The MIT Center for Transportation & Logistics (MIT CTL) is part of the Engineering Systems Division in the School of Engineering at the Massachusetts Institute of Technology.   For more than five decades, MIT CTL has been a world leader in supply chain management education and research. MIT CTL has made significant contributions to supply chain and logistics and has helped numerous companies gain competitive advantage from its cutting-edge research.  Visit our website to learn more about our educational offerings, research initiatives, and outreach opportunities! https://ctl.mit.edu/  

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Issued by The Institute for Operations Research and the Management Sciences (INFORMS)
Sep 2022

Transportation Science Meritorious Service Award

Issued by International Conference on City Logistics
Jun 2017

Best Paper Award

Issued by The Institute for Operations Research and the Management Sciences (INFORMS)
Nov 2015

Finalist, Daniel H. Wagner Prize for Excellence in Operations Research Practice

Issued by Bundesvereinigung Logistik (BVL)
Oct 2014

Science Award for Supply Chain Management

MIT Center for Transportation & Logistics : Director of Research, Feb 2024 – Present

MIT Transportation Education Committee : Chair, May 2026 – Present

MIT Intelligent Logistics Systems Lab : Founder & Director, Jun 2024 – Present

MIT CAVE Lab : Founder & Director, Jan 2017 – Present

Transportation Science (INFORMS) : Associate Editor, Cross-Cutting Methods & Emerging Technologies

Transportation Research Part E: Logistics & Transportation Review (Elsevier) : Editorial Advisory Board Member

World Economic Forum : Co-Curator, Strategic Intelligence on Supply Chain, Logistics & Transportation Transformation, 2023 – Present

MIT Climate & Sustainability Consortium : Supporting Researcher

INFORMS : Member, 2012 – Present; Organizing Committee, 2026 Transportation Science & Logistics Conference

Production and Operations Management Society (POMS) : Member, 2013 – Present

Deakin University Urban Logistics Lab : Scientific Advisory Board Member, 2019 – Present

Last-mile delivery with drone resupply at transshipment platforms

Technology and energy choices for fleet asset decarbonization under cost uncertainty

Revitalizing municipal solid waste recycling: Review of current U.S. policies and potential directions for the circular economy

Integrating mobile parcel lockers into last-mile delivery networks: an operational design for home delivery, stationary, and mobile parcel lockers

A matheuristic for the Two-Echelon Multi-Trip Vehicle Routing Problem with mixed pickup and delivery demand and time windows

2021 Amazon Last Mile Routing Research Challenge: Data Set

The MIT Center for Transportation & Logistics (MIT CTL) and Amazon engaged with a global community of researchers across a range of disciplines, from computer science to business operations, to supply chain management, challenging them to build data-driven route optimization models leveraging massive historical route execution data and machine learning models.
While we congratulated the winning teams and all participants in the news, Dr. Matthias Winkenbach joins today's Frontiers to share some insights and outcomes that the production of a research challenge brought about.
He speaks about how convening researchers from across all levels of academia and supplying rich data and a compelling problem, may drive more new research in areas of inquiry that are sparsely published on.
Learn more about the Challenge: https://routingchallenge.mit.edu/
Learn more about the Megacity Logistics Lab: https://megacitylab.mit.edu/

Amazon Last-Mile Routing Research Challenge Uncovers New Research Methods on a Global Scale

