Material demand forecasting with classical and fuzzy time series models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15210%2F21%3A73612049" target="_blank" >RIV/61989592:15210/21:73612049 - isvavai.cz</a>

Výsledek na webu

<a href="https://annals-csis.org/Volume_29/drp/pdf/8.pdf" target="_blank" >https://annals-csis.org/Volume_29/drp/pdf/8.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.15439/2021B8" target="_blank" >10.15439/2021B8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Material demand forecasting with classical and fuzzy time series models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Material demand forecasting with classical and fuzzy time series models

Popis výsledku anglicky

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.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Annals of Computer Science and Information Systems

ISSN

2300-5963

e-ISSN

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Svazek periodika

29

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

6

Strana od-do

1-6

Kód UT WoS článku

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EID výsledku v databázi Scopus

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