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

Charles V. Trappey, Hsin Ying Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)421-430
Number of pages10
JournalAdvanced Engineering Informatics
Volume22
Issue number4
DOIs
StatePublished - 1 Oct 2008

Keywords

  • Extended logistic model
  • Gompertz model
  • Short product lifecycle
  • Simple logistic model
  • Technology forecasting

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