The increasing amount of digitalization requires companies to change the way they execute their business processes. This transition, called the Industry 4.0 revolution, impacts the (inventory) planning and forecasting domain, as high accuracy is becoming increasingly important to keep up with the competition. To obtain accurate forecasts and inventory planning, (advanced) statistical algorithms are used. However, not all information can be incorporated by such planning systems. A planner has the essential task to integrate available information with the advice generated by statistical algorithms. This is commonly performed by manually adjusting the advice provided by the planning system. The aim of the dissertation is to uncover human behavior in adjusting planning advice to be able to optimally combine the strengths of planners with the systems they use. The research question central in this dissertation is: How, when, and why do planners adjust statistical forecasts and inventory planning advice, and how can we use this knowledge to improve the overall performance of these operational processes? The dissertation provides insights for managers and planners aiming to improve the planning and forecasting process. Companies can increase revenue, and reduce the time needed to execute the forecasting task by designing the planning task such that it builds on the strengths of both planners and planning systems. Furthermore, it provides valuable insights for companies that would like to improve the circumstances under which employees can design the interaction with their planning system.