Supply Chain Risk Prediction and Early Warning System Integrating Large Language Models and Generative AI

Project Overview

As supply chains become increasingly complex and globalized, the types and severity of risks in supply chain management have escalated significantly. Unforeseen disruptions not only profoundly affect business operations but also trigger cascading reactions throughout the supply chain network. Thus, efficiently identifying and predicting these potential risks has become a critical issue in supply chain management. This project aims to develop a supply chain risk prediction and early warning system by leveraging Large Language Models (LLMs) and Generative AI technologies to enhance enterprises’ responsiveness to supply chain risks. The study focuses on the field of Operations Management, exploring how advanced intelligent technologies can be utilized to build a framework for predicting supply chain risks.

Research Objectives

• Risk Identification: Utilize LLMs to extract potential risk factors related to the supply chain from unstructured data, enhancing the accuracy and efficiency of risk information capture.
• Risk Prediction: Integrate LLMs with time series forecasting models to construct a multimodal prediction system, simulating and predicting supply chain disruptions and their cascading effects.
• Risk Early Warning: Develop a real-time monitoring and early warning system to help supply chain managers identify potential risks in advance and implement preventive measures.
• Decision Support: Based on the risk prediction results, provide optimized supply chain management recommendations to assist decision-makers in managing operational uncertainties.

Research Methodology

1. Data Collection and Preprocessing

• Collect unstructured data from various sources, including:
  • Global news reports and media data
  • Social media dynamics
  • Industry reports and corporate financial data
  • Policy and regulatory changes
• Apply Natural Language Processing (NLP) techniques to clean and preprocess the data, ensuring that the input data meets the quality requirements for LLMs and Generative AI models.

2. Application of Large Language Models

• Fine-tune existing pre-trained language models (e.g., GPT-4 LLAMA 3.0) to recognize specialized terminology and risk-related elements specific to the field of supply chain management.
• Leverage LLMs to extract critical supply chain risk information from vast amounts of unstructured data, analyzing trends, anomalies, and potential risk factors.
• Develop a risk tagging system to classify and identify different types of risk events, such as supplier disruptions, transportation delays, and policy changes.

3. Generative AI for Risk Prediction

• Use Generative AI (e.g., Generative Adversarial Networks, GANs) to simulate potential supply chain disruption scenarios and assess their potential impact on the supply chain network.
• Design a multi-tiered supply chain risk propagation model to evaluate the interdependencies between nodes (suppliers, manufacturers, distributors) and the effects of risk propagation across the supply chain.
• Develop a risk propagation prediction algorithm to forecast the supply chain’s response and recovery capabilities following a risk event.

4. Design of Real-Time Early Warning System

• Build a real-time monitoring and early warning system based on LLMs and Generative AI, automatically identifying potential risks in the supply chain and issuing early warnings.
• Use visualization tools (e.g., risk heat maps and trend graphs) to display the risk exposure levels of various nodes within the supply chain network.
• Key functionalities include automated decision support, generating response strategies based on real-time risk data to assist supply chain managers in decision-making.

5. Decision Support for Risk Management

• Design a module for generating response strategy recommendations integrated with supply chain management, optimizing supply chain design by simulating various scenarios.
• The system generates diverse risk mitigation measures, such as alternative supplier selection, inventory strategy adjustments, and logistics route optimization, to enhance supply chain resilience.
• Provide a decision feedback mechanism that continually refines the model’s predictive accuracy and the effectiveness of strategy recommendations based on actual user decisions and external outcomes.

Research Innovations

1. Integration and Analysis of Multi-Source Data: By combining traditional supply chain management data with external real-time data (e.g., news and social media), the project significantly enhances the identification of potential supply chain risks.

2. Application of Generative AI: The innovative application of Generative AI in simulating and predicting supply chain risk scenarios greatly improves the precision and scope of disruption forecasting.

3. Dynamic Risk Assessment Models: The dynamic risk assessment models built using LLMs and Generative AI can automatically adjust predictions based on changes in the external environment, thus enhancing the flexibility and foresight of supply chain management.

Current Research Progress

1. Development of Data Collection Tools: Data scraping tools have been developed based on platforms like Yahoo Finance and Google News, using R and Python to implement real-time news scraping with a graphical user interface (GUI). The tools support multiple output formats (csv, xlsx, txt, json), effectively capturing unstructured data.

2. LLMs Deployment: A local deployment and fine-tuning of a 7B LLM has been completed via Ollama (utilizing the open-source model LLaMA 3.0 and fine-tuned with an RTX 4090 GPU). Additionally, a localized knowledge base has been constructed. By combining fine-tuning with Retrieval-Augmented Generation (RAG) techniques, the model achieves high accuracy outputs at low cost.

3. Development of Supply Chain Network Models: A multi-node system dynamics evolutionary R package has been designed based on complex network game theory, simulating dynamic games between nodes and generating decision matrices and payoff matrices.

   More information can be found in Open access

Next Steps

Note: For empirical research, the plan is to construct new moderating variables using LLMs under the event study and cross-sectional regression frameworks. LLMs will be employed to perform sentiment analysis on social media posts or to rate the risk levels of relevant companies, thereby improving the precision of risk control.

Research Contributions

This project seeks to provide an innovative framework for supply chain risk management within the field of Operations Management. By integrating LLMs with Generative AI, it offers theoretical support for the academic exploration of intelligent technologies in complex systems. Additionally, the system’s practicality and real-time capabilities equip enterprises with effective risk management tools, helping to enhance the resilience and robustness of supply chains.