Faculty/Staff Directory

Jean

Essila

essilaj@gvsu.edu

Associate Professor

3109 L. William Seidman Center

616-331-7475

• Management

• Operations and Supply Chain Management
• Value Stream Mapping
• Strategic Capacity Planning

Professor Jean C. Essila, PhD, DBA, is an Associate Professor of Management Information Systems at the Seidman College of Business, Grand Valley State University (GVSU). He earned his PhD in Engineering Management from the School of Engineering and Applied Sciences at The George Washington University and a Doctor of Business Administration in Business Management from California InterContinental University.  He also holds certificates in Higher Education Pedagogy and in Business Analytics from Harvard University.

Professor Essila is a Certified Supply Chain Professional (CSCP) and holds the Certified in Logistics, Transportation and Distribution (CLTD) designation from APICS/ASCM. Additionally, he is an SAP-certified consultant and an ERPsim-certified instructor.

Before returning to GVSU in 2020, Professor Essila served on the faculty at Northern Michigan University (NMU) from 2016 to 2020. During his tenure there, he received three major academic honors: the Outstanding Research Award, the Outstanding Graduate Faculty Award, and the Excellence in Scholarship Award, the university’s most prestigious research honor. This award recognized his “outstanding achievements in scholarship that have contributed to the discovery, dissemination, and application of knowledge and enhanced the educational experience of Northern Michigan University students.” Professor Essila also taught as an adjunct professor at GVSU from 2012 to 2015.

Before his academic career, Professor Essila held senior leadership positions with several Fortune 500 companies, including Perenco Oil and Gas, Johnson Controls North America, and ExxonMobil, where he accumulated over 15 years of industry experience.

His current research examines the impact of artificial intelligence-powered robotic process automation (RPA) and enterprise resource planning (ERP) systems on supply chain resilience. He has written multiple books, chapters, and peer-reviewed articles, and has presented his work at many international conferences.

·      PhD, Engineering Management, George Washington University (2018)

·      DBA, Business Management, California InterContinental University (2013)

·      Certificate in Higher Education Pedagogy, Harvard University (2018)

·      Certificate in Business Analytics, Harvard University (2020)

 

• Management Information Systems
• Operations Management
• Supply Chain Management

·      Essila J.C., Motwani, J. (in press, 2025). Efficacy of Seasonal Factor-Adjusted Naïve Forecasting in Operations Management: Insights from a Simulation Study. Operations Management Education Review, 18.

·      Essila, J. C. (2024). A Regression Model to Identify Supply Chain Cost Drivers in Healthcare and Make Cost Predictions. International Journal of Business Innovation and Research, 35(4), 497-516.

·      Essila J.C., Motwani, J. (in press, 2025). Efficacy of Seasonal Factor-Adjusted Naïve Forecasting in Operations Management: Insights from a Simulation Study. Operations Management Education Review, 18.

·      Essila, J. C., Motwani, J., Hassana, A. K. (2024). Investigating ERP Systems Adoption by Medium-sized and Large Companies in Developing Countries. International Journal of Business Information Systems.

·      Essila, J.C. (2023), "Strategies for reducing healthcare supply chain inventory costs", Benchmarking: An International Journal, Vol. 30 No. 8, pp. 2655-2669.

·      Essila, J. C., Motwani, J. (2023). Unmasking Health Care Supply Chain Cost Drivers in the United States. Benchmarking: An International Journal.  Vol. 31 (4): 1350–1382.

.      Essila, J. C., Alhourani, F., Motwani, J. (2021). Using Concept Maps in Teaching Operations Management Courses. Decision Sciences Journal of Innovative Education, 19(1), 15-39.

·      Alhourani, F., Essila, J. C., Farkas, B. (2021). Preventive maintenance planning considering machines’ reliability using group technology. Journal of Quality in Maintenance Engineering,