Predictive Analytics in Corporate Financial Management: A Case Study on Earnings Forecasting with a Global Logistics Service Provider

Predictive Analytics is a tool in corporate financial management to gain a better understanding of the future based on forecasting models helping companies to prepare for future uncertainties and crises based on past data. This chapter describes a case study on an earnings before interest and taxes (EBIT) forecasting model based on a bachelor thesis for a global logistics company specializing in the transportation of liquid bulk commodities. The company was faced with the challenge of predicting monthly profitability due to the lack of correlation between order volume and financial performance. The objective of the case study was to improve business processes and profitability by developing a forecasting model. The study analyzed 29 months of profit and loss accounts and used a step-wise regression analysis to develop a formula to estimate monthly EBIT. The results show the importance of understanding the mechanics of the income statement and the inefficiencies of the processes to generate accurate forecasts. The chapter recommends further research to optimize the EBIT formula and explore innovative machine learning techniques for forecasting. It emphasizes the importance of forecasting models for both prediction and explanation and highlights the need for regular model adjustments to account for changing dynamics. The case study’s findings demonstrate the value of financial forecasting for strategic planning and business management.