10 Best Inventory Demand Forecasting Techniques

Moving Average: This method involves taking the average of the past demand data to predict future demand.

Simple Exponential Smoothing: This method involves using a weighted average of past demand data to make a prediction about future demand.

Holt’s Linear Exponential Smoothing: This method is an extension of simple exponential smoothing and takes into account the trend in demand.

Croston’s Method: This method is used to forecast intermittent demand, which is demand that occurs at irregular intervals.

ARIMA: Autoregressive integrated moving average is a statistical method for analyzing time series data, which can be used for demand forecasting.

Neural Networks: This method uses a type of artificial intelligence called a neural network to analyze past demand data and make predictions about future demand.

Time Series Decomposition: This method involves breaking down the time series data into its component parts, such as trend, seasonality, and residuals, and then using these parts to make predictions about future demand.

Regression Analysis: This method uses statistical techniques to analyze the relationship between demand and other factors, such as price, promotions, and weather.

Box-Jenkins Method: This method is used to identify and model the underlying patterns in time series data, which can be used for demand forecasting.

Croston-Tavener Method: This is a variation of Croston’s method and is used to forecast intermittent demand with varying frequencies and varying amplitudes.