Enhancing Operational Efficiency in the Supply Chain: A Systematic Review on Artificial Intelligence Applications

Resumen

The growing complexity of globalized supply chains has compelled organizations to adopt innovative solutions to maintain their competitive edge. In this scenario, artificial intelligence (AI) has emerged as a pivotal tool for optimizing logistical processes and enhancing operational efficiency. This Systematic Literature Review (SLR) aims to analyze how AI applications improve operational efficiency within the supply chains of industrial companies. To address this objective, the PIOC methodology (Problem, Intervention, Outcomes, Context) was employed to structure specific research questions and guide the search for relevant studies in scientific databases such as Scopus. The analysis of twenty academic articles reveals that demand forecasting and inventory management are the most impactful AI applications, contributing 25% and 20% to overall performance, respectively. Other applications, such as route optimization and predictive maintenance, also demonstrate significant contributions, each accounting for 15% of the improvements. These findings underscore that AI implementation not only enhances key operational efficiency metrics—such as reducing logistics costs and minimizing downtime—but also enables companies to swiftly adapt to dynamic operational environments. This research enriches the existing literature by offering a comprehensive framework on the practical applications of AI in supply chain management and its positive effects across various global industries. Beyond highlighting the tangible benefits of AI, the study emphasizes its transformative potential in optimizing processes, particularly in the context of ongoing digital transformation. By doing so, it provides valuable insights for both academia and industry, paving the way for further exploration and adoption of AI-driven solutions in supply chain operations.