The paper presents problems of decision-making for planning and implementation of vehicle service system supplies with spare parts under incomplete information. The lack of effective supply planning models using artificial intelligence principles contributes to widening the gap in the problem. The analyses confirm that information uncertainty is one of the main factors in supply failures leading to financial losses for both vehicle service stations and supply companies. Authors structured national vehicle service system by classifying its three different segments. This allowed the identification of risks of making incorrect logistics decisions in each of defined segments. It has been shown that the supply planning process in each of segments is carried out according to different rules. Authorial decision models for each of segments are then presented. The models can be used as a tool to support and improve supplying vehicle service stations in conditions of information uncertainty. In the application part, a proprietary algorithm has been developed to solve proposed models.