Abstract

Procurement requirements and procurement analysis are approaches used by the Swedish

National Agency for Public Procurement to ensure and maintain a sustainable societal

development. The aim is to safeguard tax funds and ensure that they are used for their intended

reason. In addition, the objective is to promote healthy competition between actors. One way to

impose this is through supply chain management and spend analysis, which can help companies

improve spend efficiency by gaining more insight into their supply chain. This thesis aims to

explore the necessary prerequisites for Business Vision Consulting AB to develop and train a

machine learning model used for classifying invoices to improve and facilitate spend analysis.

By applying several preprocessing methods, two natural language processing algorithms and

training predictive models using four different machine learning algorithms, this thesis proposes

solutions to classify invoice lines to their corresponding UNSPSC-codes. The four chosen

machine learning algorithms are: logistic regression, boosted decision trees, decision forest, and

neural network. Out of the four proposed algorithms, logistic regression with n-gram features

method to transform words to numbers, proved to be the most effective with classifying invoice

lines. In the three highest levels, segment, family, and class, in the hierarchical structure of

UNSPSC, a logistic regression model with n-gram features managed to produce an overall

accuracy of 96.3%, 95.5%, and 99.0% respectively. While these accuracies are adequate, the

end of the thesis proposes areas to delve deeper into for further improvements.