# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
## 1 Introduction  
With the rise of smart manufacturing, businesses are increasingly embracing new information technologies such as the Internet of Things (IoT), cloud computing, big data, and artificial intelligence. These technologies are deployed to increase the efficiency and flexibility of production processes [1, 2]. These technological advances have also resulted in the emergence of new manufacturing models, such as cloud manufacturing, green manufacturing, and service-oriented manufacturing [3, 4]. These models are designed to meet the new demands of today's manufacturing--socialization, personalization, service delivery, and intelligence [5]. Scheduling production within custom manufacturing plays a very important role today, especially in the context of the dynamic needs of smart manufacturing systems [1, 5]. Intelligent production planning must meet new, complexgoals that go beyond those of traditional approaches. The main requirements include ensuring real-time production flexibility [6; 7]. Modern manufacturing companies often encounter a huge number of operations and complicated relationships between individual production processes [8]. These processes are often highly interconnected and require a continuous flow of information and materials [9]. As a result of the dynamic environment in the company, where unexpected changes or failures can occur at any time, the continuity of the entire production system can be disrupted. Therefore, it is essential to react quickly to dynamic events to ensure production continuity and efficiency [10; 11]. In addition, uncertain events are often present in production processes, which can lead to information asymmetry between the planned and actual production process [12]. This may affect the achievement of the set production targets. Static planning that does not consider these dynamic changes can cause a significant difference between the original plan and the actual course of production. The result is the waste of production resources, increased costs, and reduced efficiency [13; 14]. Therefore, in modern manufacturing companies, it is important to include dynamic planning strategies in planning processes, which can flexibly respond to the current state of production, thus enabling the more efficient use of resources and the optimization of production in real time [15]. For short-term planning based on a digital twin, the solution needs to have the following properties: connectivity, modularity, parametrization, controllability, and the possibility to visualize the outputs and consider the complexity of production [16]. A digital twin consists of a physical and a virtual space that communicate through channels. The virtual space collects data from the physical space (machines, MES, ERP), which are then used for simulations and production plans [17; 18; 19]. Based on previous research in the real world [6; 10; 20; 21; 22; 23], a modular structure for the future system was designed to achieve the desired properties. * Data collection and monitoring module--includes a network of sensors, monitoring devices, and business systems (MES, ERP, APS, SCADA). * Input data processing module--filters and analyzes data using big data tools and statistical methods. * Simulation and schedule generation module--uses decision rules and optimization methods to create production plans. * Statistical module--evaluates the production efficiency and equipment utilization and identifies bottlenecks. * Control module--provides a user interface to set up simulations and approve plans. * Communication module--ensures data exchange between the production system, cloud, and simulation model. A schematic representation of the modular structure is shown in Figure 1. The data collected are divided into static, dynamic, and random data and can come from different sectors within the company (logistics, maintenance, production, etc.). Production equipment is usually monitored using sensors and technologies such as RTLS, Bluetooth, Wi-Fi, RFID, optical sensors, or AI recognition.