Made possible by a grant from the UPS Foundation, the UPS Fellowship continues its mission to champion outstanding students with financial support of two exceptional students, one incoming MIT Master’s student and one MIT PhD student pursuing scholarship relating to logistics, freight transportation, supply chain management, or a related topic. A continuation of a program started in 1983, the UPS Fellowship aims to recognize and reward excellence in these fields, and selections are awarded solely on the basis of merit.
This year’s fellowship recipients are:
Erin Bahm
Erin Bahm is an incoming student in the MIT Supply Chain Management master’s program who comes to CTL as a Senior Inventory Operations Analyst for Target in Minneapolis, Minnesota, where she stepped into a role managing the end-to-end purchasing and positioning of multiple perishable food categories. Her strength in process improvement led to a promotion in Inventory Operations, where she was responsible for leading a cross-functional initiative to implement ordering optimization changes to over 300 vendors. In her role she consulted with global supply chain partners on new process initiatives to ensure order volume accuracy and replenishment agility across networks.
As a member of Michigan State University's undergraduate Applied Engineering Sciences class of 2020, Erin was also the recipient of an MIT Supply Chain Excellence Award. Since graduating, she has continued her studies with the completion of a Women's Leadership course through the Yale School of Management's Executive Education program, and she has earned a verified certificate in Supply Chain Analytics through MITx MicroMasters®. As a leader, Erin has moderated a career development panel series, and has expanded Target's new hire mentorship program.
Steven Parks
Steven Parks is a PhD candidate in transportation engineering at MIT, where he led a 16-month research project with Amazon World-Wide Real Estate Operations as a Research Assistant in the MIT Megacity Logistics Lab at MIT CTL, working to quantify the net traffic congestion effects of last-mile E-commerce activities at city scale. The project, for which Steven built a macroscopic traffic simulation model to estimate congestion caused by E-commerce for three major U.S. cities, led to recommendations to reduce congestion footprints were published through a whitepaper in 2024. "Steven's work was of critical importance for the success of the project and the reach and academic impact of the research challenge for us and our counterparts at Amazon," said Matthias Winkenbach, Steven's advisor and Director of the MIT Megacity Logistics Lab. "Steven’s research is answering the question how we can best plan recurring vehicle routes for given demand patterns, road network properties, and other environmental or operational factors related to urban form. This is a highly relevant and timely question with many real-world implications for both freight logistics and passenger transportation systems."
Steven is a graduate of Santa Clara University, where he was recognized as a Santa Clara University Johnson Scholar and earned his B.S. in Mechanical Engineering, and received his M.S. in Transportation Engineering at University of California, Berkeley. He has been awarded the Dwight D. Eisenhower Transportation Fellowship, the Professor Joseph M. Sussman Best Paper Prize, and first place in the Santa Clara University Mechanical Engineering Senior Design Conference for his work on disaster relief communications.
“The UPS Fellowships exemplify MIT CTL’s dedication to infusing innovation into real-world applications, upholding the highest standards of academic inquiry,” said Chris Caplice, Executive Director of MIT CTL. “These fellowships, with the generous backing of the UPS Foundation, stand as indispensable assets in nurturing talents such as Erin and Steven. Their contributions will help to shape the future landscape of the supply chain industry.”
By Elenna Dugundji, Andres Ayala, Ria Verma, and Thomas Koch
Of the numerous AI applications in supply chain management, supplier selection, risk resilience, and contract negotiation are often cited as offering the most potential for generative AI. The need for high volumes of text and data makes these areas particularly suitable for such applications. However, many organizations struggle to overcome the complexities of integrating generative AI into mainstream operations, one reason that projects often fail.
A pilot project at the MIT Center for Transportation & Logistics (MIT CTL) aims to overcome these complexities and demonstrate how targeted, real-world applications of generative AI can be successfully implemented in the procurement function. The project team is developing a chatbot for a leading pharmaceutical company with a direct and indirect annual spend of over $35 billion. The bot will help category managers negotiate more effectively with suppliers by providing comprehensive information on key questions like how prices are trending for specific materials.
