# Proactive Approach to Production Control Utilizing Heterogeneous Shop-Level Production Data
## 1 Introduction  
The existing body of research has established the undisputable advantage of Digital Twins (DTWs) for facilitating organizational efficiency in manufacturing [1, 2, 3]. The necessity to advance further in terms of analytics, real-time monitoring, and optimization has been growing alongside the identified need for a more efficient approach to manufacturing in the context of simulations [4]. Simulation and modeling has become a key instrument for improving industrial processes [5, 6, 7] and has remained one of the pillars of most modern enterprises. Simulation and modeling lies at the heart of innovation and agility and consistently yields positive results for respective use cases. The most widely used approach to simulating manufacturing production lines are discrete event simulations (DES); however, depending on the system features, modelers also opt for continuous [8, 9, 10, 11] or agent-based simulation. The best solution to realistically capture the system flow is a hybrid multi-layer simulation of a given cyber-physicalproduction line. Hybrid simulations offer the most adaptive and high-fidelity simulations that unify the control flow and physical processes and can serve as a base for DTw implementation. DTw technology improves on simulation technology with its core being real-time data integration and subsequent data-driven analytics for decision support. DTws help track and manage asset data flow and enable for reconfiguration at a keystroke [12; 13; 14; 15]. DTws serve to represent a physical object or a system, virtually, with real-time data and are particularly useful for ease-of-use by utilizing simulation. Simulations enable visualization of relationships among subsystems, inner components of systems, and their ability to interact with their environments. The benefits of DTw technology depend on specific uses and implementation. It can simulate production processes, optimize urban planning, monitor operations and continuously assess system variables. Studies have proven that DTws' application in enterprise resource planning (ERP) and manufacturing execution systems (MES) aids in decision-making and reduces response latency levels [16]. The combination of DTws with simulation achieves real-time control and optimization [17] of workflows [18], enabling for a closed-loop manufacturing system if integrated with artificial intelligence (AI) or machine learning (ML) This facilitates sizeable improvements in various aspects of industry use cases, such as error mitigation with real-time predictive maintenance [19] and safety monitoring of safety-critical infrastructures [20]. The aim of this study is to apply the Industry 4.0 concepts of cyber-physical systems (CPS) with real-time monitoring, data-driven control, and adaptive planning to demonstrate innovative approaches toward DTw creation for smart manufacturing systems where data sources are not only more unified than DTw solutions have been until this day but also to do so in a manner that complies with industry standards. Analysis of common challenges and drawbacks faced in established implementations or presented case studies can be summarized as follows [21; 22; 23; 24]:  
* Limited scope of data types--Research focuses on specific data types and does not fully address the complexities of handling heterogeneous data. * Focus on specific vendors--While the OPC UA architecture is aimed at vendor neutrality, implementations are still tied to specific vendors of PLCs or OPC UA software development kits [25]. * Economic viability--Implementations often lack cost-benefit analysis of data acquisition and integration solutions. * Security simplification--The cyber-security vulnerability of presented applications is often outside of the primary scope of research. In an attempt to solve some of these drawbacks, we have opted into proposing an approach which can help alleviate decision-making pressure from management workers responsible for production planning.