May 25, 2026  
2026-2027 Undergraduate Academic Catalog 
    
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2026-2027 Undergraduate Academic Catalog
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BUS 3805 - Lean-Six Sigma Techniques

3 lecture hours 0 lab hours 3 credits
Course Description
This course introduces and applies the skills and tools necessary to conduct Lean initiatives within the framework of the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) approach. This course investigates how the Lean framework is enhanced by artificial intelligence (AI). Students will explore how AI technologies-such as machine learning, predictive analytics, and intelligent automation-can accelerate data analysis, uncover hidden patterns, and optimize decision-making throughout the DMAIC cycle.
Prereq: None
Note: None
This course meets the following Raider Core CLO Requirement: None
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Implement the House of Lean to reduce the eight forms of waste in business
  • Utilize Six Sigma tools and techniques to identify defects
  • Integrate the five principles of Lean and apply the concept of value added vs. non-value-added activities
  • Adapt the Six-Sigma DMAIC (define, measure, analyze, implement and control) approach to an existing process to generate measurable results
  • Discuss the benefits of a Lean-Six Sigma culture for organizations
  • Identify value-added activities and sources of waste in operational workflows using traditional and AI-assisted tools.
Prerequisites by Topic
  • None
Course Topics
  • Lean and Six Sigma methodology
  • Lean and Six Sigma tools and techniques: 5S, cellular layouts, and production leveling techniques, etc.
  • A3 presentation format
  • Kaizen, JIT, and Kanban initiatives
  • Financials implications of projects
  • Project updates and management
  • 8 wastes of Lean
  • DMAIC
  • House of Quality
  • Culture and continuous improvement
  • Stakeholder management
  • Process mapping and value chain analysis
  • Use AI for brainstorming alternative solutions
  • AI use in monitoring, predicting, and optimizing processes
Coordinator
Dr. David Rollins


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