If the pandemic has taught us anything, it’s the value of having goods delivered right to our front door. From fast food ordered on an app, to next day shipping from an online store, the distance from click to delivery is getting shorter all the time. What most of us don’t realize is that the hardest part of delivering an order is the last mile.More Intelligent Tomorrow host Ben Taylor asked researcher Dr. Matthias Winckenbach to explain what he means by “the last mile” and why it’s so difficult to cover.The term “the last mile” refers to the home stretch of the supply chain where goods are moved from the final distribution location to the customer. It can represent as much as 40% of the overall supply chain costs despite almost always being the shortest segment. This is why there’s a lot of effort going into optimizing it."The cost and complexity of last mile delivery very much comes from the fragmentation of shipments."Customer expectations for faster, more flexible deliveries, with shorter lead times are running up the cost of covering the last mile. But financial costs aren’t the only concern. Our increase in demand might be having a negative impact on the environment as well. Increased demand means fewer opportunities for suppliers to consolidate, and that means more trips are needed to deliver the additional load of packages. Those extra trips use more fuel and put more traffic on the road.The challenge is that you're adding complexity to the system. Now you're managing inventory across many more locations while trying to predict demand in any one location. With this move to hyper localized inventory, the traditional methods of optimization must be updated.There’s a theoretical optimal solution to routing a package. It’s sometimes referred to as the Traveling Salesman Problem. But variables like traffic, customer availability, parking, and even the mood of the driver can impact the delivery in ways the optimal solution can’t predict. And all these things can change once the delivery is underway, meaning the optimal solution may no longer be valid. AI/ML can complement traditional planning methods to create better routes.Ben wonders if aerial delivery drones are the answer. They can fly directly to a location and don't have to deal with things like traffic and parking. Matthias thinks the answer seems to be no, at least not right away. There are regulatory, technological, and societal issues we need to overcome first. The density of deliveries in an urban area could lead to large swarms of drones buzzing overhead. The sky would fill up quickly.From an economic point of view, it’s hard to beat an effectively designed ground delivery route. Drones are still expensive, and you’d need up to 300 of them to match the capacity of a single truck. But one day, there might be a hybrid solution where a drone and delivery truck could collaborate to deliver your package. "A lot of people don't realize how difficult it is to get that package to the doorstep every morning."Listen to this episode of More Intelligent Tomorrow to learn:What the last mile meansHow it increases the cost of goodsThe impact the last mile has on the environmentIf drones might be the answer to the problemWhat’s needed so AI/ML can help solve the problemIf delayed gratification is the answer

The Cost and Complexity of Last Mile Delivery - Dr. Matthias Winkenbach

Vehicle routing is one of the most well-understood, extensively studied problems in both history and academia—it's been studied by academics since the early 1800s. Yet 200 years later, optimal efficiency remains just out of reach. And given the enormous increase in pressure for consumer expectations in recent years, that remaining "last mile" of vehicle routing  efficiency could, on a global scale, make a huge difference to a huge number of people.
In this episode, we're joined by Matthias Winkenbach, Director of Research at the MIT Center for Transportation & Logistics. Matthias is an expert in urban logistics, last-mile delivery, and vehicle routing, and he has just launched a new lab, the MIT Intelligent Logistics Systems Lab, that will use AI and machine-learning techniques to tackle today's vehicle routing challenges—and that could make a major impact on vehicle routing solutions where traditional methods and algorithms have come up just short.
intelligent.mit.edu
Host & Executive Producer: Benjy Kantor
Marketing Writer & Producer: Dan McCool
Sound Editor: David Benjamin Sound
Audio Engineer: Kurt Schneider

Vehicle Routing in the Age of AI

Artificial Intelligence is frequently hailed as a transformative force for global supply chains, yet the gap between technological promise and operational reality remains a central challenge for industry leaders. In this episode, host Dr. Matthias Winkenbach, Director of Research at MIT CTL, leads a nuanced discussion on the transition from AI hype to the implementation of functional "decision technology." Joining the discussion are three researchers from MIT CTL who bring diverse perspectives to the AI landscape. Willem Guter of the MIT Intelligent Logistics Systems Lab unpacks the intersection of machine learning and traditional optimization in warehouse robotics, while Dr. Elenna Dugundji, director of the MIT Deep Knowledge for Supply Chain and Logistics Lab, explains the evolution of demand forecasting and the importance of "deep knowledge" in predictive modeling. Dr. Bryan Reimer, founder of the MIT AgeLab’s Advanced Vehicle Technology Consortium, rounds out the discussion by addressing the critical human factor in autonomous systems. Together, they examine the future of AI in sourcing and procurement, the complexities of human-AI interaction, and the necessity of building decision-support tools that are grounded in real-world application rather than speculative promise.