Addressing data issues
Category managers rely on a multitude of data sources, including spend analyses, bills of materials (BOM), and other industry-specific information, to devise effective negotiation strategies. However, existing sources of this data are often underutilized because they are not easy to access or are relatively limited. For example, ERP systems represent a key source, but the number of systems and their complexity can be problematic for users. Also, current methods for data retrieval, such as visualization dashboards, often require extensive navigation and a steep learning curve for newcomers. Category managers can turn to data scientists for ad-hoc analyses. Still, these specialized staff may be in short supply, and relying on them too much creates bottlenecks that impede the information-gathering process.
With these difficulties in mind, the pilot’s goal is to augment (increase efficiency) and automate (reduce time) the collection and collation of information that category managers need when negotiating with suppliers. While the necessity of data scientists would not be reduced, generative AI could “place a data scientist in the pockets” of every procurement professional in this application area. As a result, the technology would increase managers’ efficiency, democratize data access, and foster data-driven decisions in supplier negotiations. These benefits could be realized regardless of the category managers’ technical prowess or experience level, allowing procurement professionals to focus more on strategic aspects of their jobs.
Choosing the best approach
A defining feature of generative AI is its ability to provide coherent and contextually relevant responses and flexible phrasing options that allow questions to be framed differently. For example, the technology can translate free-form text into SQL or another data-querying language to extract information, thus extending the scope of inquiries beyond pre-defined questions. This capability differentiates the technology from traditional chatbots based on fixed question-and-answer pairs. Furthermore, generative AI can empower users to perform complex tasks like generating graphs and conducting statistical analyses without requiring a coding background. When integrated with more sophisticated models, these tools can even undertake advanced tasks such as predictive and prescriptive analytics, showcasing their versatility and depth in creating new insights from the available data.
Given these capabilities, it is vitally important that the new chatbot is designed to respond to the types of questions the pharmaceutical company’s category managers typically ask in preparation for negotiations with suppliers.
The insights the company’s procurers look for vary in complexity. We initially categorized these questions into separate use cases and organized them into three buckets. This categorization is crucial as each question type can benefit from different models (see Figure 1).
A retrieval-augmented-generation (RAG) approach was employed with the company’s actual data. The RAG approach involves retrieving data from a knowledge database relevant to a question and providing it as context to large language models (LLMs) that generate a response. The model was deployed using AzureOpenAI’s LLM within a secure environment. The RAG method can be advantageous for reducing inaccuracies or “hallucinations” primarily because it prioritizes fetching information from the existing knowledge base, ensuring that the content is anchored to retrieved, reliable texts.
An important question for the project team is whether or not it is better to use an off-the-shelf AI solution to meet these various demands rather than developing a solution in-house. A bespoke chatbot can avoid the time and cost of developing a tailored solution. However, the team opted for an in-house solution for several reasons.
Generative AI is not protected against the well-established “garbage in, garbage out” or GIGO effect, which underscores the importance of data quality and modeling. The complexity of the models and data architecture significantly impacts the resulting output. Hence, the benefit of designing a tailored model while still using a proprietary LLM is the increased flexibility this affords data scientists to swiftly iterate, test various models, and deploy solutions in secure, private environments. Another advantage of the in-house option is that it can be tailored to specific organizational needs while keeping costs and the need for computationally skilled persons manageable. Furthermore, given the experimental nature of this field, piloting a proof of concept first can help foster trust from relevant stakeholders and make it easier to secure investment funds.
The project’s “low-hanging fruit” was to address Type 1 questions first (see Figure 1). Preliminary iterations utilized a LangChain SQL Database agent (LangChain is a Python library that offers tailored LLM applications that can be deployed for different tasks using different agents). Much higher accuracy was achieved when using careful prompt engineering (designing the input to produce an optimal output), such as formatting the free-form text intentionally to replicate query language.
This approach poses two critical questions: how can the likelihood of user error be accounted for, and how can category managers be protected from potentially inaccurate information when questions are posed ambiguously?’
Figure 1: Category manager questions
One strategy is to encourage category managers to learn and embrace pseudocode—a mix of plain language and coding syntax that explains how a program should work—without using actual programming language. Even without a technical background, using words like filter and aggregate or breaking down complex queries into smaller sentences first can greatly increase the tool’s accuracy.
