## 4 Conclusions  
The developed method enables identification of the technical conditions of technical objects (TOs). In the presented method, the key is to identify three technical conditions, not two as usual. Apart from the first technical condition, i.e., able to further use, and the third technical condition, i.e., not able to further use, it is important in the process of predicting the State 0 (often caused by damage) to determine the state of limited ability of further use (State 2). This condition can be determined based on archival data in technical inspections, where it is necessary to specify the last inspection during which the technical object was operational. Periodic technical inspections are used in the case of many technical objects, where, among others, we can distinguish vehicles and individual elements of various types of vehicles. In response to the hypothesis presented in this article, based on the conducted research, it can be stated _that combining two different AI methods can lead to achieving predictive results that more accurately correspond to real outcomes than using each predictive method separately_. The hybrid method, i.e., combining ANN and decision trees, proved to yield better classification results for State 2 than each of the mentioned methods separately. In the field of transport, and more precisely in supply chains, if any vehicle components are damaged, it can result in delays in the delivery of goods. The elimination of undesirable damage to the means of transport through the possibility of predicting the state of failure may increase the reliability of an entire supply chain. From the aspect of sustainability, the issue of reducing the number of failures also makes it possible to reduce supply chain disturbances, to reduce costs associated with delays, and to reduce the materials needed for the repair of the means of transport, since in this case the costs only relate to the replaced elements before their damage. Thus, it is impossible for more serious damage to occur. Often, failure of one item causes damage to others, which generates unnecessary costs and increases the amount of waste due to the number of damaged items. The proposed predictive maintenance approach, which involves a hybrid method of technical condition prediction based on AI for reducing supply chain disruptions, aligns well with the principles of sustainability and contributes to the broader concept of sustainable development in supply chain management in several ways as follows:  
**Resource Efficiency. ** By using AI to predict the technical condition of equipment and components, organizations can optimize maintenance activities. This reduces the consumption of spare parts, minimizes energy usage, and prolongs the lifespan of assets. These resource-efficient practices contribute to sustainability by conserving resources and reducing waste. Predictive maintenance minimizes unplanned breakdowns and equipment failures, reducing the need for emergency repairs and replacement parts. This leads to a reduction in the generation of waste, including discarded equipment and components, which aligns with intending to minimize environmental impact. By ensuring that equipment is in optimal condition and operating efficiently, predictive maintenance reduces energy consumption. This leads to lower greenhouse gas emissions and contributes to efforts to combat climate change. Predictive maintenance enhances supply chain resilience by reducing the risk of disruptions caused by equipment failures. A more resilient supply chain is better equipped to handle unexpected challenges, such as natural disasters, and therefore, is more sustainable in the long run. Predictive maintenance can lead to significant cost savings in terms of reduced maintenance expenses, fewer breakdown-related costs, and improved asset utilization. These financial benefits contribute to the economic pillar of sustainability.