Medium‐ to Long‐Term Demand Forecasting in Retail and Manufacturing Organizations: Integration of Machine Learning, Human Judgment, and Interval Variable

Considering that, in the recent past, several studies have reasserted that human judgment is still valuable for forecasting in supply chains, this paper proposes a demand forecasting decision model (DFDM), which mathematically integrates expert judgment estimates with forecasts generated from time series and machine‐learning models. An interval 3‐point elicitation procedure has been used to effectively obtain estimates from experts, and further, a mathematical bias adjustment mechanism is used to detect and eliminate any systematic bias in experts' forecasts. The real‐life data from manufacturing and retail firms are collected to test the proposed model. The independent variables in the data are taken as interval data series rather than crisp values to capture the uncertainty in the variables and use them for forecasting models. Error metrics to measure bias (mean error), accuracy (mean absolute error), and variance (mean squared error) of forecasts were used to evaluate the performance of the proposed DFDM. The forecasts from the proposed model were found to be significantly better than those from popular forecasting methods in practice. Finally, using temporal disaggregation, an extension to the proposed models is presented to generate very short‐term forecasts to help managers make better short‐term operational decisions.