Another approach focuses on refining the user interface. Implementing a dropdown menu can guide users away from entering free-form text where this type of input is not ideal for the model. A help area that clarifies users’ objectives before inputting free-form text can be included. In addition, an instruction-tuning facility at the back end of the application can guide the agent in answering a specific category of questions in a purposely directed way.
Next steps
Following a review of the preliminary chatbot version by the sponsor company’s CPO, the plan is to refine the model and develop a comprehensive roadmap to scale it further. A particularly promising avenue involves using a graph database or knowledge graph instead of a relational database to establish connections between BOMs and spend data, unlocking more profound insights geared toward addressing more complex questions. This refinement represents a significant opportunity to enhance the procurement organization’s analytical capabilities.
We also intend to research and develop a roadmap to facilitate the full-scale deployment of a fully accessible chatbot. This will involve outlining the roles and responsibilities of the functional groups involved in this process.
Significant challenges must be overcome before the chatbot becomes integral to the company’s procurement operations. These include data quality and access issues, API permissions for security, difficulties of latency and accuracy when using relational databases for the LLM, and the reliability of deployed applications.
However, the chatbot has the potential to deliver substantial benefits. Also, the project could set the stage for steady, transformative progress in advanced AI and establish a new benchmark for efficiency and innovation in procurement.
The truckload (TL) transportation market in the United States is large, fragmented, and highly competitive. Shippers manage their carriers using a routing guide within their Transportation Management System (TMS). The routing guide is the bridge between a shipper’s strategic procurement (usually through a reverse auction or Request for Proposal process) and their tactical execution. It specifies which carrier is the primary for each lane.
Our research examined how routing guides perform across different types of lanes. Specifically, we assessed the “macro-market” and “micro-shipper” effects. The macro-market perspective looks at each lane nationally, considering shipping data across 3 million lanes from TMC, a division of global logistics company C.H. Robinson, and the company’s Procure IQ tool, while the micro-shipper perspective considers only an individual shipper’s volume. For each perspective, a lane can be classified as being in one of four quadrants: Balanced (where there is high volume in both directions), Headhaul (where there is high volume from origin to destination but low volume in the other direction), Backhaul (where there is low volume from origin to destination but high volume in the other direction), and, finally, Sparse (where there is low volume in both directions). The only differences between the macro and micro perspectives are the specific target number for high versus low volume levels and what truckload volumes to consider (all shippers versus just one shipper).
Figure 1: Shipping lane quadrants
Before exploring routing guide performance, however, we needed to establish whether the macro-market classifications were stable over time. Our analysis showed that these lane classifications were very stable at the macro level. We found that 78% of the lanes, defined as a Key Market Area (KMA) to KMA pair, did not change quadrants over the eight years (2015–2022) of market data.
Then, we assessed the routing guide performance for lanes based on the primary carrier acceptance rate. We found no significant variance in performance across the four macro-market categories. However, we discovered a significant difference when examining lanes at the micro-shipper level. Balanced and Headhaul lanes exhibited at least an 8% higher primary carrier acceptance rate than Backhaul and Sparse lanes. This suggests that the shipper’s freight flows influence carrier behavior more than the broader macro-market flows.
This result suggested an opportunity to leverage the macro-market level to improve the routing guide performance for those lanes that are low volume at the micro-shipper level but high volume at the macro-market level. While these lanes only handle about 9% of the volume, they represent over half (about 53%) of the lanes a shipper manages.
Based on our findings, we developed a strategic procurement framework that classifies lanes into four potential relationships that should be part of every shipper’s procurement portfolio.
1. Lanes that are Balanced at the micro-shipper level are characterized as having high and predictable volumes in both directions and should be considered for private/dedicated fleet or contracted capacity.
2. Lanes classified as Headhaul at the micro-shipper level are primary candidates for traditional one-way over-the-road contracts.
3. Lanes that have low volume at the micro-shipper level (Backhaul or Sparse) but are high volume (Balanced and Headhaul) at the macro-market level are candidates for a structured spot pricing strategy where the shipper and carrier agree to set the price per load dynamically based on a mutually agreed upon 3rd party index pricing.
4. Shippers should consider following a traditional market spot approach instead of investing resources in establishing contracts for the remaining lanes.
By applying this strategic procurement framework that considers both a lane’s macro-market and micro-shipper characteristics, shippers can leverage a wider portfolio of relationships that improves their overall routing guide performance while reducing the required effort. Every year, approximately 80 students in the MIT Center for Transportation & Logistics’ (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects. These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.
