Sales Planning Using Data Farming in Trading Networks

Volatile customer demand poses a significant challenge for the logistics networks of trading companies. To mitigate the uncertainty in future customer demand, many products are produced to stock with the goal to be able to meet the customers' expectations. To adequately manage their product inventory, demand forecasting is a major concern in the companies' sales planning. A promising approach besides using observational data as an input for the forecasting methods is simulation-based data generation, called data farming. In this paper, purposeful data generation and large-scale experiments are applied to generate input data for predicting customer demand in sales planning of a trading company. An approach is presented for using data farming in combination with established forecasting methods such as random forests. The application is discussed on a real-world use case, highlighting benefits of the chosen approach, and providing useful and value-adding insights to motivate further research.