IE 59000 Leading Engineering, Technology and Operations
This course develops the leadership, analytical, and strategic capabilities required for engineers, IT professionals, and operations / supply chain & logistics engineering executives to lead enterprise-scale or business unit-level performance transformation. Students learn to connect engineering rigor with business execution through systems thinking, Operational Excellence, Lean, Six Sigma, and digital operations.
Instructor:
Jack Feng, Ph.D., IISE Fellow, Lean Six Sigma Master Black Belt E-mail: fengcj@purdue.edu
Office location: 1215 ARMS
Office Phone: 765-494-7292
Office Hours: TBD
Objectives
This course develops the leadership, analytical, and strategic capabilities required for engineers, IT professionals, and operations / supply chain & logistics engineering executives to lead enterprise-scale or business unit-level performance transformation. Students learn to connect engineering rigor with business execution through systems thinking, Operational Excellence, Lean, Six Sigma, and digital operations. Through global process and business case studies and a semester-long applied project, participants integrate data-driven decision-making, financial impact assessment, and leadership behaviors to drive sustainable business level and organizational level performance.
Prerequisites
· Graduate or Undergraduate Senior standing in Engineering, Information Technology / AI / Data Science, or Engineering Management.
· Recommended: Coursework or experience in product engineering, information technology / digital twins / AI / ML / Data Science, management and analytics in operations & supply chain / logistics, engineering management, and systems engineering.
Assignments and Grading
Component Weight Description
Discussion Reflections 15% Critical weekly engagement on leadership and systems topics.
Case Study Analyses 20% Application of Lean, Six Sigma, AI / ML / Data Science / Digital twins and leadership frameworks.
Leadership Reflection
Journals 15% Ongoing leadership development and behavioral reflection.
Component Weight Description
Midterm Analytical
Assignment 20% Operational or financial improvement assessment using data / AI / ML.
Mini Project 30% Design, analysis, and presentation of a leadership/OpEx project.