Cambridge, MA – MIT’s Supply Chain Management master’s program has been ranked the #1 SCM master’s in the world by Quacquarelli Symonds (QS) for the fourth consecutive year. For its 2024 rankings, QS evaluated the SCM master’s programs on the basis of employability, alumni outcomes, value for money, thought leadership, as well as reputation among industry professionals and academics. MIT SCM Master’s Program received an overall score of 100 out of 100 on the QS Value. This ranking relies on input from the QS Global Employer Survey, where thousands of employers identify their preferred schools for recruitment.
The SCM program at MIT offers a distinctive fusion of executive leadership training and an intensive core curriculum, placing a strong emphasis on the development of analytical and technical competencies.The program combines executive leadership training with an intensive, practical core curriculum focused on building analytical and technical knowledge. In just ten months of cohort-based, full-time on-campus study, students develop critical reasoningskills top employers look for. Offered through MIT’s Center for Transportation and Logistics, with cross-registration opportunities at the Sloan School of Management, MIT’s SCM program leads to an engineering degree and offers stellar post-graduate outcomes. A hybrid Blended program option gives students who’ve completed the online MITx MicroMasters in SCM the opportunity to earn their SCM masters in just one semester (five months) of full-time study on campus.
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Furthermore, it’s worth noting that the MIT School of Engineering, the home of the MIT SCM program, holds the top spot as the #1 engineering school, as reported by US News and World Reports. MIT SCM students have the opportunity to enroll in courses from various departments within the School of Engineering as part of their academic journey (source: https://www.usnews.com/best-graduate-schools/top-engineering-schools/eng-rankings).
About the MIT Supply Chain Management Master’s Program (MIT SCM)
Founded in 1998 by the MIT Center for Transportation & Logistics (MIT CTL), MIT SCM attracts a diverse group of talented and motivated students from across the globe. Students work directly with researchers and industry experts on complex and challenging problems in all aspects of supply chain management. MIT SCM students propel their classroom and laboratory learning straight into industry. They graduate from our programs as thought leaders ready to engage in an international, highly competitive marketplace.
The B2B foodservice distribution industry in the United States is projected to witness substantial growth over the next five years, but like many B2B industries, it is just starting to experiment with omnichannel fulfillment. Some companies have taken the plunge and borrowed from the B2C playbook, offering fulfillment through stores in addition to the more traditional direct delivery to their customers, while others are doubling down on delivery with new and more flexible delivery options. In either case, the challenge lies in redesigning supply chain networks to accommodate increased complexity and flexibility without radically inflating costs. Companies must consider factors such as inventory positioning, shrink, lead time, and more when determining how many products should be transported between suppliers, warehouses, and customers. Unfortunately, while B2C omnichannel network design is a popular research topic, B2B applications to date make up only 4% of reviewed studies and pose unique challenges.
Why don’t existing approaches or popular software packages work for us?
Implementing familiar software solutions to address these challenges may seem like a straightforward decision, but this comes with its fair share of challenges. Let’s explore three key issues.
Firstly, complicated and interrelated omnichannel options can stress existing software packages to the breaking point. Common network design approaches assume a hierarchical and sequential flow of goods from supplier to customer. However, true omnichannel designs, where all order channels for all items can potentially be fulfilled across all delivery options, can overwhelm the design user interface for common packages due to the combinations involved. This is especially true for companies with a large number of items, suppliers, and/or customers. More exotic, but potentially cost-saving, designs involving options like lateral transshipments between nodes exacerbate the problem further or may not be supported at all.
Secondly, inventory plays a crucial role in the overall supply chain strategy, yet it’s not always adequately addressed by existing software. Many packages handle the nonlinear nature of inventory equations by solving for inventory positioning separate from network design. However, this frequently leaves money on the table. Items with high inventory holding costs and/or variable demand are typically held at too many locations with sequential solve approaches, while items with the opposite characteristics could be further decentralized. Some companies attempt to get around this trade-off by iterating between network design and inventory modeling with the hope of converging towards a reasonable answer, but this then increases the complexity of the overall effort.
Lastly, solve time can be a limiting factor. As models grow more complex to handle all the flexibility required, the time required to solve problems increases significantly. This can make the intelligence from these efforts less useful to decision-makers—or less realistic by requiring unpalatable aggregations to model realistic item, supplier, and customer counts.
Is there a better way?
To tackle these challenges, we developed a specialized mathematical optimization proof-of-concept model tailored specifically for a large B2B food distribution company in the United States looking to expand omnichannel distribution options. Putting it to the test with real company data, this model can solve for a global optimal solution for both network design and inventory positioning simultaneously, at close to 10 times the speed of alternative benchmarks.
Our tailored and integrated approach has shown a significant 7.6% reduction in total supply chain costs, encompassing product cost, transportation cost, warehouse handling, inventory holding, and omnichannel expenses like cross-docking or direct ship. Across the items examined, suggested inventory holding costs reduced by up to 50%.
The secret sauce lies in our custom algorithm, which leverages new capabilities in commercial solvers to effectively handle quadratic equations. We reformulate the nonlinear inventory equations into a form that allows for simultaneous solutions, and then improve the solve time using outer approximation techniques. This enables us to efficiently and quickly solve complex, real-world network optimization problems without long delays or sacrifices in realism. While our initial proof-of-concept assumed the current network assets, this model could easily be expanded to include facility open/close decisions as well.
Omnichannel network design is a complex endeavor, but with the fusion of the latest solver technology, new advanced mathematical algorithms, and a solid foundation of supply chain theory, companies can squeeze new life and greater efficiency out of their existing networks. In industries like foodservice distribution, with razor-thin margins of 2% or less, this can be a critical differentiator between success and failure.
These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.
Dr. Maria Jesus Saenz has been named as one of the winners of this year’s Women in Supply Chain Award by Food Logistics, a publication focusing on the movement of product through the cold food supply chain, and Supply & Demand Chain Executive, a publication covering global supply chains. The award honors female supply chain leaders and executives whose accomplishments, mentorship, and examples set a foundation for women in all levels of a company’s supply chain network.
Dr. Saenz is actively involved in initiatives and professional organizations that encourage women to enter the field, excel in their careers, and assume leadership positions. As the Executive Director of the Supply Chain Management master’s programs at the Massachusetts Institute of Technology’s MIT Center for Transportation & Logistics (MIT CTL), she has played a pivotal role in shaping the education and development of future supply chain leaders. Her appointment as the Director of the MIT Digital Supply Chain Transformation research lab showcases her visionary thinking and expertise in leveraging digital technologies for supply chain optimization. Dr. Saenz has also spearheaded initiatives to transform supply chain education and bridge the gap between academia and industry. She has taught at the Master’s, PhD, and Executive Education levels and has introduced innovative curriculum enhancements, including the integration of emerging technologies, to equip students with the skills required to navigate the complexities of modern global supply chains.
Dr. Saenz has authored or co-authored more than 100 publications, and has had a global impact on supply chain education through her leadership of academic and business programs in her native Spain, across Europe, and worldwide through the MIT Global SCALE Network of SCM research and education centers.
“MIT CTL, our students, and our industry partners have all benefited from Maria’s experience, ideas, and innovations,” said Prof. Yossi Sheffi, MIT CTL Director. “Her thought leadership over the past two decades has helped to make MIT CTL and the entire SCALE Network among the most respected graduate programs in supply chain management anywhere in the world. We are especially proud that her contributions to the advancement of women in the supply chain management profession have been recognized with this prestigious award.”
Marina Mayer, Editor-in-Chief of Food Logistics and Supply & Demand Chain Executive, noted that this year, the publications received a record 400-plus submissions. Notably, 118 of those applications were submitted by male colleagues nominating their boss, co-worker, or associate, up from 75 last year. Also this year, 39 women self-nominated, compared to just 12 self-nominations last year. “This shows progress. This shows hope that one day, we won’t need an award like this because men and women in the supply chain will be equal,” Mayer said. “While there’s still more work to be done, what we’re doing is working. From truck drivers to CEOs, what these winners are doing matters to the future of all supply chains.”
The full list of winners can be found at https://foodl.me/fdx1zi. Recipients will be honored at this year’s Women in Supply Chain Forum, set to take place November 14–15, 2023, in Atlanta. Go to www.womeninsupplychainforum.com to register and learn more.
About Food Logistics and Supply & Demand Chain Executive
Food Logistics reaches more than 26,000 supply chain executives in the global food and beverage industries, including executives in the food sector and the logistics sector who share a mutual interest in the operations and business aspects of the global cold food supply chain. Supply & Demand Chain Executive covers the entire global supply chain, focusing on trucking, warehousing, packaging, procurement, risk management, professional development, and more. Food Logistics and Supply & Demand Chain Executive also operate SCN Summit and Women in Supply Chain Forum. Go to www.foodlogistics.com and www.sdcexec.com to learn more.
About the MIT Center for Transportation & Logistics (MIT CTL)
For half a century, the MIT Center for Transportation & Logistics (MIT CTL) has been a dynamic hub where industry leaders, faculty, and students collaborate to advance supply chain education and research, with a focus on solving real-world supply chain challenges. MIT CTL offers world-renowned master’s and doctoral programs in Supply Chain Management as well as a range of executive education programs. The center also fosters innovation through its Global Supply Chain and Logistics Excellence (SCALE) Network, a worldwide network spanning multiple centers of excellence and numerous corporate partnerships. Learn more: https://ctl.mit.edu/
Transportation planning for the furniture and home goods industry presents a complex puzzle. Online retailers, like the company we’re about to uncover, face the challenge of efficiently moving products from warehouses to customers’ doorsteps. However, traditional planning methods often fail to account for the inherent randomness and variation in real-world operations.
In our research, we explore the transformative journey of a large online retailer as it embraces the power of probability in middle-mile transportation planning. Their approach relied on point forecasts, not including the unpredictability of real-world operations. Consequently, disruptions, suboptimal outcomes, and increased costs were part of their logistics network, challenging their promise of timely deliveries.
Our focus was on developing a solution that leveraged probability distributions and stochastic inputs to create robust transportation plans. The key challenge was to incorporate these methods effectively, considering the complexity of implementation and the need for computational resources. To tackle this problem, we developed a simulation model within a scenario-based framework. This approach involved sampling uncertain inputs’ distributions and repeatedly solving the optimization problem with realized values. By doing so, we aimed to evaluate the model’s performance, identify blind spots, and assess improvements in plan optimization.
Embracing the unpredictable for optimal delivery
The company’s current system relies on point forecasts, which do not adequately consider the randomness and variation inherent in real-world operations. The research was motivated by the need to develop a solution that more accurately accounts for the probability distributions of inputs. Techniques such as stochastic and robust optimization, simulation and scenario testing, and reinforcement learning have been shown to address this issue effectively. However, implementing these methods at scale poses challenges in terms of expertise and computational resources.
To address these challenges, we focused on developing a simulation model using a scenario-based framework. The goal was to provide a practical approach to incorporating probability distributions into transportation planning. By leveraging this approach, our project aims to analyze and mitigate the impact of uncertainties in two primary sources of variation: facility operations and driving time between facilities.
The objective of our project is to improve plan performance and minimize disruptions by evaluating the simulation model’s effectiveness in comparison to the existing approach. The central metric of evaluation is the company’s ability to fulfill the customer promise, which measures its consistency in meeting delivery timelines.
Uncovering logistics uncertainty through data and simulation
Simulation results demonstrated differences between the current transportation plan outcomes and those generated by the scenario-based model. Certain lanes showed higher yard dwell times in the simulation compared to the service level agreement-based plan, while others exhibited the opposite trend. This suggests that service level agreements may be overly optimistic or conservative for specific lanes. Our research showcased the capability of stochastic optimization models to capture the inherent variability in transportation time and dwell, enabling more robust transportation plans that better reflect real-world conditions.
While our study highlighted the benefits of stochastic optimization, it also identified certain limitations. The accuracy of input data, specifically estimated distributions of transportation time and dwell, plays a crucial role in determining result quality. To address this, future research should focus on improving these estimates by incorporating real-time data and leveraging advanced analytics techniques. Moreover, the computational complexity associated with stochastic optimization models may pose challenges in their practical implementation for large-scale transportation networks. Exploring avenues to enhance the efficiency of these models would render them more accessible and feasible for widespread adoption.
