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.
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