Design and implementation of a low cost RFID track and trace system in a learning factory
Date
2018
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
ENGLISH ABSTRACT: The factories of the future will make use of actuators, sensors and cyber-physical systems (CPS) to provide an environment in which human beings, machines, and resources will communicate as in a social network. In such a network, communication between various “objects” relay the current state of the physical world. Business decisions are made using the information and it is therefore critical that this information is accurate and in real-time. Information flow is a key enabler of such future factories. Industrial engineers, as designers and improvement agents of such factories of the future, will need to develop better skills in various aspects of data analytics and information communication technologies. This paper describes the development and implementation of a low cost RFID track and trace system (by students) for application in a Learning Factory for teaching undergraduate industrial engineering students key concepts related to Industry 4.0 and “smart factories”. The benefit of this system is not only a demonstrator to be used in the Learning Factory, but also can be used to teach students in a “learning by doing” fashion critical skills related to real time tracking in a manufacturing environment. The system also demonstrates potential low cost implementation of such technologies in SME’s.
Description
CITATION: Louw, L. & Walker, M. 2018. Design and implementation of a low cost RFID track and trace system in a learning factory. Procedia Manufacturing, 23:255-260, doi:10.1016/j.promfg.2018.04.026.
The original publication is available at https://www.sciencedirect.com
The original publication is available at https://www.sciencedirect.com
Keywords
Track and trace system, Cyber-physical systems, Manufacturing industries
Citation
Louw, L. & Walker, M. 2018. Design and implementation of a low cost RFID track and trace system in a learning factory. Procedia Manufacturing, 23:255-260, doi:10.1016/j.promfg.2018.04.026