Over time, organizations and their supply chains have adapted to constant global change by increasingly adopting digital technologies for optimization, operational efficiency, and competitive advantage. These technologies, which are part of digital transformation, automate manual and repetitive processes, optimize operations and reduce human error. In this project, a decision support model was developed using Python to optimize the current manual costing processes involving 62 Excel spreadsheets and related calculations to achieve efficiency in the transportation processes of Company X, a third party logistics provider. The model was evaluated for accuracy, monetary savings, and time efficiency compared to the manual process. The Design Science Research Methodology (DSRM) was chosen to structure this work. The project produced positive results, with three scenarios (pessimistic, realistic and optimistic) showing annual monetary savings of over €6,000 and time efficiency of over 86%. Despite minor limitations, the project successfully applied digital transformation to transport optimization and demonstrated significant time and monetary benefits to the organization.