Smart Manufacturing

Smart manufacturing is a convergence of modern data science techniques and artificial intelligence to form the factory of the future. Smart manufacturing is about increasing efficiency and eliminating pain points in your system. It’s characterized by a highly connected, knowledge-enabled industrial enterprise where all organizations and operating systems are linked, leading to enhanced productivity, sustainability, and economic performance. 

MIT Professional Education's Smart Manufacturing online program brings together cutting-edge technology like machine learning, Internet of Things, and data analytics to understand the current transformation of the manufacturing sector. 

In keeping with MIT’s founding principle Mens et Manus (Mind and Hand)—the synergy of theory and practice being at the heart of the learning experience—you’ll learn by doing. The program centers around a smart machine—a fiber extrusion device, fondly referred to as FrED—to demonstrate concepts as well as incorporate problem-solving skills into the curriculum. Furthermore, you'll have a chance to engage with your peers and expert learning facilitators to explore how these concepts can be leveraged in your organization. Regardless of where you are on your smart manufacturing journey, this program provides the latest thought leadership in smart manufacturing techniques. The factory of the future is here. 

English Language Course (Learn more)

  • February 4, 2020 - April 22, 2020 (Register Now!)
  • May 7, 2020 - July 23, 2020 (Registration opens February 12)

Who is this program for

This program is somewhat technical in nature, however this program material is highly accessible for those new to smart manufacturing concepts, while also being valuable for those who already have some experience with these concepts. There are no prerequisites for this program. It is designed for:

  • Plant managers working in manufacturing
  • Design and manufacturing engineers seeking to learn about data and modelling in a manufacturing environment
  • Data scientists looking to apply their craft to the growing field of smart manufacturing
  • Consultants who want to add value around the latest technology transformations in manufacturing
  • Functional and cross-functional teams are encouraged to attend together to accelerate the smart manufacturing adoption process

What's in the Course

Module 1: Introduction to Smart Manufacturing and FrED 

  • Identify global trends bringing major changes to society, products, and the manufacturing process 
  • Learn how FrED serves as a prototype for innovations that are possible within smart manufacturing 

Module 2: Analyzing Data: A Visualization Approach 

  • Explore the convergence of manufacturing expertise and data science expertise in the field of smart manufacturing 
  • Use time series analysis to understand FrED 

Module 3: Modeling to Make Sense of Data

  • Build models to examine and improve FrED
  • Explore how the length of a production run can affect results

Module 4: Sensors 

  • Review the integral role that sensors play in smart manufacturing 
  • Evaluate sensors and assess the types of data that sensors produce 

Module 5: Control of Manufacturing Processes

  • Explore manufacturing process control, the role of feedback, process modeling, and monitoring
  • Discuss actual versus predicted dynamics

Module 6: Machine Vision 

  • Take test measurements using a camera 
  • Explore how machines use cameras and images to inform decisions and improve the manufacturing process 

Module 7: Applications of Machine Vision 

  • Explore applications of machine vision to video search, sports and medicine 
  • Discuss applications of machine vision in additional contexts

Module 8: Model Fitting and Sensitivity Analysis 

  • Make the connection between machine vision as a tool and statistical process control 
  • Explore the process of discovering best fit for a model 

Module 9: Statistical Process Control

  • Apply statistical process control to a manufacturing setting
  • Integrate deterministic and random variation

Module 10: Advanced Data Analysis 

  • Work with datasets derived from manufacturing process to control multiple machines 
  • Explore concepts in cloud computing to control multiple machines

About the Instructor


Principal Research Scientist, MIT Mechanical Engineering

Director, Master of Engineering in Manufacturing Program, Massachusetts Institute of Technology

Director of MIT.nano, MIT

Devavrat Shah

Professor Anthony is the co-director of MIT’s Medical Electronic Device Realization Center and associate director of MIT.nano. With over 25 years of experience in product realization, Professor Anthony designs instruments and techniques to monitor and control physical systems. His work involves systems analysis and design, calling upon mechanical, electrical, and optical engineering, along with computer science and optimization. 

He has extensive experience in market-driven technology innovation, product realization, and entrepreneurship and commercialization at the intersection between information technology, design, and advanced manufacturing. Professor Anthony spent the first part of his career as an entrepreneur. He advanced and directed the development of products and solutions for the industrial and scientific video markets. He has been awarded 20 patents, published over 50 peer-reviewed articles, and won an Emmy from the Academy of Television Arts and Sciences for innovations in sports broadcasting.