Standardize Your Supply Chain Data to Boost Revenue with Gen AI


What’s inside?

    Plug-and-Play isn’t the Way

    Generative AI (Gen AI) is set to change the world of logistics, allowing operators to stay informed, generate analyses, and automate processes. But a robust data strategy is crucial to fully leverage this technology. 

    The effectiveness of gen AI applications hinges on the quality and structure of the underlying data. Clean, harmonized, and well-hydrated data lakes are essential. Without a solid data foundation, even the most advanced AI applications can deliver inaccurate or misleading results, undermining their potential benefits.

    Vadim Tereshchuk, Solutions Architect at AWS, recently explained why effective gen AI results for maritime and supply chain aren’t going to come from a plug-and-play approach during Windward’s webinar: “Gen AI foundation models aren’t a one-size-fits-all solution. Sometimes, just adding your data to the models gives great results. Sometimes, it’s not enough, and we need to fine-tune the models or continuously pre-train them to greatly improve our results.

    To train gen AI models, supply chain companies must standardize the multiple use cases pertinent to their organization and access hundreds of unique data sources, some of which are traditional, outdated, and difficult to comprehend. 

    Unstructured and Structured Data

    Structured data – such as vessel schedules, AIS data, and freight rates in the ocean logistics ecosystem – is organized in predefined formats and typically stored in databases. This organization facilitates systematic access, querying, and analysis, and supports the development of AI models.

    Unstructured data – including text documents and contracts, such as digital bills of lading (BoL) and port contracts between freight companies and carriers, and between freight companies and their customers – require more sophisticated handling. 

    This often involves using vector databases and embedding models to filter and extract relevant information. Managing unstructured data is a daunting task because of the convoluted processes involved. Despite the enormous potential for business growth, many companies are deterred by the time-consuming nature of these processes.

    Integrating unstructured data into vector databases using embedding models helps filter and extract valuable information from text documents. This improves the performance of gen AI models, enhancing retrieval augmented generation (RAG) systems and large language models (LLMs).

    Learn to Leverage a Unique Data Strategy For Your Business

    Ensuring smooth data standardization requires human oversight to maintain AI reliability and prevent data issues. Partnering with AI specialists like Windward is crucial for effectively managing these complexities. Join us at the Gartner Supply Chain Symposium/Xpo™ 2024 to learn more.


    Attend an engaging session on June 11 at 11:00 AM in Room 118: “The Role of Maritime AI™ in Transforming Data into Business Growth.” Anthony Plummer, CTO of Ligentia, a leading provider of global supply chain solutions, and Ami Daniel, Co-Founder and CEO of Windward, will explore the critical role of data in overcoming unique freight logistics challenges. 

    This session will highlight:

    • The clear ROI that a good data strategy can yield
    • Labor investment vs. outsourcing: pros and cons
    • What makes a technology provider’s data capabilities so valuable to large-scale logistics companies

    Also, be sure not to miss us at booth #430. 

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