Wilkistar Otieno, Ph.D.

Associate Professor, Department Chair
Industrial And Manufacturing Engineering

Education:

  • Ph.D., Industrial Engineering, University of South Florida, 2010
  • M.S., Statistics, University of South Florida, 2007
  • M.S.,  Industrial Engineering, University of South Florida, 2006
  • B.S., Production and Mechanical Engineering, Moi University, Kenya, 2002

Research Focus:

  • Predictive Data Analytics for Manufacturing Systems
  • Sustainable Manufacturing (Remanufacturing)
  • Energy Sustainability
  • Reliability Analysis of Products and Systems
  • Engineering Education

Media:

Select Recent Publications:

  1. Otieno, W., Po-Hsun C., Kuan-Jui, C., Assessing the Remanufacturability of Office Furniture: A Multicriteria Decision-Making Approach, Journal of Remanufacturing, Published Online March 2020.
  2. LaCasse, P., Otieno, W., Maturana, F., Predicting Contact-Without-Connection Defects on Printed Circuit Boards Employing Ball Grid Array Package Types: A Data Analytics Case Study in the Smart Manufacturing Environment, Journal of SN Applied Science, Vol. 2, No. 156, 2020.
  3. Farahani, S., Otieno, W., Omwando, T., “The Optimal Disposition Policy for Remanufacturing Systems with Variable Quality Returns (A Case Study)”, Journal of Computers and Industrial Engineering, Vol. 140, 2020.
  4. Farahani, S., Otieno, W., Barah, M., Environmentally Friendly Disposition Decisions for End-Of-Life Electrical and Electronic Products: The Case of Computer Remanufacture, Journal of Clean Production, Vol. 224, pp. 25-39, 2019.
  5. LaCasse, P., Otieno, W., Maturana, F., Operationalization of Defect Prediction Case Study in a Holonic Manufacturing System, 9th International Conference on industrial Applications of Holonic and Multi-Agent Systems (HoloMAS), 2019, Linz, Austria.
  6. LaCasse, P., Otieno, W., Maturana, F., “A Survey of Feature Set Reduction Approaches for Predictive Analytics Models in the Connected Manufacturing Enterprise”, Journal of Applied Sciences, Vol. 9, Issue 5, 2019.
  7. LaCasse, P., Otieno, W., Maturana, F., “A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise”, Journal of Big Data, Vol. 5, Issue 45, 2018.
  8. Otieno, W., Garantiva, J., LaCasse, P., Optimal One-Dimensional Free-Replacement Warranty Period for AGM Batteries, IEEE-Explore, Annual Reliability and Maintainability Symposium Proceedings, January 2019.
  9. Omwando, T., Otieno, W., Farahani, S., Ross, A., “A Fuzzy Inference System Approach for Evaluating the Technical and Economic Feasibility of Product Remanufacture,” Journal of Clean Production, Vol. 174, pp 1534-1549, February 2018.
  10. Otieno, W., Kyureghyan-Campbell, N., Cook, M., “Work in Progress: Novel Approach to Bridge the Gaps of Industrial and Manufacturing Engineering Education: A Case Study of the Connected Enterprise Concepts, IEEE- Explore, Frontiers in Education, Indianapolis, IN., October 18-21, 2017.
  11. Otieno, W. Industry-University Partnership: Infusing Connected Enterprise Program into the Engineering Curriculum, at Rockwell Automation Vocational School Partnership Program Workshop at UWM, November, 2017.
  12. Otieno, W., Nanduri, V., Das, T. K., Savachkin, A., and Okogbaa, G., “Mentor Teacher Workshops: Train-the-Trainer Model of the USF STARS GK-12 Program,” Journal of Florida Association of Science Teachers, 2009.