Development of public dynamic spatio-temporal monitoring and analysis tool of supply chain vulnerability, resilience, and sustainability

Supply chains play a pivotal role in driving economic growth and societal well-being, facilitating the efficient movement of goods from producers to consumers. However, the increasing frequency of disruptions caused by geopolitical events, pandemics, natural disasters, and shifts in commerce poses significant challenges to supply chain resilience. This draft update report discusses the development of a dynamic spatio-temporal monitoring and analysis tool to assess supply chain vulnerability, resilience, and sustainability. Leveraging news data, macroeconomic metrics, inbound cargo data (for sectors in California), and operational conditions of California’s highways, the tool employs Natural Language Processing (NLP) and empirical regression analyses to identify emerging trends and extract valuable information about disruptions to inform decision-making. Key features of the tool include sentiment analysis of news articles, topic classification, visualization of geographic locations, and tracking of macroeconomic indicators. By integrating diverse and dynamic data sources (e.g., news articles) and using empirical and analytical techniques, the tool offers a comprehensive framework to enhance our understanding of supply chain vulnerabilities and resilience, ultimately contributing to more effective strategies for decision-making in supply chain management. The dynamic nature of this tool enables continuous monitoring and adaptation to evolving conditions, thereby enhancing the analysis of resilience and sustainability in global supply chains.

Methods
The research team implemented a two-stage procedure to streamline the collection, processing, and analysis of news data. The stages are as follows:

Lexicon Setup: This stage establishes the lexicons required for sentiment and topic analysis. Topics are categorized into eight groups relevant to supply chain risks: political, environmental, financial, supply and demand, logistics, system, infrastructure, and sector. Sentiments are evaluated using a dictionary-based approach with the AFINN lexicon.
Three comprehensive lists of countries, states, and cities are used to classify geographical locations at three hierarchical levels: countries, states within the U.S., and cities/municipalities within California.

News collection and processing: Automated algorithms collect the most recent news daily, with a 24-hour lag, based on the predefined query: (USA or United States) and (supply chain or supply-chain) and (disruption or resilience) and (retailer or warehouse or transportation or factory). Text mining tasks are performed to extract key performance metrics, including n-grams, topics, sentiments, and geographical locations. The process involves several steps:

Corpus setup,
Term Frequency-Inverse Document Frequency (TF-IDF) for measuring word relevance in documents,
Entity recognition and consolidation,
Conversion of the corpus into a Document-Feature Matrix (DFM),
Dictionary-based extraction of sentiments, topics, and geographical locations.