## 1 Introduction
All the fractal units are operated based on the data in SSrMS. An external change is detected based on the environment information, which can serve as a signal to initiate a reconfiguration of the processes, structure, or the system itself. Internal changes are detected by the fractal units in SSrMS. This helps initiate negotiations or the re-optimizing process in the system. Furthermore, all the fractals exchange information, such as the current status, request, and response, to improve homeostasis and impart flexibility to account for changes. The optimization or reconfiguration results are stored in the knowledge base as a future reference for the next adaptation. For the data-driven system, SSrMS employs big data, which comprises the environment information, knowledge base, legacy system, and equipment signal DB. To determine the system's goal, each fractal's goal needs to be identified; thereafter, the process is reconfigured accordingly, and sustainability is assessed. First, in order to decide the goal for the fractal and system, the goal decision model was developed. The goal decision model provides various types of goals for each fractaland system, including productivity, cost, profit, etc. A neural network was used to develop the goal decision model, wherein it derives the goals of the system and each fractal. Thus, a goal-oriented system can be realized using the goal decision model. Second, in order to reconfigure a manufacturing process, the negotiation model is developed. The negotiation model performs partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system's condition. Third, in order to create balance between the goal of the system and sustainability, the sustainability assessment method was developed. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system's goals using the balanced scorecard (BSC), the analytic hierarchy process (AHP), the green-bill of material, and neural network. Given that the system and three methods are operated based on a data-driven system and big data, the proposed architecture and methods can serve as a fundamental basis for a new type of SMSs, one that is capable of self-optimizing and self-reconfiguring a system structure with manufacturing processes. This paper aims to propose a new architecture and concept of smart manufacturing systems based on the fractal concept to achieve fully integrated autonomous operations. The proposed architecture supports providing a basic research foundation for self-reconfiguration, goal-orientation, and self-organization. The proposed models for goal decision and negotiation can enhance the possibility of facilitating goal-oriented and self-reconfigurable manufacturing systems. The sustainability assessment method proposed in the study can provide a new research direction to harmonize sustainability and the system's goal for the homeostasis of the manufacturing system. This paper is organized as follows: Section 2 presents a literature review of existing manufacturing systems, such as SMSs, HMS, and FrMS, which have features of self-reconfiguration, self-optimization, and self-adaptation. In Section 3, the characteristics of SSrMS are presented. Section 4 presents the architecture of SSrMS with a sequence diagram of the manufacturing process reconfiguration. In Section 5, descriptions on the models for goal decision and negotiation as well as a sustainability assessment method are presented. Section 6 concludes this paper including further research.