faculty image wilkistar otieno

Wilkistar Otieno

  • Associate Professor, Industrial and Manufacturing Engineering
  • Co-Director (Co-PI), UWM Industrial Assessment Center
  • Director, NSF/S-STEM: Preparing Engineers Computer Scientists


Co-Director (Co-PI): DOE/IAC: UWM Industrial Assessment Center


  • PhD, Industrial Engineering, University of South Florida, 2010
  • MS, Statistics, University of South Florida, 2007
  • MS,  Industrial Engineering, University of South Florida, 2006
  • BS, Production and Mechanical Engineering, Moi University, Kenya, 2002

Research Interests

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



Select Recent Publications

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

Community Involvement