Development of a Knowledge-Enhanced Neural Network Decision Support System for Strategic Planning in Semiconductor Firms

Abstract

In the highly competitive and innovation-driven semiconductor industry, strategic decision-making plays a pivotal role in ensuring sustained growth, risk mitigation, and technological leadership. However, the inherent complexity of strategic planning in this domain—characterized by dynamic global supply chains, rapid technological obsolescence, and volatile market demands—renders traditional decision support systems (DSS) inadequate. These systems often lack the ability to integrate structured domain knowledge with unstructured data, and struggle to provide interpretable, context-aware insights for long-term planning. To address these limitations, this paper presents the development of a Knowledge-Enhanced Neural Network (KENN)–based Decision Support System specifically tailored for strategic planning in semiconductor firms. The core innovation lies in the fusion of symbolic expert knowledge with the representation learning power of neural networks. The proposed system integrates a multi-source knowledge base—comprising strategic rules, historical planning cases, industry standards, and expert heuristics—into a deep learning model via attention-driven embedding and rule-guided loss regularization. This hybrid mechanism enhances both the learning efficiency and decision interpretability of the model. The architecture consists of four key modules: (1) data preprocessing and feature extraction, which transforms raw enterprise data (e.g., financial indicators, capacity statistics, market forecasts) into structured inputs; (2) knowledge base construction, which formalizes expert rules using ontologies and semantic graphs; (3) KENN inference engine, which combines knowledge-aware attention layers with a feedforward network to generate recommendations; and (4) scenario analysis and visualization, allowing decision-makers to explore strategic alternatives interactively. To validate the effectiveness of the proposed system, we conduct extensive experiments using datasets collected from mid-to-large scale semiconductor manufacturers across Asia and North America. Evaluation metrics include accuracy of strategic recommendation, alignment with expert judgments, model robustness under uncertain conditions, and interpretability as measured by rule consistency and explanation fidelity. Benchmark comparisons against standard neural networks (e.g., MLP, LSTM) and classic decision trees (e.g., XGBoost) reveal that the KENN-based system achieves 12–18% higher accuracy in strategic scenario simulation and reduces decision uncertainty by over 25% in high-risk planning contexts. Three application scenarios are examined in depth: (i) R&D investment optimization, where the system suggests funding allocations across competing technology roadmaps; (ii) production capacity planning, addressing bottlenecks under resource constraints; and (iii) supply chain risk mitigation, providing real-time alerts and alternative supplier recommendations. In all cases, the KENN-DSS outperforms conventional models in both decision quality and response time. In conclusion, this research demonstrates that integrating expert knowledge into neural network–based decision systems significantly improves strategic planning outcomes in semiconductor enterprises. The proposed KENN-DSS framework not only enhances decision accuracy and interpretability but also offers a scalable foundation for future AI-augmented management systems. This approach paves the way for more agile, intelligent, and resilient enterprise planning in high-tech industries.