Beyond the Hype: Decoding AI in Supply Chains

In this episode we sit down with Dr. Matthias Winkenbach, Director of Research at the MIT Center for Transportation & Logistics, Alejandro González, Software Business Unit Director at Mecalux, and Iñaki Fernández, Chief Technology Officer at Mecalux.
Mecalux, a provider of warehouse management solutions, is a founding research partner of the new MIT Intelligent Logistics Systems Lab, which is at the forefront of advancing logistics through innovative technology. We discuss how AI and machine learning are not just buzzwords but can actually create additional value in warehouse robotics, such as in the case of autonomous robots and software solutions that help companies manage demand out of a distributed network. 
Host & Executive Producer: Benjy Kantor
Marketing Writer & Producer: Mackenzie Berry
Sound Editor: David Benjamin Sound
Audio Engineer: Kurt Schneider

Smarter Warehouses: AI &amp; Robotics at the Forefront of Logistics Innovation

In this episode, Chris Caplice sits down with his MIT colleague and last mile transportation expert, Dr. Matthias Winkenbach. The duo discuss what it takes to build an effective last mile delivery strategy in urban areas with tactics such as:
Multi-tier distribution
On-demand delivery
Drone logistics

Their discussion wraps up with what Matthias thinks will stick around in the transportation industry as a result of COVID-19. Inam Iyoob then joins Chris for the latest North American truckload market rate trends and forecast powered by FMIC. 

[16:29] ICYMI, here’s the 2013 60 Minutes interview with Jeff Bezos where he unveils Amazon’s delivery drone concept: https://youtu.be/-qOBm3Iwlzo

Trends &amp; Innovations in Last Mile Delivery | Matthias Winkenbach

In this episode of the Freightvine Podcast, host Chris Caplice sits down with Dr. Matthias Winkenbach, Director of Research at the MIT Center for Transportation & Logistics, to explore how the “last mile” problem is evolving under modern logistics pressures. Winkenbach explains how rising customer expectations for speed and consistency are stretching traditional optimization models and how machine learning and generative AI are now being used alongside operations research to enable faster, more dynamic delivery decisions. The discussion also covers his shift in research focus from urban megacity logistics to warehouse and facility-level systems, highlighting the growing need to connect internal operations with broader supply chain networks. Finally, they examine how AI-driven scenario modeling is helping companies design more resilient supply chains and better prepare for future disruptions. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Matthias Winkenbach | New Approaches to Solving the Last Mile Puzzle

SCM.293 / 1.263 / 11.263 Urban Last-mile Logistics

SCM.275 Advanced Supply Chain Systems Planning and Network Design

SCM.275x: Supply Chain Design (MITx / edX MOOC)

MIT CTL Executive Education

MIT Sloan Executive Education

Graduate Certificate in Logistics & SCM (GCLOG)

MIT MicroMasters SCx Bootcamps

Dr. Matthias Winkenbach is a Senior Research Scientist at MIT — the highest-ranking academic research staff appointment at the Institute, recognizing independent research leadership equivalent to that of a tenured faculty member — and serves as Director of Research of the MIT Center for Transportation & Logistics (CTL). In this role, he shapes CTL’s strategic research agenda and oversees the translation of supply chain and logistics innovation into industry practice, leading a research organization of more than 40 researchers in partnership with a global network of companies. Across his career he has led more than 30 sponsored research projects with cumulative funding exceeding $15 million and supervised more than 50 graduate theses.

He is the founder and director of the MIT Intelligent Logistics Systems Lab, which applies operations research, artificial intelligence, and machine learning to transform logistics through intelligent automation, predictive analytics, and algorithmic decision-making — including recent work on drone-based delivery, fleet decarbonization, and warehouse automation. He also founded the MIT Computational Analytics, Visualization, and Education (CAVE) Lab, which develops human-centric interfaces that make advanced supply chain analytics accessible to decision-makers through interactive visualization and natural-language tools, and which created the world’s first open-source software development framework for visual supply chain analytics applications. Since May 2026, Dr. Winkenbach chairs the MIT Transportation Education Committee, serving as academic head of the MIT Master of Science in Transportation program and the MIT Interdepartmental Ph.D. Program in Transportation.

Dr. Winkenbach was the scientific lead of the 2021 Amazon Last Mile Routing Research Challenge, which engaged a global research community with real operational data and $175,000 in prizes, and served as Lead Guest Editor of the resulting Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. He is a Co-Curator of the World Economic Forum’s Strategic Intelligence on Supply Chain, Logistics & Transportation Transformation and a frequent keynote speaker at leading industry events worldwide.

