Manufacturing Process Control I

Prerequisites: Engineering Undergraduate preparation; some knowledge of basic manufacturing processes. Knowledge or probability theory is helpful but not necessary. Learn how to model variations in manufacturing processes and develop methods to reduce and control deterministic variations to achieve consistent process quality. 8 Weeks 10 – 12 Hours each week recommended

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About This Course:

Randomness is inherent in all processes including manufacturing. The fundamental concepts taught in this course will help learners develop powerful statistical process control methods that are the foundation of world-class manufacturing quality.

As part of the Principles of Manufacturing MicroMasters program, this course will introduce statistical methods that apply to any unit manufacturing process. We will cover the following topics:

  • Recognizing inherent variability in continuous production
  • Identifying sources of process output variation
  • Describing variation in a structured manner
  • Applying basic probability and statistics concepts to characterize process variation
  • Differentiating between design specifications and process capability
  • Synthesizing novel approaches to unfamiliar situations by extending the core material (i.e. go beyond the “standard” uses).
  • Assessing the appropriateness of various statistical methods for a variety of problems

Develop the engineering and management skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing & Design program.


What You’ll Learn:

  • Variation modeling using the theory of Random Processes
  • Statistical Process Control (SPC) foundations and applications
  • Xbar, EWMA, CUSUM and discrete event methods for detecting process problems
  • Methods for analyzing process changes by looking at general process physics
  • How to apply these methods to achieve world-class quality in unit manufacturing processes


  • Engineering Undergraduate preparation
  • Some knowledge of basic manufacturing processes
  • Knowledge or probability theory is helpful but not necessary

Meet Your Instructors:

David Hardt - Pearson Advance

David Hardt

Ralph E. and Evelyn F. Cross Professor of Mechanical Engineering at Massachusetts Institute of Technology Professor Hardt is a graduate of Lafayette College (BSME, 1972) and MIT (SM, PhD, 1978). He has been a member of the Mechanical Engineering faculty at MIT since 1979. His disciplinary focus is system dynamics and control as applied to manufacturing.

Duane Boning

Co-Director, MIT Leaders for Global Operations Program at Massachusetts Institute of Technology Dr. Duane S. Boning is the Clarence J. LeBel Professor in Electrical Engineering, and Professor of Electrical Engineering and Computer Science in the EECS Department at MIT. He is currently Director of the MIT/Masdar Institute Cooperative Program. Dr. Boning received his S.B. degrees in electrical engineering and in computer science in 1984, and his S.M. and Ph.D. degrees in electrical engineering in 1986 and 1991, respectively, all from the Massachusetts Institute of Technology.

Experience Level


Learning Partner

Massachusetts Institute of Technology




Principles of Manufacturing

Program Type



Engineering Math
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