AI and Machine Learning applied to industrial engineering to improve operations management

Abstract

Operations management in business environments is facing a transformation driven by emerging technologies such as artificial intelligence (AI) and machine learning (ML). These advances enable resource optimization, process automation, and support for real-time decision-making—all essential aspects for increasing competitiveness in industrial engineering. The evolution of planning systems, from MRP to today's intelligent ERP and CRM systems, demonstrates the transition toward more adaptive and predictive cyber-physical environments, where the integration of big data, IoT, and cloud computing redefines traceability and operational efficiency. This transition from Industry 4.0 to 5.0 reflects a shift toward a more human and sustainable model, combining collaboration between intelligent machines and workers with a focus on personalization and social responsibility. Globally, studies by firms such as McKinsey, Deloitte, and Gartner project an economic impact of trillions of dollars by 2025, highlighting applications in manufacturing, pharmaceuticals, energy, and services. However, significant challenges remain in the adoption of ML, such as scalability, model versioning, and alignment with strategic objectives. Consequently, the strategic integration of AI and ML constitutes a key pillar for the innovation, productivity, and sustainability of industrial operations.