Publications

Abstract
This paper presents a novel methodology for automated multi-tier supply chain mapping, leveraging Retrieval-Augmented Generation (RAG) and network science techniques. We developed an RAG-based approach that extracts supplier-customer relationships from unstructured public data sources, including SEC 10-K filings and earnings calls. The extracted entities are structured into a directed supply chain graph and analysed using network science metrics such as centrality, modularity, and path length. The case study focuses on three of the largest contract manufacturers in the electronics industry: Hon Hai Precision Industry (Foxconn), Flex Ltd., and Jabil Inc. Our findings demonstrate that Generative AI (GAI), specifically LLMs enhanced with RAG, can construct scalable and comprehensive supply chain graphs. The proof of concept is successful, as evidenced by the construction of a directed supply chain graph encompassing 4,644 nodes and 8,341 edges, covering three of the largest contract manufacturers in the electronics industry.

Driving automation is advancing rapidly, yet the biggest challenges ahead have less to do with sensing the road and more to do with understanding the human behind the wheel. Human-aware automation will become one of the defining features for an elevated driving experience and safe mobility.

The MIT CTL eJournal showcases select papers from our Research Paper Series, cohering around a new theme each issue.

Cyberattacks are crippling supply chains and exposing hidden vulnerabilities in the very technologies meant to drive efficiency. As cloud platforms, robotics, and connected systems expand, companies must treat cybersecurity as a core supply chain function, building redundancy, resilience, and cross-functional defenses to withstand the next inevitable strike.

Is it possible to strike a balance in designing information that not only looks good but also is highly legible? Can we enhance a designer’s ability to empower a brand and deliver information clearly and consistently in an increasingly mobile world? The answers may lie, in part, in something that may surprise you – the typefaces that these devices use.

Investing in resilience beyond traditional risk management can deliver long-term operational, financial, and competitive advantages, turning adaptive capacity into a strategic asset for future-ready supply chains.

In today’s world of persistent supply chain disruption—including pandemics, trade wars, traditional wars, and intensifying natural disasters—resilience has become a primary topic in supply chain circles. In fact, it appears most often in session titles at the upcoming Council of Supply Chain Management Professionals (CSCMP) EDGE event, surpassing sustainability and AI.

Managers at small and midsize businesses may perceive automation as too expensive to be feasible, but research has identified strategies that make implementing it possible even on a tight budget. A framework for evaluating the five main factors that can reduce — or increase — the cost of an automation project can help leaders make more informed decisions about whether process automation might be a viable and cost-effective option for their company. These solutions enable managers to provide updated information to sales and customer service personnel and to quickly react to potential delays — all for a technology investment that is often less than $300. Yet, most SMEs do not know where to start with automation or how to do it under their budget constraints and sometimes limited bandwidth for operational innovations.

Teaming humans with robots delivers better results for warehouse operations than having only robots, but there are opportunities to improve that collaboration in the face of increasing demands for speed and accuracy in e-commerce fulfillment. Studies have shown that having a mix of humans and robots can lead to greater efficiency, flexibility, and cost-effectiveness compared with full automation alone, but companies must carefully consider the respective roles that humans and robots should play in warehouse facilities.

The future of warehouse operations hinges on a delicate balance between human capability and robot autonomy, empowered by AI. Stakeholders who understand this dynamic and take actionable steps to integrate AI-driven Human-Robot Collaboration systems will gain significant competitive advantages in terms of efficiency, adaptability, and workforce satisfaction. While the journey to advanced HRC may be gradual, the key lies in recognizing the ongoing role of AI in improving every stage of collaboration.

AI is revolutionizing supplier negotiations, driven by the need for speed, scalability, and strategic agility in complex supply chains. Initially used for automating low-value tasks, AI can now play a crucial role in a growing number of key procurement decisions. The transition to adopting AI for supplier negotiations should be step by step: from the assisted stage, where AI tools act as copilots but do not replace human decision-making; to semi-autonomous systems, where the systems can accept pre-approved clauses or adjust prices in contracts within set limits, but critical decisions remain under human control; to the fully autonomous stage, where the systems handle negotiations end to end—within guardrails.

Consumer-facing firms often seek outsourcing services from third-party providers to reduce costs and focus on their core competencies. We consider a scenario where a single service provider offers outsourcing services to two firms through different service modes: the dedicated mode and the shared mode. With the dedicated mode, the service provider delivers customized services to one firm exclusively. With the shared mode, the two firms share the service capacity of the provider.

Procurement teams often struggle to fully leverage the valuable supplier knowledge captured during RFx (Request for Information, Proposal, or Quotation) events, mainly due to the high volume of unstructured data and fragmented data storage, which limits access to past insights. This paper explores how generative AI can preserve, retrieve, and apply RFx knowledge to strengthen supplier discovery, streamline event execution, and support data-driven strategic sourcing decisions.