Our People

Dr. Jack Feng

Professor of Practice, Edwardson School of Industrial Engineering, Purdue University
Academic Director and Co-Chair of the Faculty Advisory Committee, Online Doctor of Engineering (D.Eng) Program, Purdue University's College of Engineering

Bio

Dr. Jack Feng is the Academic Director for the Online Doctor of Engineering (D.Eng) Program in Purdue University's College of Engineering. He has a joint appointment as Professor of Practice in the Edwardson School of Industrial Engineering (ranked top 2~3 in the US). He serves as the Program Co-Chair of the Faculty Advisory Committee, overseeing curriculum development and teaching core courses aligned with the program's mission of long-term success and sustainability. His responsibilities also include external promotion, internal engagement, and student mentoring.

In addition to this new course ENGR603, he will develop and teach two other new courses in support of our D.Eng Program and other programs: (1) IE 590 Leading Engineering, Technology and Operations (3 credit hours; 2nd 8-week summer session between June 15 and Aug 7); (2) IE 590 Applied Engineering Statistics (3 credit hours) in the Fall of 2026.

Dr. Feng brings over 30 years of combined experience across academia, industry, and consulting in 20+ countries. He spent 17 years in senior or executive leadership roles in Corporate America, including at Caterpillar and as a C-suite executive at two publicly traded multinational corporations. Prior to his industry career, he held tenure-track faculty positions at Penn State and Bradley University, where he served as a tenured Professor of Industrial and Manufacturing Engineering from 2003 to 2008.

Jack is a Fellow of IISE (Institute of Industrial and Systems Engineers) and Lean 6-Sigma Master Black Belt. He has published 100+ technical papers (including 40+ peer reviewed journal papers), books, and book chapters. He holds a Ph.D. and M.S. in Industrial and Management Engineering from the University of Iowa, and a B.S. in Mechanical and Manufacturing Engineering from Wuhan University of Technology. Dr. Feng is deeply committed to developing the next generation of leaders in engineering, technology and operations (including supply chain & logistics) management by bridging real-world experience with academic excellence.

Faculty Photo
  • PhD, University of Iowa, Industrial and Management Engineering
  • MS, University of Iowa, Industrial and Management Engineering
  • BS, Wuhan University of Technology
  • Fellow, Institute of Industrial & Systems Engineers (IISE): Elected in 2018
  • Lean Manufacturing Champion by the Organizer of Annual North America Manufacturing Excellence Summit (NAMES) and other similar industry events: 2017
  • Lean Leader of the Year by China Annual International Forum on Lean Management: 2011
  • Inaugural winner of the Caterpillar China Team Collaboration Excellence Award established by Caterpillar China VP, Caterpillar Inc.: 2011
  • First faculty member from the College of Engineering to receive The Caterpillar Inc. New Faculty Achievements Award for Research and Scholarship since the award’s inception in 1963, Bradley University: 2002
  • Inaugural recipient of the College of Engineering Faculty Excellence Award for Research and Scholarship, Bradley University: 2003
  • President, IISE Central Illinois Chapter: 1999–2010
  • IISE Silver Chapter Award, IISE Central Illinois Chapter: 2006
  • Who’s Who of American Teachers: 2005–2006
  • Who’s Who in Engineering Education: 2002
  • Who’s Who International Professionals: 2001–2003
  • Who’s Who in Science and Technology: 2004–2007
  • Operational Excellence / Lean 6-Sigma / Quality Analytics
  • Workforce develop and upscaling for both office / salaried employees and frontline hourly associates
  • Digital manufacturing and digital twins: How to integrate Manufacturing Execution System (MES) with the Enterprise Resources Planning (ERP) and Product Lifecycle Management (PLC) systems, and machine learning / data analytics in improving learning, knowledge discovery, productivity, quality, new facility start up, and financial top line and bottom line.
  • Supervised and unsupervised machine learning (ML) application in manufacturing, supply chain / logistics and healthcare.