Identifying the relative importance of predictive variables in artificial neural networks based on data produced through a discrete event simulation of a manufacturing environment

ABSTRACT

This research used a discrete event simulation to create data on a shipment receiving process instead of using historical records on the process. The simulation was used to create records with different inputs and operating conditions and the resulting overall elapsed time for the overall process. The resulting records were used to create a set of predictive artificial neural network models that predicted elapsed time based on the process characteristics. Then, the connection weight approach was used to determine the relative importance of the input variables. The connection weight approach was applied in three different steps: (1) on all input variables to identify predictive and non-predictive inputs, (2) on all predictive inputs, and (3) after removal of a dominating predictive input. This produced a clearer picture of the relative importance of input variables on the outcome variable than applying the connection weight approach once.

KEYWORDS:

• Discrete event simulation (DES)
• artificial neural networks (ANNs)
• connection weight approach
• data mining

Additional information

Notes on contributors

R. Pires dos Santos

R. Pires dos Santos received her Master of Science in Technology with an emphasis in Manufacturing Engineering from Brigham Young University and her Bachelor of Science in Industrial Engineering from Universidade Federal de Pernambuco located in the state of Pernambuco, Brazil. She is interested in studying the application of data science in a manufacturing environment. Contact information: 483 Belmont PL Unit 168, Provo, UT 84606. Phone: [phone]. E-mail: [email protected]

D. L. Dean

D. L. Dean is an associate professor of IS at Brigham Young University, Utah. He received his PhD in MIS from the University of Arizona. He has published articles in Management Science, MIS Quarterly, Journal of MIS, Journal of the AIS, IEEE Transactions, Electronic Commerce Research, Expert Systems with Applications, and others. His research interests include knowledge sharing, data mining methods, scientometrics, and collaborative tools and methods. Contact information: 786 N Eldon Tanner Building, Provo, UT 84604. Phone: [phone]. E-mail: [email protected]

J. M. Weaver

J. M. Weaver is an assistant professor of Manufacturing Engineering at the Ira A. Fulton College of Engineering and Technology at Brigham Young University. He is a systems engineer with broad experience in mechanical engineering, nuclear weapon safety, product design, and reverse engineering. His research involves design for manufacturing, additive manufacturing, systems engineering, and design theory and quality. Contact information: 265 Crabtree Technology Building, Provo, UT 84604. Phone: [phone]. E-mail: [email protected]

Y. Hovanski

Y. Hovanski is an associate professor of Manufacturing Engineering at the Ira A. Fulton College of Engineering and Technology at Brigham Young University. He is a specialist in Friction Stir Technologies. Contact information: 265 Crabtree Technology Building, Provo, UT 84604. Phone: [phone]. E-mail: [email protected]