Journal of Scientific Papers


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ISSN 2071-789X

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  • General Founder and Publisher:

    Centre of Sociological Research


  • Publishing Partners:

    University of Szczecin (Poland)

    Széchenyi István University, (Hungary)

    Mykolas Romeris University (Lithuania)

    Alexander Dubcek University of Trencín (Slovak Republic)

  • Membership:

    American Sociological Association

    European Sociological Association

    World Economics Association (WEA)




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

Vol. 15, No 4, 2022

Andrea Kolková


Faculty of Economics, VŠB - Technical University Ostrava,

Ostrava, Czech Republic


ORCID 0000-0002-4764-3164


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


Aleksandr Ključnikov


Faculty of Entrepreneurship and Law, Pan-European University,

Prague, Czech Republic


ORCID 0000-0003-0350-2658


Abstract. 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.


Received: January, 2022

1st Revision: July, 2022

Accepted: December, 2022


DOI: 10.14254/2071-789X.2022/15-4/2

JEL ClassificationM21, M15, C53

Keywords: demand forecasting, prophet, SME, enterprise, statistical model, artificial intelligence, hybrid model