A Data-Driven Newsvendor Problem with Shifting Demand: A Deep Autoregressive Model with Attention Mechanism

In a complex supply chain, product demand is characterized by non-linearity and instability. In particular, demand trajectory with intermittent or abnormal spikes exists, which makes it difficult for suppliers to accurately estimate the time-varying demand distribution and make the accurate inventory decision. Hence, a deep autoregressive model with a time attention mechanism (Attention-DeepAR model) was proposed to overcome this practical issue. In addition, a separated estimation and optimization approach was provided to solve a newsvendor problem with shifting demand. Temporal features were extracted from historical data through the Attention-DeepAR model to identify the long-term and short-term trends of the demand. Accordingly, the time-varying demand distribution was accurately fitted, which can assist in making precise inventory decisions. In addition, the rolling window design was proposed to introduce new demand data to update the demand. Finally, the validity of the Attention-DeepAR model was verified through the Monte Carlo simulations and a real case. Results show that the Attention-DeepAR model can effectively capture the temporal correlation between the regular and promotional demand values under volatile demands, improving the fitting accuracy of the time-varying demand distribution. This model can provide precise inventory decisions and significantly decrease average total costs. © 2023 School of Science, IHU. All rights reserved.