Forecast inbound volumes and workforce needs in a logistics warehouse

This thesis explores the forecasting of incoming volumes and workforce requirements in a logistics warehouse. The main objective of the study is to develop a reliable forecasting model based on statistical data to improve resource management and the organisation of goods receiving operations. Through an analysis of historical data, the research aims to identify and quantify patterns, trends and seasonal variations that influence incoming volumes. The thesis consists of two levels: a theoretical discussion of forecasting methodologies and an empirical case study applying these concepts within a distribution centre. Methodological approaches for data collection and analysis are also examined, as well as the development of forecasting algorithms customised to meet the specific operational needs of the logistics centre. The development of the case study will facilitate an examination of the efficacy of forecasting methods that have been specifically designed for a logistics warehouse. By analysing monthly, weekly and daily data, the research will demonstrate how the forecasting model can reduce the costs associated with over- and understaffing, thereby reducing the overall costs of the warehouse.