Ticket Classification

Project description

Many companies have experienced the challenges of managing incoming requests, inquiries, and issues effectively within the organisations. Traditional methods of manual sorting and forwarding often lead to delays, errors, and inefficiencies, ultimately hindering productivity and customer satisfaction.

To address these challenges, the integration of automated ticketing systems has emerged as a transformative solution for businesses across various industries. Ticket automation systems
streamline the process of capturing, categorizing, and routing incoming requests, ensuring
prompt and accurate handling by the appropriate personnel. By leveraging advanced algorithms and intelligent routing mechanisms, these systems expedite response times and enhance organisational effectiveness.

Context

CTV also known as Container Trucking Venlo is a logistics company specialising in container
transport and intermodal solutions. Founded in 1989 with the primary goal of transporting
containers in the Venlo region and over the years expanded their services and became experts in offering intermodal transport from the western seaports. Intermodal transport refers to the movement of goods by using multiple different ways of transportation in the same journey. Multiple ways of transportation might include trains, trucks and ships. This way of transportation is made possible by implementing the use of standardised containers or trailers that can be easily transferred from one type of transportation to the other. CTV specifically aims to combine rail, barge and truck efficiently as well as focusing on making
the last-mile as short as possible.

Results

The result of our project is a simple web application which serves as a proof of concept that makes use of our trained AI model. The AI takes as input the title of the ticket and the type of the ticket and outputs a prediction of which team within the company has to handle the request. The accuracy we have achieved on our test set is 94% which is significantly better than what the current accuracy of the company is.