A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Abstract

In the field of transport, and more precisely in supply chains, if any of the vehicle components are damaged, it may cause delays in the delivery of goods. Eliminating undesirable damage to the means of transport through the possibility of predicting technical conditions and a state of failure may increase the reliability of the 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. This article provides an author’s method of technical condition prediction; by applying the method, it would be possible to develop recommended maintenance activities for key elements related to the safety and reliability of transport. The combination of at least two artificial intelligence methods allows us to achieve very good prediction results thanks to the possibility of individual adjustments of weights between the methods used. Such predictive maintenance methods can be successfully used to ensure sustainable development in supply chains.