Material demand forecasting with classical and fuzzy time series models

• Autorzy: Zakrytnoy Sergey , Luukka Pasi , Stoklasa Jan

Identyfikatory

DOI

10.15439/2021B8

• Konferencja: International Scientific Conference Knowledge on Economics and Management KNOWCON 2021, (17 ; 11-12.11.2021 ; Olomuniec, Czechy)
• Języki publikacji: EN

Abstrakty

EN

Direct material budgeting is an essential part of financial planning processes. It often implies the need to predict quantities and prices of hundreds of thousands of materials to be purchased by an enterprise in the upcoming fiscal period. Distortion effects in demand projections and overall uncertainty cause the enterprises to rely on internal data to build their forecasts. In this paper we are dealing with material demand forecasting and evaluate the feasibility of fuzzy time series forecasting models as compared to classical forecasting models. Relevant methods are shortlisted based on existing practice described in academic research. Three datasets from industry are used to evaluate the predictive performance of the shortlisted methods. Our findings show an improvement in prediction accuracy of up to 47% compared to naïve approach. Fuzzy time series models are reported to be the most reliable forecasting method for the analyzed intermittent time series in all three datasets.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 29
• Strony: 1--6
• Opis fizyczny: Bibliogr. 18 poz., wz., tab., wykr.

Twórcy

autor

Zakrytnoy Sergey

• Sievo Oy, Mikonkatu 15 A, 00100 Helsinki, Finland

autor

Luukka Pasi

• School of Business and Management, LUT University, Yliopistonkatu 34, 53851 Lappeenranta, Finland

autor

Stoklasa Jan

• School of Business and Management, LUT University, Yliopistonkatu 34, 53851 Lappeenranta, Finland
• Palacky University Olomouc, Faculty of Arts, Department of Economic and Managerial Studies

Bibliografia

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