Last-Mile Delivery

Supply Chain AI

Drone Logistics

Vehicle Routing

Warehouse Automation

Urban Logistics

Supply Chain Visualization

Intelligent Systems

English

German

French

MIT CTL Profile

Personal Website

MIT CAVE Lab

Google Scholar

ResearchGate

ORCID

OpenAlex Author Profile

MIT CAVE Open-Source Framework (GitHub)

World Economic Forum Contributor Page

Smarter Warehouses: AI & Robotics at the Forefront of Logistics Innovation

MIT-Mecalux research collaboration: Q&A with Matthias Winkenbach

60 Seconds with Matthias Winkenbach, Director of Research at MIT

3 Questions: Enhancing last-mile logistics with machine learning

Last-mile logistics – Interview with Matthias Winkenbach (MIT Research Scientist)

Thought starters - Implementing AI in the supply chain with Matthias Winkenbach

Trends & Innovations in Last Mile Delivery | Matthias Winkenbach

Commentary: Drone-Delivery Projects Must Look Beyond the Hype

Walmart Expands Online Grocery Delivery to 100 Cities

Augmented Reality to Revolutionize How Companies Manage Supply Chains, Says MIT Researcher

Research collaborations with the world's leading logistics, retail, and consumer brands.

Organizing Committee &amp; Lab Sponsor,

Plenary “Future Supply Chains” and customer panel with BMS, Cytiva, and McKinsey & Company

Featured Speaker

Intelligent Logistics Systems: How AI Will Transform the Way We Move, Store, and Deliver (Keynote)

Technology Impact on Supply Chains

AI-Driven Innovation in Logistics

Hear the latest in freight transportation, supply chain and logistics from the Freightvine podcast.

Freightvine

More Intelligent Tomorrow is a wide ranging exploration of the potential impact of AI on the world around us. On the podcast we meet some of the most extraordinary experts in the industry to discuss curious topics from aliens to AI consciousness as well as the practical changes in healthcare, business and society at large.

More Intelligent Tomorrow: a DataRobot Podcast

Thought leadership from MIT Center for Transportation & Logistics.

MIT Supply Chain Frontiers

[Content to be added]

experts-in-the-media-mits-dr-matthias-winkenbach-is-the-leading-expert-when-it-comes-to-logistics-and-evs

Director of Research, MIT Center for Transportation & Logistics | Senior Research Scientist, MIT | Chair, MIT Transportation Education Committee | Director, MIT Intelligent Logistics Systems Lab & MIT CAVE Lab

In the Green Car Reports article about the European debut of the new Mercedes-Benz eSprinter, Dr. Matthias Winkenbach is cited as a leading voice in logistics innovation and urban mobility. A sought-after global expert and Director of the MIT Megacity Logistics Lab, Dr. Winkenbach emphasizes that vehicles like the eSprinter represent far more than a shift from diesel to electric — they are key components of a new, data-driven logistics ecosystem. As he explains, the rise of e-commerce and rapid urbanization are forcing major cities to rethink how goods are moved efficiently and sustainably, and electric vans will play a critical role in that transition.

 Dr. Winkenbach highlights that the eSprinter’s design — with its modular battery system, enhanced range, and digital connectivity — positions it as an adaptable solution for modern delivery networks. He notes that smart routing, telematics integration, and real-time data sharing are transforming delivery vehicles into intelligent, connected assets capable of optimizing performance in dense urban environments. These capabilities, he suggests, are vital to reducing emissions while maintaining the speed and reliability that consumers expect in the age of same-day delivery.

The philosophy behind the new Sprinter models was to stop thinking about them as passive objects and incorporate them as fully connected parts of the “Internet of Things.” Dr. Matthias Winkenbach of the Massachusetts Institute of Technology spoke about the evolving importance of connecting vehicles at a recent Sprinter technology briefing. He noted that by 2025, the world’s 600 fastest growing cities will account for 25 percent of the global population and 60 percent of global GDP.  With the quick (and increasingly same-day) turnarounds and often-tight delivery windows demanded by online commerce, integrating the delivery vehicle itself into a company’s IT infrastructure allows maximum logistical efficiency. Combining this new logistical world with the eSprinter's electric powertrain makes for a potent package in the world of urban delivery, where Mercedes-Benz expects the van to find its biggest market. The company will only hint at possible new battery options in the (near) future for the eSprinter—though with this being the first new generation of Sprinter in 12 years, updates during its model life seem all but inevitable.  February 2018 - Green Car Reports  As one of the world’s most respected logistics experts, Dr. Winkenbach brings both academic insight and practical expertise to the conversation. His research on last-mile delivery and urban network design has influenced how cities and major corporations approach sustainable logistics. The article uses his perspective to underscore a larger point: that the Mercedes-Benz eSprinter is not just another electric van, but a symbol of how advanced design and data intelligence can reshape urban transportation for a cleaner, more efficient future.

