New product sales forecasting in the FMCG segment

Življenjski cikel novih izdelkov je vedno krajši, zato igra pomembno vlogo napovedovanje prodaje. Prve ocene prodaje so zelo pomembne za podjetja, saj usmerjajo podjetje pri planiranju kapacitet in nadzoru zalog. Cilj diplomske naloge je napovedati prodajo novih izdelkov v FMCG sektorju. Najprej smo pridobili primerne podatke in jih preoblikovali v ustrezno obliko za modeliranje. Problem smo rešili s pomočjo metode DemandForest in jo implementirali na različnih številih skupin. Poleg točne napovedi prodaje metoda vrne tudi napovedni interval. Ugotovili smo, da metoda napoveduje bolje kakor benchmark metoda.Companies nowadays have to deal with shorter product life cycles, which increases the need to properly forecast demand for new products. