasing inspection without governance can reduce defect escape but can increase false containment and cycle-time penalties, that model-based detection improves time-to-detection but can fail under miscalibration and review overload, and that a two-tier governed approach reduces expected yield loss by reducing tail propagation and stabilizing containment decisions under drift and capacity constraints. Three copy-ready tables and complete prompts for data-driven figures are provided for Techne submission.
AI-Enabled Demand Forecasting Capability, Inventory Resilience, and Operational Performance among Vietnamese Retail SMEs: The Mediating Role of Decision Quality Trần Văn Nam Trần Văn Nam; Lê Thị Hồng Nhung Lê Thị Hồng Nhung
Oikonomia Review: A Journal of Applied Management, Accounting, and Business Strategy Vol. 1 Núm. 2 (2025): Oikonomia Review: Journal of Economics, Management, and Accounting
Publisher : Kalam Practica Media

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Retail SMEs across Southeast Asia increasingly experiment with AI-enabled analytics to improve demand forecasting, yet performance outcomes remain uneven because technology adoption does not automatically translate into better operational decisions. This study examines how AI-enabled demand forecasting capability influences operational performance among Vietnamese retail SMEs through inventory resilience and decision quality. Building on capability theory, information processing logic, and resilience perspectives, the model conceptualizes AI capability as a set of routines that combine data readiness, tool reliability, and managerial interpretive competence. Survey data were collected from 455 Vietnamese retail SMEs that had used digital analytics tools for forecasting or replenishment planning for at least six months. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test direct effects and parallel mediation. Results indicate that AI-enabled forecasting capability is positively associated with operational performance, with decision quality and inventory resilience operating as complementary mediators. Decision quality strengthens resilience by improving replenishment timing and reducing overreaction to short-term noise. The findings clarify why AI initiatives often fail when data and interpretation routines are weak, and they offer ASEAN-relevant insights for small retailers facing demand volatility and supply disruptions. Practical implications emphasize pairing analytics tools with data governance and managerial training, enabling SMEs to convert forecasts into robust inventory actions.
Co-Authors Trần Văn Nam Trần Văn Nam