Empirical testing of forecast update procedure for seasonal products

Abstract

Updating of forecasts is essential for successful collaborative forecasting, especially for seasonal products. This paper discusses the results of a theoretical simulation and an empirical test of a proposed time-series forecast updating procedure. It involves a two-stage longitudinal case study of a toy supply chain. The theoretical simulation involves historical weekly consumer demand data for 122 toy products. The empirical test is then carried out in real-time with 291 toy products. The results show that the proposed forecast updating procedure: 1) reduced forecast errors of the annual consumer demand, 2) determined timing for the commitment to subsequent replenishment during the selling seasons within acceptable forecast uncertainty, and 3) facilitated collaborative forecasting with more accurate forecast updates. However, during the empirical test, the forecast updating procedure provided less forecast accuracy improvement and it needed a longer time to achieve relatively acceptable forecast uncertainty.