# Towards a Flexible Smart Factory with a Dynamic Resource Orchestration
## 5 Literature Review  
A literature review was carried out as a part of Step II of the DSR methodology described in Section 2. Noted here are only the papers most relevant for the topics covered in this paper. As the production orchestration is the core functionality of our Orchestrator, first we present other implementations found in the literature. Additionally, in the light of the presented Orchestrator architecture, it is relevant to compare some of its segments with existing solutions and research ideas found in the literature as well. Highly frequent repetitive tasks may still be a common playground in production systems of today, but an imminent I4.0 trend is extensive product customisation and small lot-size of products [5]. To achieve customisation and potential production of a single product while integrating the new technologies, it is possible to implement an imagined black-box-like system that acts as an SF controller capable of coordinating all available assets depending on the input and desired output. This highly abstract controller is the Orchestrator, while the coordination process is orchestration [22; 23]. The digitalisation of the complete production process is a necessary step in reaching any of the numerous and distinct goals of Industry 4.0. The automated shop floor reconfiguration and service or task-oriented programming of machines are among these goals. Therefore, the complete production processes and all production resources need to have formal specifications, alongside the digitised product [24]. According to these descriptions, modern industrial software systems orchestrate the delegation of instructions and their assignment to smart resources [22; 23]. Drawing on the proposals of the authors [22; 23], here the proposed system is targeting the very execution of the delegated instructions, either in simulated or on the real-life shop floor. To achieve high production flexibility in SF, the variability of the production process needs to be fulfilled practically in the runtime, without stopping the production [25]. This means that any addition or reconfiguration of the participating physical resources should be carried out according to the Plug-and-Play concepts [26]. As previously discussed, the proposal is to always lean on resource discovery mechanisms, that are therefore also integrated into the Orchestrator. Smart resources will seamlessly enter the shop floor in SF, ready to be controlled by the Orchestrator. They will offer their particular capabilities and receive their assigned tasks for the execution accordingly. The matchmaking of the production process and the production resources according to their capabilities is enabled by the introduction of semantic-knowledge in the manufacturing environment, which formalises the rules and the relationships between the participating objects [27]. Ontologies and other semantic-knowledge concepts in production research are not a novelty. In the past two decades, various concepts have been presented in similar domains, from capability ontologies [28; 29] to robot and automation ontologies [30; 31]. An approach for automatically configuring a control system software composed of automation agents, based on a generative programming approach that uses a declarative knowledge base is presented in [32]. In contrast to the listed works, the presented solution uses a knowledge-based (KB) system to represent resources and their capabilities. This KB supports the mechanism of easy enrichment and add-on of every new shop floor resource that is capable of introducing it to the system. In the future, it should be able to easily lean on the currently developed administrative shell concept [20]. Standardisation is certainly one of the fundaments of the future factories, to guarantee interoperability between different modules of the production line, with different providers and manufacturers closely cooperating with one another and with the scientific community.