## 2 Literature Review
They developed a bi-objective mathematical model to assign the parts production to the MTS or MTO system to minimize both inventory space and set-up times. The above works studied the production planning of hybrid systems with the release of the parts for the MTS or MTO system. [PERSON] et al. [14] studied three different production systems--MTS, MTO, and hybrid MTS-MTO--using a system dynamics simulation approach. The main performance metrics analyzed were holding costs and lead time. The simulation results highlighted the superior capacity of the hybrid MTS-MTO system to handle dynamic conditions such as demand uncertainty, variable operating expenses, and pricing fluctuations. However, the use of a system dynamics model limited the range of parameters that could be investigated. [PERSON] et al. [15] proposed a scheduling approach to select jobs for production in either a pure MTS or a hybrid MTS-MTO system. The proposed model was tested in a simplified manufacturing system with two machinesand six jobs. [PERSON] et al. [16] developed and analyzed a hybrid Make-To-Stock (MTS) and Make-To-Order (MTO) production system using a Markov decision process (MDP) model, extending previous single-machine models to multiple machines. The system allowed machines to switch between MTS and MTO production, incurring setup periods. The MDP model aimed to minimize average costs per period, including MTS inventory holding, MTO backlog, setup, and lost sales costs. Numerical investigations assessed optimal switching strategies and machine dedication. Results indicated that switching one machine per period was sufficient, and partial dedication to MTS or MTO did not affect average costs, demonstrating efficient control of hybrid systems. [PERSON] and [PERSON] [5] in their review argued that hybrid production control has showcased substantial potential compared to other, more straightforward approaches. Nonetheless, these advancements frequently entail increased complexity, thereby posing implementation challenges in practical settings. To streamline the adoption of such intricate policies in real-world scenarios, sophisticated information technology becomes imperative. In this context, the Internet of Things (IoT) emerges as a pivotal concept, enabling pertinent product data to be transmitted and received autonomously, thereby facilitating network data exchange without the need for human intervention. [PERSON] and [PERSON] [17] conducted a systematic literature review on empirical research regarding the customer order decoupling point (CODP) in operations and supply chain management. Analyzing 40 articles, they identified 32 factors influencing operations upstream and downstream of the CODP, categorized into market/product, operations, supply chain, and performance measures. They proposed a framework highlighting distinct configurations for MTS (upstream) and MTO (downstream) operations, and suggested areas for further research and practical implications. One of the future research paths suggested is the mix of MTO and MTS items in the same production system. The review highlights various research efforts on hybrid MTS-MTO (Make-To-Stock/Make-to Order) production systems, but also identifies some limitations:  
* The complexity of the mathematical models proposed can be solved with heuristic approaches, and often the manufacturing systems are simplified. The complexity of real industrial cases can reduce the possibility of using these models. * The greater part of the models proposed are static and work with production planning models to assign to the MTO or MTS system. The dynamicity concerns the production planning of the systems, and not the changes of the decoupling point. * The dynamic decoupling point is studied in few works, with determination of the decoupling point when the customer orders are collected. Thus, a dynamic approach that changes the decoupling point during the manufacturing system activities has not been provided in the literature.