Digital twins are becoming ever more important in smart specialisation of factories of the future. Transition from using current state in industry to using digital twins is a big step. We propose an initial step to upgrade simulations to digital twins to enhance the productivity even further. The multi-objective optimisation approach is important in achieving high efficiency of production scheduling. The goal of the optimisation is to find a production schedule that satisfies different, contradictory production constraints. We take a simulation tool that was used by a memetic version of the Indicator-Based Evolutionary Algorithm with customized reproduction operators and local search procedures to find a set of feasible, non-dominated solutions and analyse the required steps to achieve a digital twin. We show that with a multi-objective approach that is able to find high-quality solutions and flexibility of many ``equal" solutions, the digital twin becomes a powerful tool for a decision maker.