# Towards a Flexible Smart Factory with a Dynamic Resource Orchestration
## 6 Conclusions
**Generic, dynamical scheduling is hard to achieve** as the complexity becomes drastically higher when orders multiply, and scheduling of orders that are different in variability is required to be carried out in parallel. Scheduling of the assembly of individual order of Controller-A proved to be a relatively simple challenge that the Orchestrator handled close to real-time. Complexity is higher when addressing variability of the user-specific requests in step 2 of the described industrial assembly process. The complexity of the scheduling rises, as expected, thus making the scheduling problem solution time much longer. Although some level of generic scheduling mechanism was partly a goal of this research and application, it was not possible in the current context. Scheduling proves to be tightly dependent on the factory-specific optimisation criteria, but also on the needs and characteristics of the shop floor itself. For the purpose of this proof-of-concept, scheduling was made for a specific process only, and dynamical scheduling will be a part of future research. In comparison to the use-case shown previously in [19], it is harder to achieve complete synchronisation between the simulation and real-time execution. Both simulations--one created in a proprietary graphical editor, and the other shown in the Process Modeller tool--were adapted following specific characteristics of the assembly process, thus lacking the feature of genericity. Although the creation of generic Digital Twin was not a part of this research, the emergence and addition of potentially powerful solutions will be gladly accepted. In our opinion, in the near future, it will be possible to supplement--if not completely replace--some of the functions of expert workers and to automate and enrich current assembly lines by adaptation. An automated black-box that controls the whole factory production is far away, but technologies and solutions similar to ours bring us one step closer. It is possible to make formal models of both the production processes and the shop floor resources (RQ1). This formal modelling aims to support the dynamic orchestration of the shop floor resources to follow the production processes. A system that enables automation of this dynamic orchestration is attainable by using the approach presented here (RQ2). The knowledge built into the KB can already be used for extending plans to procure new resources, supporting supply chain configuration, as well as for optimising the production planning as a whole. A big breakthrough in Artificial Intelligence and other technologies are anticipated, in several aspects such as matching, scheduling and command execution without pre-teaching of resources. Our research group is continuing the work on the presented architecture. In this paper, the fundaments and the first results of the research are given and discussed. Here, discussed are some of the steps of the DSR methodology chosen for this research, as stated in Section 2 of the paper. The Evaluation and the Communication steps are still ongoing--detailed metrics and in-depth results will be published upon completion of the Evaluation. The lessons learned so far confirm that many areas of investigation are still open, in addition to those already started. Among others, these include:  
* the extension of the scheduling algorithms based on Artificial Intelligence (AI) towards dynamical I4.0 scheduling solutions;
* decentralisation of the orchestrating process to the participating assets;
* further adaptation of existing legacy and non-smart resources by leaning on the concepts developed by organisations responsible for standardisation and interoperability;
* automatic derivation of the production process steps out of the provided CAD/CAM diagrams or recipes; and
* using the previously collected data and acquired AI knowledge to deduce the missing information on process descriptions.