Applied Artificial Neural Networks for Industrial Decision-Making Optimization

Industrial decision-making processes today face significant challenges due to the increasing complexity of problems. This complexity arises from the wide range of optimization constraints to consider, the vast amounts of information to process, and the numerous potential solutions to explore. Incorporating Artificial Intelligence (AI) into industrial decision-making processes has emerged as a transformative force, converting organizational strategies and operational paradigms to unlock new levels of efficiency, production, and profitability while preserving the lead. This study presents a novel decision-making optimization approach that efficiently uses artificial neural networks and the analytic hierarchy process to generate optimal decisions based on real-time performance data analysis and prior in-stance learning.