Abstract
The management of a complex manufacturing enterprise requires great attention to all interrelated business processes. Such processes are characterized by a high level of complexity, and additionally, by massive volumes of aggregated information. The current approach of production management is based on using a standard industrial methodology adopted for various enterprises. The industrial methodology contains algorithms and coefficients, accumulated from the statistic of the whole industry. The principal disadvantage of this approach is a strong contrariety between real production indicators and indicators described in the methodology. Enterprise management tasks must be solved using new automation and intellectualization approaches to analysis and forecasting of production indicators. Information systems of the enterprise contain all the necessary information to evaluate the state of production. The production processes can easily be represented by a discrete time series that could be extracted from information systems. It is necessary to use time series that modeling with type 2 fuzzy sets to account for the fuzziness of the real world. Using the fuzzy approach allows creating models that can improve the quality of the decision-making. The fuzzy approach and ontology engineering methods are used in this research. The hybridization of these approaches allows analyze the data about production processes and makes linguistic summarization of the production state in the process of decision-making.
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The reported study was funded by RFBR and the government of Ulyanovsk region according to the research projects: 18-47-732016, 18-47-730022, 18-47-730019, and 19-47-730005.
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Romanov, A., Yarushkina, N., Filippov, A. (2020). Application of Time Series Analysis and Forecasting Methods for Enterprise Decision-Management. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_31
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