The EV industry is in a constant state of evolution and change, and if you are a reporter looking to cover this topic, then let us help.

Dr. Matthias Winkenbach is available to speak with reporters - simply click on his icon now to arrange an interview today




Experts in the Media: MIT's Dr. Matthias Winkenbach is the leading expert when it comes to logistics and EVs

In this webinar, MIT research scientist Matthias Winkenbach discusses how interactive visualization and augmented reality technology will change the way we access, understand, and utilize complex systems and logistics data. Watch the full webinar: https://www.youtube.com/watch?v=NMNXEua7pSM

Using Visualization to Connect Humans and Analytics in Supply Chain Decision-Making

In this webinar hosted by Dr. Eva Ponce, the Director of the MITx MicroMasters Program in SCM, expert panelists Dr. Matthias Winkenbach of MIT CTL and Chris Huber of Mecalux, share how they are leveraging AI tools and algorithms—particularly in positioning inventory in complex omnichannel networks as well as the use of Autonomous Mobile Robots (AMRs)—to bring flexibility and optimize operations. Watch the full webinar: https://www.youtube.com/watch?v=XTQqkTD8l8Q

The Role of AI in e-Commerce and e-Fulfillment

Dr. Matthias Winkenbach, Director of the MIT Megacity Logistics Lab, discusses how novel technologies and advanced algorithms can enable future last-mile logistics services to create superior value for consumers and businesses. Watch the full lecture: https://www.youtube.com/watch?v=7_ZCA8_ntqQ

Last-mile Logistics on Steroids: How to Connect Humans, Machines, and Algorithms to Deliver to the Future Needs of Consumers

Global experts - intelligent insights. A focus on AI and the supply chain. 

In this series, global experts share insights on AI's impact on global supply chains and data centres, covering everything from product design and manufacturing, logistics and distribution to retail. These discussions highlight the importance of data, human interaction, efficiency and sustainability, as AI reshapes industries worldwide.

In this episode, we hear from Matthias Winkenbach, Principal Research Scientist at MIT Center for Transportation & Logistics. 

"I think some of the main areas of application of machine learning and AI in the supply chain industry in the future are going to be around demand prediction, inventory planning, network design, and any kind of complex optimisation problem that is becoming increasingly difficult to solve for the industry.  

I'm Matthias Winkenbach and I'm a Principal Research Scientist at the MIT Centre for Transportation and Logistics.  

The key ingredients to successfully implementing an AI strategy in your supply chain organisation is having a strategy around the AI compute architecture that you're going to use. So whether you're going to be able to build infrastructure in-house, or you're going to rely on third party cloud services, basically. Having a solid data strategy so that you have a good foundation to actually train models on. But I think the most critical part, however, is having the right people who can translate your actual business problems into the requirements for an AI model. 

So you need someone who understands your business well enough to be able to say, okay, this is the problem that we're trying to tackle first when we implement AI. And then obviously you need talent that can take that translation and turn it into an actual model implementation. So we are not yet at a point where we just say AI can solve everything in a better, faster, cheaper way. And that's kind of where we are currently pushing the boundary of research to still ensure that the domain experts who will hopefully end up using these models and methods, trust them. Developing AI methods or models that would be able to beat existing state-of-the-art methods in terms of solution speed, solution quality, but most importantly solution cost.  

In terms of AI adoption across the entire supply chain logistics industry, we are still at a very early stage, and that's because right now this is really cutting-edge technology that requires resources that not many companies in the industry currently possess. 

