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Advanced Manufacturing Systems Analysis
This course is part of Principles of Manufacturing MicroMasters.
This comprehensive course, part of MIT's Principles of Manufacturing MicroMasters program, explores complex manufacturing systems analysis. Students learn to optimize manufacturing performance and control costs through advanced analytical methods. The curriculum covers Multi-Part-Type Manufacturing Systems, Material Requirements Planning (MRP), Multi-Stage Control, Scheduling, Simulation, and Quality management. Participants develop intuition about stochastic production lines, understand inventory buffer costs, and learn to create effective production system management policies. This course builds upon Introduction to Manufacturing Systems and prepares students for roles in operations and supply chain management.
3.8
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What you'll learn
Analyze and optimize multi-part manufacturing systems performance
Implement material requirements planning for efficient production management
Develop stochastic production line models and simulation techniques
Design effective manufacturing process control strategies
Optimize inventory management and cost control methods
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This advanced manufacturing systems course focuses on analyzing and optimizing complex production environments. Students learn to manage multi-part manufacturing systems, implement material requirements planning, conduct simulations, and develop control strategies for production lines. The course emphasizes practical applications of theoretical concepts in modern manufacturing settings.
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Principles of Manufacturing MicroMasters
Instructors
10 Courses
Manufacturing Systems Pioneer and Control Theory Expert
Stanley B. Gershwin has shaped manufacturing systems engineering through a remarkable career spanning over 45 years at MIT. After earning his B.S. in Engineering Mathematics from Columbia University and Ph.D. in Applied Mathematics from Harvard University, he began his journey through positions at Bell Labs and Draper Laboratory before joining MIT in 1972. As a Senior Research Scientist in MIT's Department of Mechanical Engineering, he developed groundbreaking mathematical methods for predicting and optimizing production line performance. His seminal work "Manufacturing Systems Engineering" (1994) became a cornerstone text in the field. Currently serving as Chief Scientific Officer at Dillygence since 2017, he continues to advance Industry 4.0 manufacturing solutions. His research contributions span manufacturing systems control, real-time scheduling, and decomposition methods for large-scale systems, earning him numerous accolades including IEEE Fellowship and multiple IIE Best Paper Awards. He has collaborated with major manufacturers including Boeing, General Motors, and Hewlett Packard, while developing the MIT Principles of Manufacturing online program. His work has garnered over 13,000 citations, reflecting his profound impact on manufacturing systems theory and practice.
9 Courses
A Visionary Leader in AI and Healthcare Innovation
Regina Barzilay, born in 1970 in Chișinău, Moldova, is a distinguished Israeli-American computer scientist who serves as the School of Engineering Distinguished Professor for AI and Health at MIT. As the AI faculty lead at MIT's Jameel Clinic, she has revolutionized the intersection of artificial intelligence and healthcare. After emigrating to Israel at age 20, she completed her education at Ben-Gurion University before earning her PhD from Columbia University. Her groundbreaking work spans natural language processing, deep learning applications in chemistry, and oncology. A personal battle with breast cancer in 2014 inspired her to focus on medical AI research, leading to innovations like the Mirai AI model for breast cancer detection and the Sybil system for predicting lung cancer risk. Her achievements include developing Halicin, a powerful new antibiotic compound discovered through machine learning. Barzilay's exceptional contributions have earned her numerous prestigious accolades, including the 2017 MacArthur "Genius Grant," the 2020 AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, and election to both the National Academy of Medicine and the National Academy of Engineering. Her work continues to push boundaries in AI applications for drug discovery and medical diagnostics, making her one of the most influential figures in the field of artificial intelligence and its practical applications in healthcare.
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