Synopsis
The key objective of the client to predict the “Delivery Status” of a freight starting the operational journey. Before this solution, there was no clear mechanism for agents to achieve this. There was neither a proactive communication to the customer nor a pre-emptive intervention to recover the delay, resulting in elevated operation costs and lower customer experience. The customer was looking for a solution which could address their problem statement by predicting Cargo delivery status
About the Client
An Anglo-Spanish multinational airline holding company with its registered office in Madrid, Spain, and its operational headquarters in London, UK. It was formed in January 2011 after a merger agreement between British Airways and Iberia
Business Challenge
Challenge was to predict Cargo 'Delivery Status' of freight in its entire operational journey to reduce operation cost and elevate the customer experience.
Our Solution
Our solution implements machine learning on historical freight delivery data, to predict the date and time of key operational milestones (RCS, DEP, and NFD) of the new AWB being booked. The delay is calculated as the difference between the 'planned route map' and the 'flown route map'.
Delivering More Value
Improved IATA rating for IAG Cargo performance. Optimizing operation cost (potentially savings 0.5 million GBP based on 2GBP per AWB delay). Better customer experience / Retention
Coforge Technologies Ltd Advantage
Cargo delivery was a complex process, with multiple integrations points with multiple actors involved in the process. Our project team worked in close conjunction with stakeholders, business analysts to ensure smooth and timely delivery. Our expertise in AI technologies and the Travel domain was a key differentiator.
