School of Engineering and Computer Science

Engineering Center, Room 301
115 Library Drive
Rochester, MI 48309-4479
(location map)
Dean's Office: (248) 370-2217
Academic Advising: (248) 370-2201
SECSDean@oakland.edu

School of Engineering and Computer Science

Engineering Center, Room 301
115 Library Drive
Rochester, MI 48309-4479
(location map)
Dean's Office: (248) 370-2217
Academic Advising: (248) 370-2201
SECSDean@oakland.edu

Robert P. Van Til, Ph.D.

Robert P. Van Til, Ph.D.Pawley Professor of Lean Studies and Chair
Industrial and Systems Engineering Dept.
502 EC; (248) 370-2211
vantil@oakland.edu
secs.oakland.edu/~vantil

Ph.D., Northwestern University, 1985

  • Joined Oakland University in 1984
  • Member IIE, SME, ASME, ASEE, Tau Beta Pi (Chief Advisor MI Theta Chapter) and Pi Tau Sigma

Teaching

  • Flexible and Lean Manufacturing Systems
  • Robotic Systems
  • Lean Principles and Application
  • Senior Design
  • Introduction to Industrial and Systems Engineering

Research

  • Analytical and simulation modeling of manufacturing systems
  • Application of lean to manufacturing and healthcare
  • Product Lifecycle Management

PLM, Lean, Manufacturing Systems

Manufacturing environments are dynamic and random in nature. In order to maximize productivity, people and processes must respond to changing conditions as they occur. Real-time scheduling techniques can be used to identify and respond to the current behavior of a manufacturing system. However, accurate predictions concerning the future behavior of the manufacturing system would also be useful information for engineers and managers as they try to optimize the performance of a system.

A key performance measure for most manufacturing systems is the throughput (# parts/unit time). Our (Professor Sengupta and myself) current research considers the construction of a throughput prediction tool. This tool will collect real-time performance data from the manufacturing system and use it to make a prediction concerning future behavior.

For example, the predictive tool could collect data during the first 4 hours of a production shift and use it to make a prediction of the total system throughput at the end of the 8 hour shift. Such a prediction is useful to manufacturing management personal in making both short and long term production decisions.

There are several powerful data-driven modeling techniques that are being studied for constructing such a predictive tool, these include neural networks, statistical regression and case-based reasoning. Note that to successfully use these techniques, a large set of input-output data reflecting the system's performance is required.

This set of input-output performance data will be referred to as historical performance data. For example, each element in this set of historical performance data could contain the throughput and Work-In-Progress (WIP) collected at multiple locations in the manufacturing system for various times during the first 4 hours of a given production shift (the input) as well as the total throughput at the end of the shift (the output). Unfortunately for many modern manufacturing systems, a sufficiently large set of such historical performance data required to use these data-driven modeling techniques is usually not available.

There are two main reasons for this shortage of historical performance data. First, the manufacturing system may be too new to have accumulated a large enough set of historical performance data. However, a more important reason is due to the continuous improvement process applied to most manufacturing systems. Most manufacturing systems are modified on a weekly, if not daily, basis. Hence, the current behavior of a manufacturing system is usually not reflected in the input-output performance data collected during the previous month due to system modifications.

Our solution is to use a verified simulation model of the manufacturing system to produce a set of simulated historical performance data. This set of simulated historical performance data is used to train the throughput prediction tool via one of the data-driven modeling techniques. Once the throughput prediction tool is trained and verified, it will use real-time performance data collected from the manufacturing system in order to make its prediction.

Selected Publications

1. M. Deneweth, S. Sengupta and R. Van Til, “Operational Concerns When
Modeling a Global Supply Chain,” Proc. of the Winter Simulation Conf., 2016.

2. N.R. Choudhury, S. Sengupta and R.P. Van Til, “A Novel Method To Reduce
Inspection Process Cycle Time While Using A Coordinate Measurement
Machine,” Proc. of the AMSE DSC Conf., 2015.

3. A. Khiste, P. Hillberg and R. Van Til, "Developing an IT Infrastructure
for Educational Institutions Teaching Product Lifecycle Management," Proc.
of the ASEE NCS Conf., 2014.

4. S. Sengupta, T. White, K. Das and R. Van Til, “Analysis of a New Signal
for Bottleneck Identification and Loss Allocation to Individual Machines,”
International Journal of Industrial and Systems Engineering, Vol. 13, pp.
175-196, 2013.

5. T. White, S. Sengupta and R. Van Til, “Analysis of a New Signal for
Bottleneck Detection using Higher Order Statistics based on Inter-Departure
Time Data,” Proc. of the Industrial & Systems Engineering Research Conf.,
2012.