A Machine Learning Approach to Fiber Delivery Lead Time Estimation

Abstract: Everybody’s talking about big data. Telecom companies have access to huge quantities of data but unfortunately, most of it is not further analyzed in search of valuable information regarding the optimization of processes. Telia Infra delivery operates to supply fiber to the whole of Swe- den. An important part of planning future deliveries is to decide on a delivery date immediately after the order is placed, a so-called ”First promise”. Today, there are great opportunities for improvement in this decision making. In 2018, the business had a low delivery precision on First promise, which today is done with manual resources. Therefore, the aim of the master thesis project is to develop and apply a model that improves the delivery precision at First promise, and can be implemented in the business as support for decision making at the delivery date. A variety of machine learning algorithms were trained and tested in order to estimate fiber de- livery lead time. One of the algorithms was chosen and results have shown that a significant increase in delivery precision is possible to obtain by implementing an automated system for estimation of delivery lead times based on analyzed available data. It gives reason to believe that to take the step from basing decisions on a hunch and move to a more technology based solution will optimize processes and with a high possibility reduce costs and resource demands. The master thesis has shown that there exists great potential for other tasks within the field of machine learning and automation of today’s processes.