Application of Generative AI to Derive Insight from Supply Chain & Logistics Contracts

Abstract Contract management is a critical process for energy companies operating across upstream, midstream, and downstream sectors. These companies deal with numerous complex contracts containing intricate legal language, cross-references, and long document lineages spanning amendments and supplemental materials. Manually extracting insights and managing obligations from these highly unstructured contracts is extremely time-consuming and error-prone. This paper presents a novel framework leveraging machine learning and generative AI (GenAI) to automate and streamline contract management. The proposed solution utilizes large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and chain-of-thought reasoning to extract structured data (such as fees, escalations etc.) from contracts, perform analyses, and generate natural language responses. It enables use cases like optionality analysis, fee calculation, cross-contract insight generation, invoice processing, and user feedback integration. The architecture combines LLMs with contract knowledge bases and external data sources to optimize operations, mitigate risks, and enhance decision-making. Key capabilities include centralized repositories, clause extraction, compliance tracking, workflow automation, and advanced analytics leveraging LLM question-answering, summarization, and code generation abilities. The paper discusses technical details, use case scenarios applicable to crude oil transportation contracts in supply & chain, and highlights GenAI's potential to transform energy industry contract management practices. When conducting analyses involving hundreds of contracts, the proposed solution exhibited significant efficiency gains. Case studies showed that it reduced processing time by 10 to 100 times compared to manual methods.