Context
Introduction
The global nature of Navisphere Logistics, Ltd.'s operations means that the company must navigate a complex web of international tariffs and customs regulations. Efficiently managing these tariffs is critical to minimizing delivery times and costs. Dr. Hicks and her team are tasked with refining the company’s AI systems to accurately predict tariff costs across different countries and product categories. Dr. Maya Hicks, Lead Data Scientist with a specialization in artificial intelligence and machine learning and a keen interest in optimizing supply chain efficiency through innovative technologies. Her responsibilities are to Lead the AI research and development team in refining and enhancing the company's AI-driven tariff prediction models. Evaluate datasets for integrity and compliance with corporate policies and standards which reflect international regulations and privacy considerations. Collaborate with procurement and legal colleagues to ensure that the data and AI models are in line with global standards and regulations. Train and optimize AI models to accurately predict tariffs, involving sophisticated algorithms and machine learning techniques. Integrate the refined AI models into Navisphere Logistics' operational systems and conduct extensive testing to ensure accuracy and efficiency. Establish and maintain a feedback loop for continuous monitoring and improvement of the AI models based on real-world application insights. Ensure the responsible use of AI in accordance with Navisphere Logistics’ standards and privacy laws, particularly in the handling of sensitive data. Communicate the progress and outcomes of the AI enhancements to stakeholders, including technical teams, management, and commercial clients. Stay updated with the latest developments in AI, machine learning, and international logistics practices to continually drive innovation within the company.
Role
Business Objectives
Harmonize global tariff schedules into a unified, AI-friendly format to enhance prediction accuracy. Refine and improve the AI-driven tariff prediction models to minimize cross-border delivery times and costs. Ensure that all collected tariff data meets stringent data transparency, accuracy, lineage, and AI data usage requirements for integrity and compliance with international regulations. Achieve high accuracy in tariff predictions across different countries and product categories through sophisticated AI algorithms. Streamline customs clearance processes through more precise tariff assessments, benefiting the company’s worldwide commercial clientele.
Products
Through application of the data provenance standards metadata for its global tariff schedule datasets Navisphere Logistics, Ltd. has achieved a significant enhancement in the operational efficiency and accuracy of its AI-driven tariff prediction models. The outcome includes: Improved data consistency and compatibility: By specifying the version used for the metadata, Navisphere ensured that all datasets adhered to a uniform standard, facilitating seamless integration and interpretation by the AI models, regardless of the data's origin or when it was collected. Enhanced data identification and access: The establishment of a unique metadata identifier and a metadata unique URL for each dataset enabled easy identification, access, and reference, streamlining the data ingestion process for the AI systems, and reducing the time spent on data preprocessing. Streamlined data lineage and dependency tracking: The metadata location for datasets feeding the current dataset allowed Navisphere to efficiently manage data dependencies and lineage, ensuring that updates or corrections in source datasets could be rapidly propagated through the system, maintaining the accuracy and timeliness of tariff predictions. Increased accountability and data integrity: Detailed metadata entries for the creator, source, and data origin geography provided clear accountability and context for the data, enhancing trust in the data's reliability and compliance with regional laws and international regulations. Better data privacy and security measures: The application of privacy enhancing technologies (PETs) and the careful classification of data confidentiality ensured that personally identifiable information (PII) and sensitive personal information (SPI) were adequately protected, aligning with global privacy standards and ethical considerations in AI application. Legal compliance: Detailed metadata on data processing and storage geographies, consent locations, and the license to use the data ensured that all AI operations remained within legal boundaries, respecting data sovereignty laws and consent.
Codebook
Dataset
License
Available Formats
Data Provenance
