This project will study data-driven decision making under local, loosely-connected digital twin services considering uncertainty and sampled behaviour of systems. The reference architecture for industry4.0 (RAMI4.0) shows a structured approach to connect services in a service-oriented architecture. It breaks up boundaries between production and product, across organizational hierarchy levels and business layers and across departments and companies over the product life-cycle and value stream. This service-orientation reaches up to the consumer and includes aspects of transport and logistics. By nature, it is affected by regional or city-level decisions in urban mobility. Even if services are technically data-connected, there are hidden challenges on how to perform connected decision making. In literature on Digital Twins, the micro and macro level are often merged into one large master model but in many cases, this is not tractable or feasible due to organisational boundaries or the different levels of detail depending on the decision-making focus. The challenge lies in integrating independent or loosely-connected decision processes towards better holistic decision making.