Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04130081%3A_____%2F22%3AN0000006" target="_blank" >RIV/04130081:_____/22:N0000006 - isvavai.cz</a>

Alternative codes found

RIV/61989100:27510/22:10251144

Result on the web

<a href="https://www.economics-sociology.eu/?926,en_demand-forecasting-ai-based-statistical-and-hybrid-models-vs-practice-based-models-the-case-of-smes-and-large-enterprises" target="_blank" >https://www.economics-sociology.eu/?926,en_demand-forecasting-ai-based-statistical-and-hybrid-models-vs-practice-based-models-the-case-of-smes-and-large-enterprises</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14254/2071-789X.2022/15-4/2" target="_blank" >10.14254/2071-789X.2022/15-4/2</a>

Result language

angličtina

Original language name

Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Original language description

Demand forecasting is one of the biggest challenges of post-pandemic logistics. It appears that logistics management based on demand prediction can be a suitable alternative to the just-in-time concept. This study aims to identify the effectiveness of AI-based and statistical forecasting models versus practice-based models for SMEs and large enterprises in practice. The study compares the effectiveness of the practice-based Prophet model with the statistical forecasting models, models based on artificial intelligence, and hybrid models developed in the academic environment. Since most of the hybrid models, and the ones based on artificial intelligence, were developed within the last ten years, the study also answers the question of whether the new models have better accuracy than the older ones. The models are evaluated using a multicriteria approach with different weight settings for SMEs and large enterprises. The results show that the Prophet model has higher accuracy than the other models on most time series. At the same time, the Prophet model is slightly less computationally demanding than hybrid models and models based on artificial neural networks. On the other hand, the results of the multicriteria evaluation show that while statistical methods are more suitable for SMEs, the prophet forecasting method is very effective in the case of large enterprises with sufficient computing power and trained predictive analysts.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50204 - Business and management

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2022

Confidentiality

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

Name of the periodical

Economics and Sociology

ISSN

2071-789X

e-ISSN

—

Volume of the periodical

15

Issue of the periodical within the volume

4

Country of publishing house

PL - POLAND

Number of pages

24

Pages from-to

39-62

UT code for WoS article

000915274100002

EID of the result in the Scopus database

2-s2.0-85145176542