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.

This chapter explores the transformative power of AI in bringing transparency and visibility to modern supply chains. It draws extreme importance to AI technologies, including predictive analytics, machine-learning models, blockchain, and IoT for the real-time tracking of shipments, for efficient decision-making, and for ethical sourcing. It analyzes comprehensively how AI is addressing challenges that have long plagued global supply chains: managing risks, compliance, and operational efficiency. Practical applications and case studies give evidence of the tangible improvements brought about by AI's implementation. Other challenges are considered: data privacy concerns, legacy system integration, infrastructural requirements, and resistance to change in organizations. Strategic models are proposed to assist in planning the implementation of AI from phase-wise integration to capacity building, governance, and, lastly, organizational feedback.