Contemporary logistics companies operate within an increasingly complex and dynamic environment characterized by growing parcel volumes, fluctuating business demands, and evolving customer expectations for faster and more reliable delivery. Sorting terminals, as critical nodes within logistics networks, face significant operational challenges due to the intricate interdependencies between design-time capabilities, operational configurations, and key performance indicators (KPIs) such as sorting time, chute blockages, and resource utilization. These complexities are further amplified by uncertainties in parcel inflow patterns and the non-linear interactions among system components leading to potential inefficiencies that can cascade across the network. This chapter explores the critical role of sorting terminals in modern logistics operations and examines the challenges they face in maintaining operational efficiency. It advocates a digital twin-based approach as a robust decision-making aid to address these challenges. By providing an accurate, agent-based representation of sorting terminal components and their behaviors and interactions, a sorting terminal digital twin enables precise evaluation of operational interventions. Its in silico simulation capabilities offer a means to continuously align operational strategies with business goals, adapt to growing demands, and meet changing customer preferences. Through a detailed discussion of the complexities inherent in sorting terminal operations and effective utilization of digital twin technology, this chapter highlights the potential of digital twin for evidence-based decision-making to drive resilience, adaptability, and efficiency in the logistics sector.