M. Hibon, N. Kourentzes and S. F. Crone, 2013, The 33rd Annual international Symposium on Forecasting, Seoul.
New product forecasting is a prerequisite for operational decisions in production and inventory management. With no historic demand data, traditional statistical forecasting methods cannot be employed and new product forecasting is left to the judgment of human experts. With some industries introducing thousands of new products multiple times per year, analytical methods to forecast new products are needed. This paper proposes a methodology of time series clustering and similarity search for analytical, data driven and fully automatic new product forecasting by analogies; designed to construct launch profiles from past product launches data, it utilitizes increasing sources of information including product features before launch, recalibrated by using initial orders during launch and early sales observations past launch. The method provides forecasts for new products and empirical quantiles which are used to derive safety stocks. Its promising performance is illustrated in an empirical evaluation using real data from the textile industry.