But hopefully as these methods become more mature and more applications are published, we do hope that this technology quickly propagates through the entire industry, and also smaller companies start adopting it more frequently. However, it also requires to basically say a few words of caution. Because this technology is highly powerful and it's powerful in a way that most of us don't fully understand what it is actually capable of. So be careful when it comes to sharing confidential information. Be mindful when it comes to investing into the resources and talent that you actually need to get AI models off the ground within your company. And also be aware that it will take time to build those capabilities. Yes, AI and machine learning is probably the next big thing in the supply chain logistics industry, but it's not the only thing you should be thinking about. Try to find the right balance between investing some of your budget for research development analytics into exploring this space, but not all of it. There is still a lot of research to be done."

Watch the full series here: https://www.goodman.com/about-goodman/thought-starters
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Our purpose at Goodman is to make space for all of our stakeholders’ greatest ambitions.

As global industrial property specialists, we own, develop and manage high-quality properties in strategic locations across Asia Pacific, Europe and the Americas. However, we’re conscious it’s not just what we do that’s important, but how we do it.

Goodman plans for the long term and looks at the big picture. We have the teams, scale, expertise, infrastructure and capital to develop long-term relationships with customers and partners around the world, but we’re still flexible enough to adapt to local business needs. 

We have a people-focused, sustainable approach that leads to positive outcomes for our business, our stakeholders and the world more broadly.

Find us on LinkedIn: https://www.linkedin.com/company/goodman/
Find us on Twitter: https://twitter.com/Goodman_Group
Find us on Instagram: https://www.instagram.com/goodman.group/

We interviewed Matthias Winkenbach, Principal Research Scientist at the Massachusetts Institute of Technology Center for Transportation & Logistics @MITCTL. He founded the MIT Computational Analytics, Visualization & Education (CAVE) Lab, where he leads a team of researchers, UI/UX designers and software developers to create technologies for visual supply chain analytics applications. Winkenbach has worked with numerous global industry partners to solve problems that have real-world impact on people, profits and the planet. He regularly advises start-ups and established industry players on projects involving supply chain analytics, logistics systems design and last-mile technologies. 

Watch this video next -- MIT Center for Transportation & Logistics launches new lab supported by Mecalux to research the potential of AI in logistics  https://youtu.be/wTVGKjuxPKU 

Chapters: 


Matthias Winkenbach, Director of Research at the @MITCTL (MIT Center for Transportation & Logistics) and the Intelligent Logistics Systems Lab, details the research collaboration between @mit and @MecaluxGroup.

This collaborative project aims to expedite the integration of self-learning artificial intelligence (AI) in logistics, with applications that hold significant potential for businesses and society.

The teams at the Intelligent Logistics Systems Lab and Mecalux will develop two key research areas:
✅ Increasing the productivity of autonomous warehouse robots.
✅ Training self-learning AI models to anticipate new customer purchasing habits.

Chapters: 


MIT-Mecalux research collaboration: Q&amp;A with Matthias Winkenbach

Complex data can be difficult to visualize and analyze. Working in cross-functional teams also adds a layer of complexity to making decisions from big data analysis. The CAVE Lab uses interactive visualization to improve data visibility, data analysis, and decision making for supply chain and logistics challenges.

Together with MIT CTL’s corporate partners, the lab can be leveraged as an interactive decision-making space. By co-developing interactive visual analytics applications addressing specific, data-driven decision problems of our partners and presenting them in a way that optimally leverages human perception and cognition, we aim to promote the effective future use of data and analytics by corporate decision-makers at all levels. 

Corporate engagement with the lab can be based on dedicated research and development projects and may entail interactive decision making and data analytics workshops with employees, customers, suppliers, or peers of the sponsoring partner at the lab.

Learn More: https://ctl.mit.edu/cave

MIT Computational Analytics, Visualization &amp; Education (CAVE) Lab

At Goodman, we make space for our customers’ greatest ambitions. That’s why we’ve spoken to inspirational experts around the world to uncover insights into the key trends shaping our customers’ businesses – today, tomorrow and beyond.

E-commerce, supply chain and urbanisation are just the beginning. We found out that what people want, will change cities. We challenged if data is enough to solve the complex logistics puzzle of last-mile delivery. And investigated what makes Gen Z tick as consumers.

Thought Starters is a short-form video series that delves into how changes to cities, society and spaces, will influence how we do business as the 21st century unfolds.

