Taming Complexity: Generative Doppelgangers For Stochastic Data Trends In Complex Industrial Manufacturing Systems

Abstract

The defining characteristics of complex industrial systems are interconnected processes that generate immense quantities of stochastic data, often impeding operations optimization, particularly metrics such as Overall Equipment Effectiveness (OEE). To address the limitations of traditional methods and earlier machine learning techniques in capturing this complexity, this paper proposes a novel approach employing generative doppelgangers, a Generative Adversarial Network (GAN)-based model, to simulate the operational behavior of these systems. This "behavioral doppelganger" learns intricate relationships within historical operational data from a production facility, enabling proactive what-if analyses for OEE optimization. The proposed framework's ability to replicate the impact of process parameters on availability, quality, and performance, collectively contributing to OEE, is highlighted. The research validates this approach using real-world data from an industrial sugar plant, demonstrating its potential for providing valuable insights into system behavior under various operational scenarios for proactive optimization.