# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
## 5 Conclusions and the Direction of Future Research  
In conclusion, the state space search algorithm can provide an alternative approach to maintenance by optimizing the use of remaining spare parts. It identifies the most cost-effective route to mitigate potential risks, using selected samples of critical equipment in machinery as the basis for analysis. Permission to conduct this research was obtained from the Ethics Committee and Institutional Review Board through a formal process at a university in collaboration with an electrical wire manufacturing company in Thailand. Due to operational restrictions and trade secrets in certain areas of the machinery, it was only possible to install IIOT sensor kits on a limited number of samples. Data from these sensors could be collected from permissible sections of the system's database. As a result, the algorithm may not be able to provide the level of detail that would be expected.  
In the future, sustainability in smart factories can be achieved through the optimization of the IMS using the state space search algorithm, which can be applied to various types of industrial automation and smart factories [37]. By increasing the number of datasets and the variety of equipment monitored, the algorithm will be better positioned to learn and generate more accurate predictions. Additionally, advanced algorithms, such as quantum algorithms [38], can be utilized to accelerate searches and address complex problems more effectively. The challenges when incorporating quantum computing into IMS are the following:  
1. Fast SSS algorithms: Help to improve the speed for some algorithms that use more memory to process, such as BFS, Uniform Cost Search, and A* Search, but Quantum computers are highly susceptible to errors due to decoherence and quantum noise. Developing robust error correction methods is essential but remains a significant challenge.
2. Hardware Development: Quantum hardware is still in its early stages. Building reliable, fault-tolerant quantum computers that can operate consistently over long periods is a major hurdle, and because of complex problems typical in industrial settings, it is difficult.
3. Interdisciplinary Expertise: Implementing quantum computing in IMS necessitates collaboration between quantum computing experts and industry professionals to tailor algorithms and solutions to specific maintenance needs.
4. Cost: The high cost of quantum computing hardware and the associated infrastructure can be prohibitive for many industries; how to make quantum computing at low cost remains a significant challenge.  
Moreover, the application of deep learning algorithms within the industrial meta-verse [39; 40] can further facilitate remote monitoring and virtual reality while minimizing concerns regarding intellectual property leakage or trade secrets.  
Methodology, N.T., S.S. and A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted in accordance with the Declaration of Helsinki, the Belmont report, CIOMS guidelines and The international conference on harmonization Good clinical practice (ICH-GCP) and approved by the Institutional Review Board of Chulalongkorn University, protocol code COA No. 153/67,researchNo. 660328, date of approval 26 March 2024.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.