# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 1 Introduction
In [29], the authors state that most of the research on DT for planning focuses on frameworks and concepts, and the actual implementation of online dynamic scheduling with DT support is still in its infancy--this is a key direction that requires further research. Similarly, [30] points out that the application of DT in the operational phase of production (especially for scheduling) has not yet reached its full potential. This indicates a gap between theory and practice: many works suggest potential uses of DT, but few demonstrate how to actually achieve these in a live production environment. Another gap is the limited flexibility and real-time adaptability in the implementations to date. Although algorithms for dynamic replanning exist, the integration of truly up-to-date data and an immediate response to them is often incomplete. In [31], the authors point out that, while technologies enable dynamic planning, in practice, there is a lack of flexibility in adapting production to environmental changes based on real-time data. This implementation gap means that systems may not be able to process new orders or failures quickly enough. For example, traditional ERP/MES systems often do not plan production immediately for each change, resulting in time delays and missed optimization opportunities. Modern DT solutions promise to address this, but the literature documents only modest progress in fully realizing this vision. In other words, real-time, closed-loop control between the physical shop floor and the planning algorithm in DT has so far only appeared in pilot studies. The prioritization of orders and multiple objectives represents another weakness in the current work. Many existing approaches focus on a single goal (typically minimizing the makespan or delays), with all orders treated as equal or sorted by a simple rule (e.g., earliest delivery date first). However, in a highly customized manufacturing environment, this is not sufficient--different orders may have different degrees of importance for the company (e.g., strategic customers vs. one-off orders, large orders vs. prototypes). The literature shows that the system mechanisms for the prioritization of orders are insufficient. The authors of [32] describe that, in highly variable manufacturing, foremen often determine priorities subjectively--according to experience or customer relationships--instead of using objective criteria. Such ad hoc decisions can be inconsistent and at odds with the company's strategy. There is therefore a lack of systematic, data-based prioritization models that take into account multiple factors and allow for their changing weights. Moreover, in the mass customization environment, tension arises between large orders (which create the main part of the turnover) and small orders (often prototypes or new products). Without an advanced priority model, smaller orders tend to be pushed aside in favor of larger ones, which hinders innovation and growth for smaller customers. Although the current literature addresses the technical aspects of planning, the business aspect of order prioritization remains a gap--only a few works integrate multi-criteria decision-making into planning algorithms that reflect business priorities or allow for dynamically changing preferences according to the situation. The main gap is the lack of fully implemented DT solutions--most studies are conceptual or simulated, and real deployments on production lines are missing. Furthermore, the real adaptability is limited--systems do not yet reach the promised level of immediate rescheduling based on live data. Another is subjective prioritization--there is no advanced model that can fairly and strategically take into account the differences in the importance and nature of orders. The described new approach integrates data-driven prioritization into real-time digital planning.