a cnn–lstm hybrid deep learning model for detergent products demand forecasting: a case study

An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. classical demand predictions are based principally on qualitative approaches relying on data from experts’ opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the covid-19 health crisis. in this paper, we present a hybrid neural network approach for accurate demand forecasts of the detergent manufacturing industry. it mainly consists of the combination of long short-term memory (lstm) with convolution neural network (cnn) based approaches. we performed a series of experiments on real data sets and assessed the performance of the proposed cnn–lstm hybrid model. numerical results showed that the combination of lstm layers with complementary cnn layers provides more accurate results than other state-of-the-art forecasting models.