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Yossi Sheffi, PhD

Director, MIT Center for Transportation & Logistics | Director, MIT Supply Chain Management Program | Elisha Gray II Professor of Engineering Systems, MIT MIT Center for Transportation & Logistics

  • Cambridge MA

Dr. Sheffi is an expert in systems optimization, risk analysis, and supply chain management.

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Biography

Dr. Yossi Sheffi is a professor at the Massachusetts Institute of Technology, where he serves as Director of the MIT Center for Transportation and Logistics (MIT CTL). He is an expert in systems optimization, risk analysis, and supply chain management, which are the subjects he teaches and researches at MIT. He is the author of many scientific publications and nine books.

Under his leadership, MIT CTL launched many new educational, research, and industry/government outreach programs, leading to substantial growth. He founded the MITx MicroMasters in Supply Chain Management. He is the founder and the Director of MIT's Master of Supply Chain Management degree. He also led the international expansion of MIT CTL by launching the Supply Chain and Logistics Excellence (SCALE) global network of academic centers of education and research. The network includes centers modeled after MIT CTL in Zaragoza, Spain; Bogota, Colombia; and Kuala Lumpur, Malaysia.

From 2007 to 2011 he served as the Director of the MIT Engineering Systems Division, where he set a strategy, revamped the PhD program, and set the division for future growth.

Outside the university Professor Sheffi has consulted with governments and leading manufacturing, retail and transportation enterprises all over the world. He is also an active entrepreneur, having founded and co-founded five successful companies:

• Princeton Transportation Consulting Group Inc.
• LogiCorp Inc.
• e-Chemicals Inc.
• Syncra Inc.
• Logistics.com Inc.

Dr. Sheffi was recognized in numerous ways in academic and industry forums and was on the cover of Purchasing Magazine and Transportation and Distribution Magazine. In 1997 he won the most prestigious recognition given by the Council of Logistics Management—the Distinguished Service Award. In 2006 he won the Aragón International Prize. In 2010 he became an honorary Doctor (Doctor Honoris Causa) of the University of Zaragoza in Spain and in 2011 he was awarded the Salzberg Medal and Award for "outstanding leadership and innovations in Supply Chain management" by the University of Syracuse. He is also a life fellow of Cambridge University's Clare Hall College. View a complete list of awards here.

He obtained his B.Sc. from the Technion in Israel in 1975, his S.M. from MIT in 1977, and Ph.D. from MIT in 1978. He now resides in Boston, Massachusetts.

Areas of Expertise

Systems Optimization
Supply Chain Management
Risk Analysis

Education

Massachusetts Institute of Technology

PhD

Civil Engineering

1978

Massachusetts Institute of Technology

MEng

Civil Engineering

1976

Technion - Israel Institute of Technology

BEng

Civil Engineering

1975

Languages

  • English

Media Appearances

What’s Behind Shein’s $1.4B China Bet

Sourcing Journal  online

2026-02-24

“It is clear that Shein will invest in better supply chain processes, which means they will be faster to market and faster to customize items to consumers’ wishes,” said Yossi Sheffi, the Elisha Gray II professor of engineering systems at the Massachusetts Institute of Technology. “Of course, getting closer to Beijing may raise the ire of the administration and they may face higher tariffs.”

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The Supply Chain Threat You May Not Have Seen Coming

Risk and Reliance Hub  online

2026-03-04

Power-hungry data centers and other risks threaten supply chains’ access to reliable electric energy.

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Lobster heist was third case of seafood theft in New England in recent weeks

The Boston Globe  online

2025-12-29

However, recent cases in the United States show that fraudsters are getting more sophisticated, said Yossi Sheffi, an engineering systems professor at MIT.

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Research Papers

Estimating the task content of work: workforce design for AI-driven human-robot collaboration in intralogistics

International Journal of Production Research

Pierre Bouquet, Nicolò Piergiovanni Bagnoli, Yossi Sheffi

2026-03-20

This paper addresses the challenge of strategic workforce planning for AI-driven human-robot collaboration (AI-HRC) in intralogistics. We ask two questions: how can task-level full-time equivalent (FTE) estimates be constructed from existing labour statistics, and how can these estimates, combined with AI exposure metrics, inform strategic AI-HRC design and workforce planning? Drawing on U.S. Bureau of Labor Statistics employment data, O*NET occupational profiles, and task-level AI exposure scores, we develop a stochastic task-time framework that decomposes occupations into tasks and models task frequencies as probability vectors on the simplex. A covariance-completion procedure reconstructs task covariance matrices consistent with survey standard errors, enabling the translation of occupational data into task-level and detailed work activity (DWA)-level FTE estimates with uncertainty bounds.

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Across the Atlantic: Early Labor Market Responses Following the Introduction of AI in the United States and the European Union

MIT Center for Transportation & Logistics Research Paper

Nicolo Bagnoli, Pierre Bouquet, Amin Kaboli, Yossi Sheffi

2026-02-03

We examine early labor market adjustments in the period associated with generative AI (Artificial Intelligence) introduction by comparing employment dynamics in the US (United States) and the EU (European Union). Using large-scale workforce data linked to task-based AI exposure measures, we estimate within-firm employment reallocation by seniority and occupation while absorbing firm-level shocks. Across both regions, early-career employment contracts after 2022, with systematically larger relative declines in higher-exposure groups. Once firm-level shocks are controlled for, the magnitude of the declines is similar across the US and EU Moving beyond exposure-quintile aggregation to an occupation-level framework reveals substantial heterogeneity that aggregate analyses obscure. Within the same occupation, employment responses vary across seniority levels.

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News Sentiment as a Dynamic Predictor of Job Automation Risk

MIT Center for Transportation & Logistics Research Paper

Pierre Bouquet, Yossi Sheffi, Amin Kaboli

2026-01-30

As artificial intelligence increasingly disrupts job and task structure, it is essential for companies and society, in general, to anticipate which tasks are at risk of automation and how these risks can guide workforce management strategies to proactively reskill employees, restructure roles, and optimize operations. To address these challenges, we introduce a machine learning pipeline that leverages news sentiment as a dynamic proxy for job automation risk assessment. By processing two million news articles, the model computes exposure scores at the task, job, and sector levels, enabling both historical trend analysis and real-time monitoring. Our findings demonstrate that these exposure scores align with prior studies that use rigorous, expert-driven methods. Through its dynamic evaluation, this approach models the impact of AI innovations and can help inform strategies for workforce transformation.

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Event Appearances

A Vision for the Future of AI in Supply Chain

Maersk CEOs Summit  Panamá Province, Panama

2026-06-24

Supply Chains, A.I., and the Future of Work

Crossroads Conference - MIT  Cambridge, MA

2026-04-14

Supply Chain Risk and Resilience

MIT Research and Development Conference  Cambridge, MA

2025-11-18

Awards

Best Indie Book Award: Nonfiction

BIBA
2020

Winter Reading Guide: The New (Ab)Normal

Inbound Logistics
2021

Sejong Book Award: Balancing Green

Korean Ministry of Culture, Sports and Tourism
2021