To handle the increasing variety and complexity of managerial forecasting problems, many forecasting Demand Forecasting techniques have been developed over time. Each has its special use, and care must be taken to select the correct technique for a particular application. The manager as well as the forecaster has a role to play in technique selection; and the better they understand the range of forecasting possibilities, the more likely it is that a company’s forecasting efforts will bear fruit.
To have an idea on the impact of the forecast accuracy on the bottom line, take following rule of thumb: each 1% improvement in forecast accuracy translates to 0.5-1 % less inventory.
– Naive Forecasting: Estimating technique in which the last period’s actuals are used as this period’s forecast, without adjusting them or attempting to establish causal factors. It is used only for comparison with the forecasts generated by the better (sophisticated) techniques.
– Time Series Analysis: The cornerstone of traditional forecasting is based on the Fourier series time series mathematical analysis conceived by Joseph Fourier in 1822. Fourier statistical modeling uses a historical data series to create seasonal forecasts and set the course of forecasting for the next 125 years.
– Moving Average: Moving Average (MA) is a popular method for averaging the results of recent sales history to determine a projection for the short term. The MA forecast method lags behind trends. Forecast bias and systematic errors occur when the product sales history exhibits strong trend or seasonal patterns. This method works better for short range forecasts of mature products than for products that are in the growth or obsolescence stages of the life cycle.
– Exponential smoothing: In 1957, Holt-Winters took time series analysis to a new level with exponential smoothing. Inherent in the collection of data taken over time is some form of random variation. There exist methods for reducing of canceling the effect due to random variation. An often-used technique is “smoothing”. This technique, when properly applied, reveals more clearly the underlying trend, seasonal and cyclic components. It is a very popular scheme to produce a smoothed Time Series. The past observations are not weighted equally. Exponential Smoothing assigns exponentially decreasing weights as the observation get older. In other words, recent observations are given relatively more weight in forecasting than the older observations. First suggested by Charles C. Holt in 1957 it was meant to be used for non-seasonal time series showing no trend. He later offered a procedure (1958) that does handle trends. Winters (1965) generalized the method to include seasonality, hence the name “Holt-Winters Method”.
– Box–Jenkins method: Named after the statisticians George Box and Gwilym Jenkins, applies autoregressive moving average ARMA or ARIMA models to find the best fit of a time-series model to past values of a time series. The first step in developing a Box–Jenkins model is to determine if the time series is stationary and if there is any significant seasonality that needs to be modelled.
– Demand Sensing: Demand Sensing is a next generation forecasting method that leverages predictive analytics and near real-time information to create an accurate forecast of demand, based on the current realities of the supply chain. The typical performance of demand sensing systems reduces near-term forecast error by 30% or more compared to traditional time-series forecasting techniques.
Demand Sensing imports fresh daily demand data, immediately senses demand signal changes compared to a detailed statistical demand pattern, and evaluates the statistical significance of the change. It analyzes partial period actual demand to perform automatic short-term forecast adjustments using probabilistic pattern recognition and predictive analytics. Advanced statistical analytics identify and rapidly react to replenishment issues or sudden changes in customer demand.