Every year, approximately 80 students in the MIT Center for Transportation & Logistics’ (MIT CTL) Master of Supply Chain Management (SCM) program complete approximately 45 one-year research projects.
These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.
Cambridge, MA – The MIT Supply Chain Management (SCM) Master’s Program celebrated the graduation of the Class of 2023 on May 31st. These graduates have completed a rigorous program, gaining expertise in supply chain strategy, data analytics, risk management, and sustainability. Equipped with essential skills, they are ready to make a positive impact in the field.
The ceremony marked a significant milestone for the graduates, who now join a distinguished network of SCM alumni. The MIT SCM Master’s Program congratulates the Class of 2023 on their achievements and wishes them success in their future endeavors.
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About the MIT Supply Chain Management Master’s Program (MIT SCM)
Founded in 1998 by the MIT Center for Transportation & Logistics (MIT CTL), MIT SCM attracts a diverse group of talented and motivated students from across the globe. Students work directly with researchers and industry experts on complex and challenging problems in all aspects of supply chain management. MIT SCM students propel their classroom and laboratory learning straight into industry. They graduate from our programs as thought leaders ready to engage in an international, highly competitive marketplace.
On the tail end of a global pandemic, we have become accustomed to the importance of flexibility. Supply chain and resilience are front-page buzzwords, and just-in-time is being replaced with just-in-case. Now is the perfect time for truckload shippers to re-evaluate their procurement strategies and measure their routing guide performance.
Shippers tender loads on the spot market to compete for daily rates. They also utilize routing guides to tender loads to carriers at contracted rates. We wanted to understand whether their routing guides can insulate them from the volatility experienced in the spot market during disruptive events.
Together with C.H. Robinson and their TMC division, we reviewed routing guide carrier tendering data from 2015–2021. We explored the resilience of routing guides of 90+ shippers used for full truckload, long-haul (>250 miles), dry-van shipments. To quantify the impact of planned and unplanned disruptive events on shipper routing guide performance, we considered the routing guide depth (the number of tenders made before tender acceptance), linehaul cost per mile, primary carrier (highest-ranked carriers in the routing guide) acceptance rate, and the percentage of routing guide failures for loads on both high-volume and low-volume lanes. We assessed routing guides for statistically significant changes in performance during disruptions (and in the one-week periods before and after the events) compared to a six-month benchmark period in the same year.
Do all disruptions behave the same?
We categorized routing guide performance during disruptions by the number of years in which they were disrupted versus their magnitude of impact. Independence Day caused the most year-over-year changes in performance on both high- and low-volume lanes, but Memorial Day did not cause statistically significant changes in primary carrier acceptance in any year on any lane type. We also noted differences in performance for events that occurred on weekends versus weekdays.
Most interestingly, we found that routing guide performance changed with market conditions and that routing guide depth varied with lane consistency, frequency, and load volume. Low-volume lanes were more susceptible to routing guide performance degradation in soft markets (when carriers’ supply exceeds shippers’ demand) compared to high-volume lanes. Low-volume lanes were more likely to see routing guide failures across all disruptive events, regardless of the market type. We recommend that shippers consider dynamic procurement strategies for low-volume, infrequent lanes, because they were impacted in most annual holidays.
Comparatively, routing guide performance for high-volume lanes was more resilient. High-volume lanes were impacted during holidays that occurred in tight market years or in extreme, unplanned events like Hurricane Harvey. Overall, high-volume lanes are less impacted by scheduled events like holidays or DOT Roadcheck, an annual vehicle and driver compliance inspection event, compared with low-volume lanes.
Finally, in tight markets (when shippers’ demand exceeds carriers’ supply), we noticed that the effectiveness of routing guides garnering carrier load acceptance diminished. Shippers tendered loads beyond the fourth back-up carrier in the routing guide; however, the loads were not accepted and were moved to the spot market at higher costs after more time had passed. The “right” number of back-up carriers to include in a routing guide should be determined based on performance during tight market conditions.
Measuring success
Time is money, and routing guide depth can be used as a KPI to assess the extra energy spent by shippers to tender loads during disruptions and in tight markets. Shippers should also consider segmenting lanes by volume and cadence (frequency of weekly volume) when studying routing guide performance. Benchmarking performance each year can also help to isolate the effects of disruptions from those of changing market conditions.
