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National Repository of Grey Literature 1 records found  Search took 0.00 seconds. 
Integrating Artificial Intelligence into Fast-Moving Consumer Goods
 Bagi, Juraj ; Hříbek, David (referee) ; Rozman, Jaroslav (advisor)
Accurate sales forecasting is pivotal for operational efficiency in the Fast-Moving Consumer Goods (FMCG) sector. This thesis explores the application of Long Short-Term Memory (LSTM) models, a specialized form of recurrent neural networks, to enhance the precision of sales predictions. Unlike traditional statistical methods, LSTMs are adept at capturing temporal dependencies within sales data, potentially offering more accurate forecasts. By applying LSTM models to historical sales data from a food industry company, this research demonstrates improvements over conventional forecasting techniques. The findings suggest that LSTMs can significantly help FMCG companies in optimizing inventory management and demand planning, thereby contributing valuable insights into artificial intelligence applications in supply chain management. These results emphasize the practical implications for FMCG stakeholders to embrace advanced artificial intelligence technologies to remain competitive in a dynamic market environment.
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