A Comparative Study of the Three Predictive Tools for Forecasting a Transfer Line's Throughput

Authors

  • Patrick A Hillberg DELMIA Corp
  • Sankar Sengupta Oakland University
  • Robert P Van Til Oakland University

DOI:

https://doi.org/10.23055/ijietap.2009.16.1.202

Keywords:

Forecasting, statistical regression, neural networks, case-based reasoning, system simulation

Abstract

A study comparing the performance of three predictors of a manufacturing system’s future state based upon its current state is presented. The three predictors are constructed using statistical regression, neural networks and case-based reasoning techniques. An asynchronous transfer line with unreliable machines is considered for this study. The line’s current Work-In-Process (WIP) is used to forecast future throughput. A simulation model of the system is used to generate training data for constructing the predictors as well as data used for validation. The impacts of the number of machines and the number of line partitions on forecast accuracy are investigated. Results indicate that the predictors based on statistical regression and neural network techniques offered comparable performance, and both performed better than the predictor based on case-based reasoning technique. Future work includes development of a prediction model to assist in day-to-day operational tasks such as scheduling of opportunistic maintenance and manpower planning.

Author Biographies

Patrick A Hillberg, DELMIA Corp

P

Sankar Sengupta, Oakland University

Sankar Sengupta currently is a faculty member in the Electrical and Systems Engineering Department at Oakland University. He earned a B.S degree in mechanical engineering from I.I.T. Kharagpur in India, an M.S degree in industrial engineering from the University of Oklahoma, and a Ph.D. in industrial engineering from Clemson University. He has more than 12 years of work experience in the process industry. His research interest includes modeling and analysis of manufacturing/service systems, quality control, application of lean principles in a manufacturing/service system, production planning and control, and modeling and analysis of a supply chain. He has published technical papers in refereed journals and presented technical papers in international conferences. He is actively involved with industry to establish a strong tie between OU and local partners. He is a member of IIE, ASQ and SME.

Robert P Van Til, Oakland University

Robert Van Til is currently a professor in the Industrial and Systems Engineering Department at Oakland University. He earned a B.S degree in mechanical engineering from Michigan State University, an M.S degree and a PhD in mechanical engineering from the Northwestern University. His research interest includes modeling and analysis of manufacturing/service systems, quality control, application of lean principles in a manufacturing/service system, production planning and control, and modeling and analysis of a supply chain. He has published technical papers in refereed journals and presented technical papers in international conferences.

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Published

2022-03-14

How to Cite

Hillberg, P. A., Sengupta, S., & Van Til, R. P. (2022). A Comparative Study of the Three Predictive Tools for Forecasting a Transfer Line’s Throughput. International Journal of Industrial Engineering: Theory, Applications and Practice, 16(1), 32–40. https://doi.org/10.23055/ijietap.2009.16.1.202

Issue

Vol. 16 No. 1 (2009)

Section

Statistical Analysis

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