# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
## 4 Discussion  
Research on preventive maintenance and the design of intelligent maintenance systems for the era of Industry 4.0 reveals significant interest in the modernization of machine maintenance within smart factories. Ongoing advancements in design are essential to meet the evolving demands of this environment. The research findings indicate enhanced maintenance efficiency, with the primary goals of reducing costs, minimizing downtime, and improving user-friendliness [7; 9]. Artificial intelligence is increasingly employed to prioritize maintenance tasks in industrial applications, effectively reducing the duplication of engineering work in large facilities that must manage multiple maintenance activities. The research found that AI algorithms can enhance efficiency in task queuing by building upon the gold standard for telecommunications network elements (NEs). Additionally, the UPDT study includes an investigation into models designed to optimize machine downtime in various ways, aimed at minimizing human error [7]. Intelligent sensor technology, combined with SCADA software and IT tools, facilitates systematic UPDT management [9] and contributes to the development of a predictive maintenance framework aimed at reducing UPDT [8]. In addition, deep learning, neural networks, heuristic approaches, and research on the application of state space search algorithms across various fields have been shown to help minimize total annual costs within a constrained spare parts budget [31].  
Building on previous research in IMS, CMMS, and AI algorithms within the fields of industry and smart technology, this study highlights the advantages and identifies the gaps in existing research. These insights have been used to enhance the new IMS design, enabling the system to retrieve the necessary variables for processing with greater precision and accuracy. Without introducing issues or discrepancies in the original system, direct and indirect costs are key variables imported from the database, alongside equipment and working hours data from sensors. When processed by the SSS algorithm, these data enable the system to forecast optimization, leading to more efficient UPDT management guided by the FMEA risk standard.  
The results of this research, through the design of a new IMS, incorporate real-time data at all points of interest. This includes monitoring the lifespan of equipment when machinery starts or stops. As the components of interest move, the system counts the operating hours to compare with the actual lifespan of the equipment and retrieves maintenance cost data for individual equipment from the warehouse database. The algorithm can identify the lowest-cost maintenance path compared to traditional periodic maintenance, potentially reducing costs by up to 45%. When the algorithm considers RPN data from the warehouse database, it can present the risk associated with the equipment along with the operating hours data. If the usage exceeds the limit, the system will issue an alert, preventing  
Figure 23: Total cost for maintenance (**a**) Total cost from algorithms; (**b**) Total cost from traditional periodic maintenance.  
equipment damage, which is a primary cause of UPDT [36]. The research findings indicate that this approach can reduce up to 100% of UPDT.