Intelligent System Support Potential in Manufacturing Process Management

Manufacturing enterprises are under constant pressure to deliver quality products at the right to meet the highly dynamic needs of their customers. Given the increasingly stiff competition from both national and international enterprises on the market, manufacturing managers have no choice but to optimize their processes to increase productivity, flexibility, and responsiveness. This paper attempts to show that deploying intelligent support systems in managing manufacturing processes is crucial for the survival of manufacturing enterprises and ensuring growth and sustainable development. The study focuses on the current research related intelligent decision support systems, genetic algorithms, and artificial neural networks and how they can help optimize manufacturing processes. Manufacturing enterprises are under constant pressure to sustain growth and development in increasingly competitive business landscapes. Enhancing productivity and flexibility in manufacturing requires rapid decision-making for optimal outcomes. A review of existing works of literature identifies the significant role of a robust decision support system in process optimization. The study establishes that genetic algorithms provide better tools for strategic decision-making and hold great potential for solving process optimization challenges. Moreover, a review of existing literature on artificial neural networks present them as essential tools for decision-making through their capacity to generate optimal outputs in process optimization. The observations emphasize the ability of artificial neural networks and genetic algorithm in enriching decision support systems capable of optimizing manufacturing processes. The study shows the need for further studies on unified techniques and methodologies of their implementation in manufacturing processes.