Exploring and quantifying the impact of rapidly evolving digital technologies and AI as they reshape end-to-end (E2E) supply chains
What is the Digital Supply Chain Transformation Initiative?
Digital Supply Chain Transformation is the strategic imperative for competitive advantage in today’s volatile landscape. It fundamentally reshapes operational capabilities, granting organizations the essential agility and resilience they need. This shift secures measurable impacts, including:
- Up to 50% process cost reductions.
- Up to 20% new revenue gains.
- Increased trust in AI decisions.
- Exponential scalability for processes.
Our Research Initiatives
Human-AI Collaboration
By combining human insights with the power of artificial intelligence, organizations are fundamentally upgrading their core operational performance.
Scaling End-To-End Automation Roadmap
Exploring how to move from automation pilot projects to full integration, from suppliers to final customers, fundamentally transforming core tasks to deliver exponential operational output.
Autonomous Operations
Achieving true operational independence, where systems self-manage to guarantee consistent and efficient performance.
Executive Education
Supply Chain Management: Leading with AI and Digital Transformation
This 6-week online executive program, offered with MITx Pro and Emeritus, provides a holistic view of digital transformation in the supply chain. The curriculum focuses on the strategic perspective and the necessary digital supply chain visions and capabilities. We examine real case studies and essential topics for discussions, including:
- Digital Supply Chain Transformation Roadmaps
- End-to-End Visibility
- Human-AI Collective Intelligence
- Artificial Intelligence
- New Technological Trends in Supply Chain Management
Selected Research Partners
Highlights
Human-AI Collaboration: Boosting Supply Chain Performance
Revolutionize cold chain: an AI/ML driven approach to overcome capacity shortages
This research investigates how Artificial Intelligence (AI) and Machine Learning (ML) forecasting methodologies can be leveraged for cold chain capacity planning, specifically utilizing Prophet and Seasonal Autoregressive Integrated Moving Average parametrized through grid search. In collaboration with Americold, the world’s second-largest refrigerated logistic service provider, the study explores the challenges and opportunities in applying AI/ML techniques to complex operations covering 385 customers and a capacity of 73,296 pallet positions.
From natural language to simulations: applying AI to automate simulation modelling of logistics systems
Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling.
Pair People and AI for Better Product Demand Forecasting
A new framework helps leaders orchestrate human and AI agents to accurately forecast product demand.
Ready to get involved?
Contact Us
1 Amherst Street, MIT Building E40-379
Cambridge, MA 02142
United States
digitalsc@mit.edu