Demand Forecasting Using MLR-ARIMA Hybrid Model
Authors:-Vaibhav R. A. Prasad, Anunita Bhattacharya
Abstract- Data analytics (DA) is becoming increasingly important in supply chain management (SCM) due to its ability to provide valuable insights that can improve efficiency and decision-making. One of the key applications of DA in SCM is demand forecasting, which involves predicting future demand for products or services. Accurate demand forecasting is crucial for ensuring that the right amount of inventory is maintained, reducing the risk of stock outs, and optimizing production and logistics processes. There are several algorithms that can be used for demand forecasting in SCM, and they can be broadly classified into two categories: time-series forecasting and causal forecasting. Time-series forecasting algorithms rely on historical data to make predictions. This study will Evaluate both time-series and casual algorithms and study their efficacy and uses.
