Interpretable artificial intelligence empowering economic diversification in smart manufacturing

Economic diversification across the Gulf region, including Kuwait Vision 2035, positions smart manufacturing as a key enabler of sustainable growth. Yet, industrial datasets in the region are typically small, heterogeneous, and incomplete, limiting the performance and trustworthiness of conventional AI models. This paper introduces a Scalable Random Forest (SRF) framework enhanced with a Decision Path Search (DPS) mechanism to address these challenges through both technical robustness and practical interpretability. The SRF pipeline incorporates leakage-safe preprocessing, mixed-type imputation, and small-data augmentation to improve prediction stability under real industrial constraints, while DPS transforms model internals into actionable operational causal knowledge identifying optimal and avoidance parameter ranges. Case studies, including investment casting, demonstrate that SRF + DPS not only outperforms established baselines such as Random Forest (RF), XGBoost, LightGBM, and CatBoost but also deliver transparent insights that engineers can directly apply to reduce defects and enhance process control. The findings highlight how interpretable AI frameworks can accelerate industrial modernization, strengthen regional manufacturing competitiveness, and support national economic diversification strategies.