Artificial neural network-based decision support systems in manufacturing processes: A systematic literature review

Abstract

The use of artificial neural network models to enrich the analytical and predictive capabilities of decision support systems in manufacturing has increased. The growing complexity and uncertainty in the manufacturing sector demand improved decision-making to ensure low operations costs, high productivity, and sustainable use of resources. Artificial neural networks have the inherent capacity to analyze the most uncertain and complex patterns in unstructured decision problems. This review aims to synthesize and provide a comprehensive summary of recent studies on artificial neural network-based decision support systems as applied in manufacturing processes. First, the specific processes in manufacturing where artificial neural network-based decision support systems are used are analyzed. A total of 99 multi-disciplinary publications on artificial neural network-based decision support systems published between 2011 and 2021 are retrieved and processed following a rigorous execution of the designated acceptance criteria and quality assessment. A review of the selected studies indicates a growing interest in applying artificial neural networks in decision support systems. Product and process design, performance evaluation, and predictive maintenance are the main application areas identified. A growing tendency to combine artificial neural network models with other intelligent tools, notably fuzzy logic, and genetic algorithm, is noted to overcome drawbacks such as slow convergence when training the algorithms. Further research should extend to other tools for enriching the performance of artificial neural networks in manufacturing processes.