The “new normal” means we must all expect the unexpected, like weather events and holiday demand spikes. Shippers that understand their routing guide performance and leverage more dynamic procurement strategies like limiting the number of back-up carriers and re-routing shipments in advance of disruptive events will be better positioned to reduce tender rejections in all market types.
These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.
Global warming is a reality. Characterized by extreme weather, economic disruptions, and diminishing natural resources, climate change prevails as a growing concern worldwide. By 2025, an estimated 1.8 billion people may suffer absolute water scarcity due to greenhouse gas emissions from industrial activities. Among these activities, operations along the supply chain produce the majority (up to 90%) of a company’s overall emissions. Moreover, transportation and logistics remain the leading contributors (15% to 20%) of these emissions. To offset this trajectory and to address mounting regulatory and societal pressures, many companies aim to proactively reduce their supply chain carbon footprints.
Carbon emissions from the corporate supply chain are known collectively as Scope 3 emissions. Although many companies demonstrate efforts to govern their direct emissions, their Scope 3 emissions remain largely unchecked. Managing Scope 3 emissions is difficult due to limited visibility into supplier operations, supplier data limitations, and uncertainties around performance and emissions trade-offs. These challenges hold particularly true in a company’s inbound transportation network due to innate complexities and a heavy reliance on logistics providers’ data. To decarbonize their inbound transportation operations, companies must answer three key questions:
How can they estimate current Scope 3 inbound transportation emissions?
What are the emissions trade-offs and impacts of their supply chain decisions?
How can they monitor supplier data and identify emissions improvements?
Breaking the ice: Steps to baseline and reduce Scope 3 emissions
Estimating Scope 3 emissions begins with a thorough understanding of the current state of operations. Activities include supplier data collection and stakeholder interviews to map the inbound transportation network and to identify emissions “hotspots,” or steps in the shipment process driving the most carbon emissions. Hotspots exist from booking to delivery and highlight relationships between shipment characteristics and emissions. Based on these findings, companies may establish their Scope 3 emissions baselines via an emissions calculation tool. The calculation tool should integrate company-relevant assumptions and adapt to various estimation methodologies depending on the data given. A robust tool fills data gaps, provides reliable emissions estimates, and offers emissions intensity insights to support supplier selection.
Following emissions calculations, companies should examine trade-offs between supply chain decisions and Scope 3 emissions. Shipment levers, such as transport mode and logistics spend, may serve as proxies for planning, sourcing, and inventory decisions. Multivariate regression, correlation, and scenario analyses reveal meaningful levers, quantify impacts, and find a balance between sustainability and commercial targets. Finally, companies should assess logistics providers’ data to illuminate improvement opportunities and inform data disclosure requirements for future tenders. An accurate baseline depends on the availability and quality of shipment-level data. A maturity assessment across these two dimensions compares limitations across providers, facilitates negotiations, and incentivizes data transparency.
Making a splash: Research findings and the path forward
Applying these techniques in a real-world setting, our study discovered that booking through delivery activities collectively drive a “fixed emissions cost” for every shipment, regardless of its characteristics. On average, large suppliers produce lower emissions for high-volume, long-haul shipments, while smaller suppliers are more environmentally friendly for frequently used routes and standardized cargo. Furthermore, mode selection, cargo weight, fuel consumption, shipment type, load type, and transit time exhibit sizeable emissions impacts, such that a 1% improvement in these levers may drive a combined 1.67% reduction in emissions. Interestingly, we uncovered a nonlinear relationship between logistics spend and emissions, offering insights for future logistics investment decisions. The importance of supplier data also cannot be overstated: We found that data limitations cause discrepancies of up to 40% in Scope 3 emissions baselines.
Overall, our research has enabled us to develop recommendations and strategies to help companies reduce their Scope 3 carbon footprints. While useful for their inbound transportation networks, companies may adopt our frameworks and analyses for outbound transportation and other areas to make meaningful progress towards supply chain decarbonization.
These students are early-career business professionals from multiple countries, with two to 10 years of experience in the industry. Most of the research projects are chosen, sponsored by, and carried out in collaboration with multinational corporations. Joint teams that include MIT SCM students and MIT CTL faculty work on real-world problems. In this series, they summarize a selection of the latest SCM research.
