Most success stories of digital twins are realized within the scope of a product/machine where the applications are often sufficiently manageable and scalable (like for example predictive maintenance applications). Nevertheless is the potential added value within the broader scope of production and logistics environments (DT4PL) quite large and the potential applications endless because of the need for decision support which is inherent to an environment with such complex interactions between a multitude of entities such as machines, products, transport carriers, people and control software. The high complexity brings also a lot of challenges to allow cost efficient creation and maintenance of digital twins. This project will research how that workflow can be improved by an optimal reuse of existing sources of information. The improved method will be validated on a number of industrial cases.