Digital twins and shadows have gained increasing popularity in industry and research. The terms describe simulation systems that mirror real-world systems, such as service or manufacturing lines, aligned to (near) perfection based on automated data streams. We implement a set of perfect model experiments to demonstrate how deviations between a digital shadow and the real world can arise, affect predictive accuracy, and may be eliminated. As an illustrative example, we simulate a simple sequential production line and its digital shadow. The paper concludes with a summary of identified research opportunities.