Abstract
The quality of technical services is one of the main criteria for assessing the service processes of agricultural machinery, and it has a significant impact on the decision-making process when choosing a service provider. Technical service quality has a significant role in maintaining agricultural machinery in optimal technical condition, thus ensuring its high reliability and durability. The purpose of this study is to present a decision support method for choosing the right agricultural machinery service facility. The method is based on fuzzy inference. The choice of service workshop is based on decision criteria individually accepted by farmers (experts). The method was checked by way of research carried out among 25 farmers facing the choice of a service facility. The decision-making process allows for ranking the decision criteria and decision-makers. The results of the presented research can be used by farm owners and service companies to plan their development directions.
Highlights
Agricultural machines constitute a group of technical objects which are clearly distinguished from others
The aim of this research is to present a method using fuzzy logic to support the decision-making processes involved in choosing a service workshop
The choice of this method is justified by the fact that fuzzy logic allows the possibility of considering measurable and non-measurable features
Summary
Agricultural machines constitute a group of technical objects which are clearly distinguished from others. They should be characterized by high reliability and capability. Economic development in agriculture is expressed via levels of equipping with technical means. The development of agricultural techniques is closely related to the needs and financial resources of farms [2], including the need for modernization of machines within these financial resources [3]. Instead of zeros and ones (0 or 1), fuzzy logic enables the use of linguistic variables. They assume imprecise values and concepts of spoken language. It allows us to describe occurrences of an ambiguous nature that cannot be described in binary terms [22,23,24]
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