## 1 Introduction  
Since the advent of Industry 4.0 as a new paradigm, the manufacturing industry has evolved significantly. Furthermore, the paradigm of a new manufacturing system shifted rapidly due to the increasing demands of mass personalization, sustainable production, sustainable products, etc. Hence, a variety of smart manufacturing concepts and architectures have been suggested. [PERSON] [1] suggested the latest term of smart manufacturing systems (SMSs), which includes six essential pillars: materials, data, predictive engineering, sustainability, resource sharing and networking, and manufacturing technology and processes [2; 3]. SMSs employ similar technologies, such as information and communication technology (ICT), industrial internet of things (IIoT), artificial intelligence (AI), big data, sensor data, etc. These technologies have led to the smartization of manufacturing and highly connected resources throughout the process [4]. [PERSON] et al. [2] described fully integrated autonomous functions for smart manufacturing, as follows:  
* Integration, organization, and allocation for advanced information and communication technologies [5];
* Self-optimizing processes [6], data-driven decision making [7], self-regulation, and self-organization for adaptive and intelligent manufacturing control [8];
* Predictive situational awareness [9], statistical process monitoring and prognostics [10], predictive maintenance [10], and predictive manufacturing situations [11] for operation reliability and accuracy. Among these autonomous functions, the core functions are related to decision-making and predictions and preservation. A decision-making-related function consists of aspects such as self-optimization, self-organization, self-decision-making, etc. Moreover, prediction- and preservation-related functions consist of aspects such as real-time monitoring, predictive maintenance, etc. Autonomous functions are operated based on certain factors, including the distributed control system, multi-agent system, AI, knowledge-base, big data, etc. SMSs exhibit similar properties because they inherited the structures and characteristics of NGMSs. The holonic manufacturing system (HMS), the biological manufacturing system (BMS), the intelligent manufacturing system (IMS), and the fractal manufacturing system (FrMS) are the most popular representatives of the next generation manufacturing system (NGMSs). Nevertheless, studies have focused on evolving IMS, HMS, and FrMS into SMSs via new technologies such as AI, big data, sensor data, etc. Among NGMSs, FrMS reported by [PERSON] et al. [12; 13; 14] exhibits suitable characteristics such as a distributed control system and a multi-agent system, using AI and the knowledge-base, is required for SMSs. Furthermore, FrMS features vertical and horizontal structures based on the fractal structure. It is based on the concept of autonomously cooperating multi-agents termed as fractals, which are referred to as the basic fractal unit (BFU) [12; 13; 14; 15; 16]. Specifically, BFU consists of an observer, a reporter, a resolver, an analyzer, and an organizer. BFU determines the goal decision, process optimization, and reconfiguration within the system's final goal. To determine the system's goal, goal-orientation technology is adopted through the goal decision model. The structure and process of the manufacturing system are subsequently altered by DRP. Self-optimization is conducted via BFU based on the knowledge base. [PERSON] et al. [12; 13; 14] were the first to propose the concept of a goal model for FrMS. However, FrMS needs to be improved and evolved into SMSs, including sustainable development. FrMS has sufficient potential and the fundamental background required to evolve into SMSs. However, thus far, approaches based on big data or active data utilization have not been adopted in FrMS in order to reconfigure the processes and the system. Therefore, in this paper, we proposed an SSrMS architecture based on FrMS. SSrMS exhibits the features of a data-driven system.