Analysing and Predicting Invoice Payment in Finnish Road Freight Transportation Industry
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School of Business |
Bachelor's thesis
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Date
2022
Department
Major/Subject
Mcode
Degree programme
Tieto- ja palvelujohtaminen
Language
en
Pages
41
Series
Abstract
This thesis examines the relationship between diesel prices, 12-month Euribor interest rates, and bankruptcies of road freight transportation companies and their effects on invoice payment delays in a sample of road freight transportation companies. This investigation is relevant to the road freight transportation industry, as the changes in diesel prices and interest rates can significantly impact the transportation companies’ cost structures. The sample was collected from a heavy equipment spare parts retailer and contained information on over 70 000 invoices. The work consists of a literature review and two quantitative methods, time series regression model and binary classification models. The time series regression model looks at 92 months from 2015 to 2022. The independent variables are monthly average diesel prices, 12-month Euribor interest rates and bankruptcies, and the dependent variable is the average monthly payment delay in days. Binary classification models predict whether an invoice will be paid on time or late. These models include logistic regression and k-nearest neighbors. Overall, the time series regression model is statistically significant. From the individual coefficients, diesel prices were the most significant predictor, suggesting that they impact payment delays the most. Euribor interest rates and bankruptcies were not found significant at the 0.05 level. Furthermore, the analysis revealed an unexpected relationship where a decrease in diesel prices leads to longer delays in payments. Additionally, the binary classification results show that the k-nearest neighbors’ method with a small hyperparameter k value and the logistic regression model were effective in predicting late payments.Description
Thesis advisor
Hekkala, RiittaKeywords
payment delays, time series regression, binary classification, Gauss-Markov theorem