Supply-Demand Forecasting Using Adaptive Neuro Fuzzy Expert Systems

Supply-Demand Forecasting Using Adaptive Neuro Fuzzy Expert Systems

Divya Rathwe1, Prof. Virendra Verma2

Abstract: Recently, there has been a substantial increase in the demand for pharmaceutical products and pharmaceuticals as a result of the ongoing COVID-19 situation. There have been multiple recorded cases where a significant shortage of medication has resulted in the death of individuals. Therefore, it is crucial to adopt a comprehensive strategy to demand forecasting for the pharmaceutical supply chain. Recently, soft computing techniques are being used widely for demand forecasting due to relatively higher accuracy compared to conventional statistical techniques. This study introduces a hybrid model called the wavelet transform-adaptive neural fuzzy inference system (DWT-ANFIS) for predicting pharmaceutical product forecasts. The Discrete Wavelet Transform (DWT) has been employed as a tool for data cleansing and optimization, while the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been utilized for forecasting purposes. The assessment of the suggested work was conducted using the mean absolute percentage error, regression analysis, and system iterations. Evidence demonstrates that the suggested approach surpasses the current system in terms of the Mean Absolute Percentage Error (MAPE).

Keywords: Demand Forecasting, Machine Learning, ANFIS, MAPE, Accuracy.