An American multinational delivery services company that offers a portfolio of transportation, e-commerce, and business services to customers worldwide was facing significant issues like shipment delays as a result of package content misclassification, and an increase in release costs and penalties due to incorrect documentation. Erroneous Harmonized Commodity Description and Coding System (HS) mappings were delaying freight delivery and consequently resulting in customer dissatisfaction. Thus, the customer aspired to implement an AI-based solution that would proactively predict the caging probability and classification of shipments, in order to reduce delays and improve customer experience.

HCL came on board to help the client reduce complexities in their shipment process through a strategic transformation exercise. Its machine learning models reduced the manual efforts to classify shipments that were not being mapped correctly by rule-based systems, thus simplifying the entire shipping process.

Read the case study to learn more.

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An American multinational delivery services company that offers a portfolio of transportation, e-commerce, and business services to customers worldwide was facing significant issues like shipment delays as a result of package content misclassification, and an increase in release costs and penalties due to incorrect documentation. Erroneous Harmonized Commodity Description and Coding System (HS) mappings were delaying freight delivery and consequently resulting in customer dissatisfaction. Thus, the customer aspired to implement an AI-based solution that would proactively predict the caging probability and classification of shipments, in order to reduce delays and improve customer experience.

HCL came on board to help the client reduce complexities in their shipment process through a strategic transformation exercise. Its machine learning models reduced the manual efforts to classify shipments that were not being mapped correctly by rule-based systems, thus simplifying the entire shipping process.

Read the case study to learn more.