The Use of AI in Demand Forecasting for Small and Medium Enterprises
Pujol Volodina, Maxim (2025)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025051311470
https://urn.fi/URN:NBN:fi:amk-2025051311470
Tiivistelmä
Having accurate demand forecasting is crucial for SMEs. The current traditional statistical methods used mostly by SMEs often struggle with the lack of high-quality data available. AI techniques can provide more precise forecasts, but currently there are not much guidance available specifically for SMEs.
This study will show how can SMEs adopt AI demand forecasting, and what benefits and barriers they encounter compared with classical approaches. It is based on reviews of the most up-to-date academic papers, industry reports and SME case studies. The available literature was analysed in depth and the performance levels of statistical, AI and hybrid models were compared.
The results show that AI models can significantly decrease forecast inaccuracies, but also highlights that success is linked to data quality, well-planned implementations and external support. The thesis offers a step by step framework that provides SME managers and policymakers a practical and straightforward roadmap for an effective AI implementation.
This study will show how can SMEs adopt AI demand forecasting, and what benefits and barriers they encounter compared with classical approaches. It is based on reviews of the most up-to-date academic papers, industry reports and SME case studies. The available literature was analysed in depth and the performance levels of statistical, AI and hybrid models were compared.
The results show that AI models can significantly decrease forecast inaccuracies, but also highlights that success is linked to data quality, well-planned implementations and external support. The thesis offers a step by step framework that provides SME managers and policymakers a practical and straightforward roadmap for an effective AI implementation.