Optimizing Smart Factory Operations: A Comparative Study of Distributed vs. Intelligent Control Systems in Process Automation

EasyChair Preprint 14303

Abstract

In the rapidly evolving landscape of Industry 4.0, smart factories represent the pinnacle of manufacturing innovation, leveraging advanced technologies to enhance operational efficiency, productivity, and flexibility. This paper presents a comparative study of distributed and intelligent control systems in the realm of process automation within smart factories. Distributed control systems (DCS) have long been a cornerstone of industrial automation, offering reliable and scalable solutions for managing complex processes. In contrast, intelligent control systems, driven by artificial intelligence (AI) and machine learning (ML), are emerging as transformative alternatives that promise adaptive, self-optimizing capabilities. Through a detailed analysis, this study evaluates the performance, scalability, and adaptability of these two paradigms in various manufacturing scenarios. Key performance indicators (KPIs) such as response time, fault tolerance, energy efficiency, and maintenance requirements are examined. The findings reveal critical insights into the strengths and limitations of each approach, providing a comprehensive understanding of their respective roles in optimizing smart factory operations. This comparative analysis aims to guide industry stakeholders in selecting the most suitable control strategy for their specific needs, ultimately contributing to the advancement of more intelligent, resilient, and efficient manufacturing systems.

Keyphrases: Artificial Intelligence (AI), Distributed Control Systems (DCS), Key Performance Indicators (KPIs)

@booklet{EasyChair:14303,
  author    = {Abill Robert},
  title     = {Optimizing Smart Factory Operations: A Comparative Study of Distributed vs. Intelligent Control Systems in Process Automation},
  howpublished = {EasyChair Preprint 14303},
  year      = {EasyChair, 2024}}