The last-mile delivery market is highly competitive and is saturated with numerous small operators. In this context, the fierce competition between operators, joint with the rapid increase in the demand for home-delivery, resulted in a significant increase in urban freight traffic further worsening congestion and pollution. To tackle these issues, previous research has studied the implementation of collaborative last-mile operations, with organisations sharing resources in the form of inventory space or transportation capacity. However, a common limitation of the proposed models is ignoring time windows and the effects of externalities such as network congestion. In this work, we propose a framework to quantify the efficiency loss in urban last-mile delivery system by comparing the solutions of a fully-decentralised and fully-centralised last-mile delivery problem. In doing so, we develop a Multi-depot Vehicle Routing Problem with Time Windows and Congestible Network that is solved using a bespoke Parallel Hybrid Genetic Algorithm that accounts for the non-linearities arising from modelling endogenous network congestion. The model is evaluated on a case study based on central London to assess the efficiency gaps of realistic last-mile delivery operations. When time window constraints are not included, our results show that the efficiency loss fluctuates the most with a small number of customers, while it stabilises to less than 15% for instances with over 100 customers. However, time windows could significantly exacerbate this issue, resulting in an additional 25% of efficiency loss.