Abstract
The paper deals with the modelling of customer’s behavior in the shop of the retail chain. The paper shows that the fuzzy-expert system is a good tool for describing the behavior of a system, where the customer’s behavior is influenced by weather conditions and by events in the surroundings of the shop. The article also offers a procedure that allows dividing the system into logical units and reducing the number of necessary rules. The paper also details how the individual parts of the system have been verified. On specific real-time data the paper also presents the detection of incorrect (stereotypical) steps done by experts in compiling the knowledge base. The procedures that have been used have enabled effective identification and elimination of the errors. The advantage of our procedure was also that the IF-THEN rules that have been used were easily readable and understandable. At the end of the research work the expert system has been tested by means of available historical sales forecast data to optimize inventory, reduce storage costs, and reduce the risk of depreciation due to exceeding maximum warranty period. Achieved results have proved that fuzzy-expert systems are suitable also for the modelling of customer’s behavior and can provide us good results.
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Acknowledgment
This work was supported during the completion of a Student Grant with student participation, supported by the Czech Ministry of Education, Youth and Sports.
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Frankeová, M., Farana, R., Formánek, I., Walek, B. (2019). Fuzzy-Expert System for Customer Behavior Prediction. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_13
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DOI: https://doi.org/10.1007/978-3-319-91189-2_13
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Online ISBN: 978-3-319-91189-2
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