An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles

Many successful technology forecasting models have been developed but few researchers have explored a model that can best predict short product lifecycles. This research studies the forecast accuracy of long and short product lifecycle datasets using simple logistic, Gompertz, and the time-varying extended logistic models. The performance of the models was evaluated using the mean absolute deviation and the root mean square error. Time series datasets for 22 electronic products were used to evaluate and compare the performance of the three models. The results show that the time-varying extended logistic model fits short product lifecycle datasets 70% better than the simple logistic and the Gompertz models. The findings also show that the time-varying extended logistic model is better suited to predict market capacity with limited historical data as is typically the case for short lifecycle products.