Master advanced supply chain network design to optimize cost, service, and sustainability in complex business environments.
Master advanced supply chain network design to optimize cost, service, and sustainability in complex business environments.
This intermediate-level course from MIT's Center for Transportation & Logistics offers a comprehensive exploration of strategic supply chain systems planning and network design. You'll learn to make informed decisions about production and distribution facility configuration, sourcing strategies, and customer service approaches. The course emphasizes practical applications, teaching you to use Python-based tools and interactive visualizations to implement quantitative models. You'll start with foundational network models and progress to complex multi-tier, multimodal networks. The curriculum covers revenue and inventory integration into supply chain design models, equipping you with the analytical skills to balance competing objectives effectively. By the end of the course, you'll be able to translate real-world business problems into formal mathematical models and provide actionable recommendations based on your analyses.
Instructors:
English
English
What you'll learn
Analyze key challenges in contemporary supply chain management
Apply analytical tools for strategic supply chain network design
Develop Python-based models for supply chain optimization
Integrate revenue and inventory considerations into network design
Optimize facility locations and transportation networks
Balance cost, service, and sustainability in supply chain decisions
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 4 modules in this course
This course provides a comprehensive overview of advanced supply chain systems planning and network design. It begins with an introduction to key challenges and decisions in supply chain management, emphasizing the strategic importance of network design. Students learn to use analytical tools and techniques, including Python programming, to support decision-making. The curriculum covers a range of topics, from basic transportation and facility location problems to complex multi-tier network structures. Advanced concepts include incorporating revenue considerations, inventory management, and multi-objective optimization into supply chain models. Throughout the course, students apply these concepts to real-world case studies, developing the ability to translate business problems into formal mathematical models. The course also integrates interactive visualization tools from MIT's CAVE Lab, providing hands-on experience in analyzing and interpreting supply chain network designs.
Introduction to Supply Chain Network Design and Basics of Python for Supply Chain Network Design
Module 1
Basics of Supply Chain Network Design
Module 2
Generalized Supply Chain Network Design problem and incorporating revenue
Module 3
Inventory considerations in Supply Chain Network Design
Module 4
Fee Structure
Instructors
1 Course
Pioneer in Data-Driven Supply Chain Network Design and Analytics
Dr. Milena Janjevic is a Research Scientist at MIT's Center for Transportation & Logistics, where she leads groundbreaking research in supply chain network design. Her work focuses on improving decision-making through data-driven optimization and simulation models integrated with interactive visual tools. After earning her doctorate and master's in Engineering with specializations in Logistics from Université libre de Bruxelles in Belgium, she gained valuable industry experience at McKinsey & Company, working on projects in telecommunications, insurance, and retail sectors. During her doctoral studies, she was a Visiting Scholar at the Center of Excellence for Sustainable Urban Freight Systems at Rensselaer Polytechnic Institute, where her research concentrated on optimal design of urban logistics systems using multi-tier distribution networks and electric vehicles. Her current research portfolio includes developing customer-centric supply chains, sustainable network design, and improving last-mile delivery systems. She has worked extensively with global organizations, including a notable project with a pharmaceutical company to design last-mile channel strategies for new product launches. Her expertise extends to urban freight policy, infrastructure design, and the development of visual analytics decision-support tools that enable intuitive interaction with quantitative methods.
1 Course
Pioneer in Intelligent Logistics Systems and Supply Chain Analytics
Dr. Matthias Winkenbach is a Principal Research Scientist at MIT's Center for Transportation & Logistics, where he leads groundbreaking research in urban logistics and supply chain innovation. After earning his Ph.D. in Logistics and Masters in Business from WHU – Otto Beisheim School of Management, with additional studies at NYU Stern and HEC Montréal, he has established himself as a leading expert in intelligent logistics systems. As founder and director of the MIT CAVE Lab, he develops human-centric interfaces that make advanced supply chain analytics more accessible through interactive visualization and natural language controls. His research focuses on multi-tier distribution network design, urban freight policy, and data analytics in urban logistics contexts. His industry impact includes collaborations with major companies like Volkswagen, Deutsche Telekom, and McKinsey & Company. His excellence has been recognized through numerous awards, including the Science Award for Supply Chain Management from the German Logistics Association (2014), finalist status for the Daniel H. Wagner Prize (2015), and the Transportation Science Meritorious Service Award (2022). Currently serving as an Associate Editor for Transportation Science, he regularly publishes in both academic journals and practitioner-oriented outlets like the Wall Street Journal and Sloan Management Review.
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