Abstract
Actually, a lot of companies have tried to optimize their systems of warehouse stock management to minimize the production costs. The main goal is evident – not to spend too much money for stock. To predict the behaviour of the system, there are usually used the methods of time series analysis. They are able to determine the main trend very well, seasonal influences, etc. But they do not take into account the internal and external influences acting on the system. For their destination there is appropriate to use the expert knowledge from companies that are often vague. As an appropriate tool, therefore appears to be a fuzzy expert system. Its use, however, causes problems if the system exhibits a significant trend. It turns out that the system for determining the main trend is appropriate to use methods known from the time series analysis and then followed by taking advantage of the expert system. This paper presents a fuzzy expert system that combines expert knowledge with the analysis of the trend of the system. The presented expert system was also verified in a practical application.
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Acknowledgment
This work was supported by the project “LQ1602 IT4Innovations excellence in science” and during the completion of a Student Grant with student participation, supported by the Czech Ministry of Education, Youth and Sports.
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Farana, R., Formánek, I., Klimeš, C., Walek, B. (2017). Warehouse Stock Prediction Based on Fuzzy-Expert System. In: Silhavy, R., Silhavy, P., Prokopova, Z., Senkerik, R., Kominkova Oplatkova, Z. (eds) Software Engineering Trends and Techniques in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-57141-6_4
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DOI: https://doi.org/10.1007/978-3-319-57141-6_4
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