Manufacturing Systems I
Manufacturing Systems I
Course Information
Certificate Track
Learn for Free
About this Course
In this course, part of the Principles of Manufacturing MicroMasters program, you will learn how to analyze manufacturing systems to optimize performance and control cost. You will develop an understanding of seemingly opaque production lines with a particular emphasis on random disruptive events – their effects and how to deal with them, as well as inventory dynamics and management.
Manufacturing systems are complex and require decision-making skills and analytical analysis. Managers and practitioners use a wide variety of methods to optimize the performance of manufacturing systems and control costs. The many processes and functions involved in building and maintaining these systems demand a high-level of knowledge.
In this course, you will learn about these various methods and processes. We will start with a review of probability and statistics, and then cover topics in linear programming, queueing theory, inventory management and the Toyota Production System (TPS). Lastly, we will introduce stochastic manufacturing systems models developed here at MIT.
The topics covered will provide the basis for learners to continue into the manufacturing field in such roles as an operations manager or supply chain manager.
Develop the 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
- Applications of basic probability models
- Building and solving optimization models
- Inventory dynamics and management
- Philosophy behind the Toyota Production System (TPS)
Prerequisites
Knowledge and comfortability with undergraduate-level calculus, probability and statistics.
Meet your instructors
David Hardt
Ralph E. and Evelyn F. Cross Professor of Mechanical Engineering
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. His research has been on flexible automation, and process control, with an historical emphasis on welding and forming processes, and a current focus on polymer micro embossing. In welding, he pioneered the use of multivariable control techniques for modeling and control of GMAW, and demonstrated the use of adaptive control in these systems. In the forming processes, he concentrated on the use of in-process measurements and real-time modeling to reduce sensitivity to machine and material variations, and has developed a flexible tooling and closed loop shape control that has implemented in the aerospace industry with specific uses for repair part manufacture. His more recent work has been in the hot micro-embossing process for micro-fluidic device manufacture and scale-up of soft lithography methods using roll to roll processes. In both cases the theme of the work is novel equipment design and overall equipment and process statistical control Prof. Hardt has taught classes in both Mechanical Engineering and Manufacturing, and has led the creation of a new graduate degree: Master of Engineering in Manufacturing" at MIT. This is the first professional degree offered by the ME Department at MIT, and is the culmination of many years of course and curriculum development. Prof. Hardt served as Director of the MIT Laboratory for Manufacturing from 1985 - 1992 and as Engineering Co-Director for the MIT Leaders for Manufacturing Program from 1993 to 1998. From 1999 to 2011 he was the MIT Chair of the Singapore MIT Alliance (SMA) Program: "Manufacturing Systems and Technology", a research and teaching collaboration with Nanyang Technological University in Singapore. He was a member of the MIT Commission on Productivity in an Innovation Economy, and served on the Workforce team on the Advanced Manufacturing Partnership program (AMP 1.0).
