Supply chain optimization strategies through artificial intelligence in corporate holdings

Abstract

The present research is framed within the positivist paradigm, under a quantitative approach, using a descriptive field research design, and identifies strategies for optimizing the supply chain (SC) through the use of artificial intelligence (AI) in the context of corporate holdings. It describes the transition from traditional management models to advanced digital paradigms that integrate linear programming (LP) and game theory (GT) with machine learning algorithms. The study identifies that CS is a vital driver for profitability under environments of high uncertainty and geopolitical fragmentation. The procurement, production, and distribution phases are evaluated through the application of linear programming models to optimize supply chain processes focused on their performance. The results indicate that AI allows for more robust decision-making by incorporating exogenous risk variables that are unapproachable for deterministic methods. It is concluded that the adoption of a digital organizational culture and operational resilience are fundamental requirements for competitive survival in the national and international market.