Intermittent demand forecasting for medical consumables with short life cycle using a dynamic neural network during the COVID-19 epidemic.
Accurate demand forecasting is always critical to supply chain management. However, many uncertain factors in the market make this issue a huge challenge. Especially during the current COVID-19 outbreak, the shortage of certain types of medical consumables has become a global problem. The intermittent demand forecast of medical consumables with a short life cycle brings some new challenges, such as the demand occurring randomly in many time periods with zero demand. In this research, a seasonal adjustment method is introduced to deal with seasonal influences, and a dynamic neural network model with optimized model selection procedure and an appropriate model selection criterion are introduced as the main forecasting models. In addition, in order to reduce the impact of zero demand, it adds some input nodes to the neural network by preprocessing the original input data. Lastly, a modified error measurement method is proposed for performance evaluation. Experimental results show that the proposed forecasting framework is superior to other intermittent demand models.
['Liu|Peipei|P|0000-0002-9838-7762']
D000086382:COVID-19 D006801:Humans D008421:Materials Management, Hospital D016571:Neural Networks, Computer D058873:Pandemics D000086402:SARS-CoV-2 D012621:Seasons
intermittent demand forecasting short life cycle dynamic neural network optimized model selection accuracy measurement