Chapter 5 Demand Forecasting

Multiple regression forecast

A forecast technique using multiple regression.

naîve forecast

A forecasting approach where the actual demand for the immediate past period is used as a forecast for next period's demand.

Linear trend forecast

A forecasting method in which the trend can be estimated using simple linear regression to fit a line to a time series of historical data.

cause-and-effect forecasting

A forecasting method that uses one or more factors (independent variables) that are related to demand to predict future demand.

running sum of forecast errors (RSFE)

A measure of forecast bias - that is, whether the forecast tends to be consistently higher or lower than actual demand.

Forecast Bias

A measure of the tendency of a forecast to be consistently higher (negative bias) or lower (positive bias) than the actual demand.

simple moving average forecast

A method that uses historical data to generate a forecast; it works well when the demand is fairly stable over time.

time series forecasting

A prediction technique based on the assumption that the future is an extension of the past and that historical data can thus be used to forecast future demand.

tracking signal

A tool used to check the forecast bias.

collaborative planning, forecasting, and replenishment (CPFR)

According to the Council of Supply Chain Professionals, "CPFR seeks cooperative management of inventory through joint visibility and replenishment of products throughout the supply chain. Information shared between suppliers and retailers aids in planning and satisfying customer demands through a supportive system of shared information. This allows for continuous updating of inventory and upcoming requirements, essentially making the end-to-end supply chain process more efficient. Efficiency is also created through the decrease expenditures for merchandising, inventory, logistics, and transportation across all trading partners."

Business cycle

Alternating periods of expansion and contraction in economic activity.

Mean absolute deviation (MAD)

An indicator of forecast accuracy based on an average of the absolute value of the forecast errors over a given period of time. The measure indicates, on average, how many units the forecast is off from the actual data.

Mean absolute percentage error (MAPE)

An indicator of forecast accuracy based on the true magnitude of the forecast error. The monthly absolute forecast error divided by actual demand is summed, then divided by the number of months used in the forecast to derive an average, and lastly multiplied by 100. The measure indicates, on average, what percent the forecast is off from the actual data.

Mean square error (MSE)

An indicator of forecast accuracy. The forecast errors are squared and then summed and divided by the number of periods to determine the mean square error. The measure penalizes large errors more than small errors.

quantitative forecasting methods

Forecasts based on mathematical models and relevant historical data.

qualitative forecasting methods

Forecasts based on opinions and intuition.

Forecast error

The difference between actual demand and the forecast.