Integrated solutions for product delivery planning and scheduling in distribution centres are proposed and built on a cluster analysis and simulation optimisation methodology. A cluster analysis of product demand data of stores is used to identify typical dynamic demand patterns and product delivery tactical plans. Further, simulation optimisation techniques are applied to find optimal parameters of product transportation and vehicle delivery schedules. In the paper, a cluster analysis of the demand data by using the K-means clustering algorithm and silhouette plots mean values is performed, and an NBTree-based classification model is built. In order to define optimal parameters of vehicle schedules, a genetic algorithm is applied and interacts with a discrete-event simulation transportation model built in AnyLogic simulation environment. Integrated solutions are illustrated and adjusted to a specific business case.