Implementing Digital Twins That Learn: AI and Simulation Are at the Core-Reference-Cited by-同舟云学术

Implementing Digital Twins That Learn: AI and Simulation Are at the Core

Published:2023-03-27 Issue:4 Volume:11 Page:425
ISSN:2075-1702
Container-title:Machines
language:en
Short-container-title:Machines

Author:

Biller Bahar¹,Biller Stephan²^ORCID

Affiliation:

1. SAS Institute, 100 SAS Campus Dr, Cary, NC 27513, USA

2. School of Industrial Engineering and Mitchell E. Daniels School of Business, Purdue University, 315 N. Grant Street, West Lafayette, IN 47907, USA

Abstract

As companies are trying to build more resilient supply chains using digital twins created by smart manufacturing technologies, it is imperative that senior executives and technology providers understand the crucial role of process simulation and AI in quantifying the uncertainties of these complex systems. The resulting digital twins enable users to replay history, gain predictive visibility into the future, and identify corrective actions to optimize future performance. In this article, we define process digital twins and their four foundational elements. We discuss how key digital twin functions and enabling AI and simulation technologies integrate to describe, predict, and optimize supply chains for Industry 4.0 implementations.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering

Link

https://www.mdpi.com/2075-1702/11/4/425/pdf

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