We provide a decision support system that (a) integrates digital twins to optimize the overall production process according multiple parameters, and (b) democratizes decision making granting human worker and machines a say during decision making. Each individual demand is consolidated to achieve a decision support, which is then optimized. We combine (a) model-based approaches to transparently design, simulate and improve decision making, (b) co-creation laboratory using models and physical experiments as communication media to all actors and, (c) reliability indication of data and AI algorithms. Those models can be assessed according legal, ethical, security and safety concerns and in case of approval ensure that only compliant models are used for decision making. Hybrid decision making is provided by combining human- and machine-centric knowledge representation into a holistic model. Simulations on time, cost, quality, safety or energy efficiency contribute to transparent decision making, which evolves via model changes. BOC provides graphical modelling enabling intuitive knowledge representations for humans. JOTNE provides data repositories for machines. JR provides data marketplaces and optimizations. MORE enable decentralized decision making with AI. RWTH provides both (a) AI and intuitive machine interaction and (b) social and ethical guidance for trustful decision making. The use cases of the car manufacturer CRF and the robot plant provider FLEX are challenged by human and machine interaction, complex environment interactions and influence factors requiring continuous re-arrangements. The challenge is to consider each actors needs in a concise and complex situation still targeting an overall optimization. The non-profit organization OMiLAB provides its world-wide network on modelling omilab.org, and interacts with DIH, PPP on AI, Data and Robotics and contribute with corresponding CSA as part of their mission.