Urban logistics is one of the key elements of urban mobility planning. The use of real-time information systems in logistics operations generates an enormous amount of data, nowadays used mainly for the purpose of monitoring and control of large flows of goods. At the same time, urban planners, business stakeholders, and city administrators are in need of adaptive, data-driven decision support solutions to address today's urban logistics problems. Recently, digital twins have received a lot of attention to support advanced experimentation, simulation and decision-making for on-demand logistics operations. Questions still remain on how to realize these for urban logistics management in a mixed public-private stakeholder context. We argue that this lack of a specific framework for city logistics with a model library for data mergers, linking physical and virtual data exchange, can compromise the timely adoption of digital twin technology. We contribute to filling this gap by presenting a systematic review of the literature, proposing a conceptual framework for digital twin applications in urban logistics, and providing use case scenarios for their demonstration. Together, these should advance the technical implementation of digital twins in a sustainable city logistics context.