Matthias Winkenbach – Director of the MIT Megacity Logistics Lab and the MIT CAVE Lab looks at how businesses can use data and design to solve the challenges of urban distribution.

"Where you have your distribution facilities, what kind of facilities you have and how you use them, is basically the most strategic question that you have to answer if you want to master the urban last-mile. 

My name is Matthias Winkenbach and I am the direct of the MIT Megacity Logistics Lab and the MIT Cave Lab. 

The last-mile is typically what we refer to as the last leg of transportation to the final recipient. That is what we define as the urban last-mile. While the last-mile is just the shortest, final leg of the global supply chain, it's actually the most complex and also the most costly part. The final mile actually accounts to 40% of the overall supply chain cost, so that tells you how important it is to get that last-mile right. It's not about applying off-the-shelf solutions, it's about having tailor-made solutions to the specific urban environments that you care about. 

A very common problem in last-mile logistics is a driver deviating from the optimal route that was planned for him or for her. We were experimenting lately with a couple of machine learning tools, for instance, to use something simple like GPS data or just log files from the vehicle, like when which customer was being served and feeding that into these rather complicated models and then basically having a way to identify which customer was actually causing the disruption along the route. Which was otherwise only possible by talking to the driver directly and actually asking, "Why did you deviate?" which you obviously can't do at scale. So understanding both the behaviour of the driver and also extracting the local knowledge that this driver has about the urban environment that he or she operates in. 

Now we have a tool that just uses data to extract human knowledge from seemingly cold data. There's not a one-size-fits-all solution to any given city in the world. So a solution for last-mile logistics in New York City might look very different from the best solution for last-mile logistics in Sao Paolo or in Paris. 

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Our purpose at Goodman is to make space for all of our stakeholders’ greatest ambitions.

As global industrial property specialists, we own, develop and manage high-quality properties in strategic locations across Asia Pacific, Europe and the Americas. However, we’re conscious it’s not just what we do that’s important, but how we do it.

Goodman plans for the long term and looks at the big picture. We have the teams, scale, expertise, infrastructure and capital to develop long-term relationships with customers and partners around the world, but we’re still flexible enough to adapt to local business needs. 

We have a people-focused, sustainable approach that leads to positive outcomes for our business, our stakeholders and the world more broadly.

Find us on LinkedIn: https://www.linkedin.com/company/goodman/
Find us on Twitter: https://twitter.com/Goodman_Group
Find us on Instagram: https://www.instagram.com/goodman.group/

Why is the last-mile of delivery so costly and complex? Matthias Winkenbach

Thought starters - Global experts. Intelligent insights. 

Matthias Winkenbach – Director of the MIT Megacity Logistics Lab and the MIT CAVE Lab looks at how businesses can use a multi-tiered distribution approach to master last-mile logistics within urban environments. 

"Where you have your distribution facilities, what kind of facilities you have and how you use them, is basically the most strategic question that you have to answer if you want to master the urban last mile. 

The last mile is typically what we refer to as the last leg of transportation to the final recipient. That is what we define as the urban last mile. While the last mile is just the shortest, final leg of the global supply chain, it's actually the most complex and also the most costly part. The final mile actually accounts to 40% of the overall supply chain cost, so that tells you how important it is to get that last-mile right. It's not about applying off-the-shelf solutions, it's about having tailor-made solutions to the specific urban environments that you care about."
-----------------------------------------------------------------------------------------------------------------------------------------------------

Our purpose at Goodman is to make space for all of our stakeholders’ greatest ambitions.

As global industrial property specialists, we own, develop and manage high-quality properties in strategic locations across Asia Pacific, Europe and the Americas. However, we’re conscious it’s not just what we do that’s important, but how we do it.

Goodman plans for the long term and looks at the big picture. We have the teams, scale, expertise, infrastructure and capital to develop long-term relationships with customers and partners around the world, but we’re still flexible enough to adapt to local business needs. 

We have a people-focused, sustainable approach that leads to positive outcomes for our business, our stakeholders and the world more broadly.

Find us on LinkedIn: https://www.linkedin.com/company/goodman/
Find us on Twitter: https://twitter.com/Goodman_Group
Find us on Instagram: https://www.instagram.com/goodman.group/

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