From Simple to Sophisticated: Investigating the Spectrum of Decision Support Complexity with AI Integration in Manufacturing

Abstract

In the evolving manufacturing landscape, the integration of Artificial Intelligence (AI) into Decision Support Systems (DSSs) has become crucial for enhancing decision-making. However, a visible challenge arises from the wide range of methodologies available, requiring a thoughtful choice of a suitable method for a given problem description. The absence of adequate resources for guiding developers in selecting an appropriate method is evident. In response to this gap, the presented work aims to improve the clarity and understanding of integrating existing methods, including AI, into DSSs. The clarity is achieved by introducing a structured grouping of DSSs based on the implemented methodology into four categories: rule-based, optimisation-based, simulation-based, and learning-based. Furthermore, this research illustrates decision-making with real-world examples by drawing insights from the literature. It underlines the user-centric importance in decision-making, emphasising that the effectiveness of the chosen DSS category depends on user interaction and comprehension. Looking ahead with the continuous evolution of AI, the ongoing incorporation of methodological advancements into DSSs is crucial for the continuous improvement of decision-making processes and alignment with the dynamic needs of users and the challenges present in modern manufacturing.

Keywords: Decision Support System, DSS application, Manufacturing, User-Centric, Artificial Intelligence

DOI: 10.54941/ahfe1004711