Monte Carlo Methods for Industry 4.0 Applications

TL;DR

This paper introduces a structured workflow for applying Monte Carlo simulations in Industry 4.0, compares two Monte Carlo methods, and discusses their practical limitations for process quality evaluation.

Contribution

It proposes a clear simulation workflow for Monte Carlo methods in Industry 4.0 and compares Cumulative and Markov Chain Monte Carlo techniques.

Findings

The workflow facilitates Monte Carlo method selection in Industry 4.0.

Comparison reveals strengths and limitations of the two Monte Carlo methods.

Results guide practical application of Monte Carlo simulations in industrial settings.

Abstract

The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods.