Applying deep learning to forecast the demand of a Vietnamese FMCG company - Dao, Le Duc; Khoi, Le Nguyen (Tạp Chí Khoa học Trường Đại học Quốc tế Hồng Bàng, 2023)
In the realm of Fast-Moving Consumer Goods (FMCG) companies, the precision of demand forecasting is essential. The FMCG sector operates in a highly uncertain environment marked by rapid market shifts and changing consumer preferences. To address these challenges, the application of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, has emerged as a vital solution for enhancing forecast accuracy. This research paper focuses on the critical role of demand forecasting in FMCG, emphasizing the need for LSTM-based deep learning models to deal with demand uncertainty and improve predictive outcomes. Through this exploration, we aim to illuminate the link between demand forecasting and advanced deep learning, enabling FMCG companies to thrive in a highly dynamic business landscape.