Process mining uses data logged during the execution of processes to understand, analyse, and improve processes. Logistics process management and optimisation are highly relevant for the industry, as they are crucial to the business’ operations but not intrinsically value-adding. Despite the advantages of applying process mining to logistics processes, its full potential can not yet be leveraged. Current process mining techniques assume that an event’s execution solely depends on its associated identifiable objects, their attributes, relationships, and previously executed events. However, in logistics processes, counts of items, which may not be uniquely identifiable, play a crucial role. For instance, a replenishment order is triggered when the stock level falls below a threshold, or a second shipment is dispatched if not all ordered items are available during the first shipment. This work proposes a framework that integrates the concept of a quantity state based on properties derived from common logistics processes. We introduce extensions to object-centric event logs and object-centric Petri nets that include such counts of items. We show the feasibility of detecting the quantity state from the proposed event log class and demonstrate its capability to convey quantity dependencies using a Python-based implementation.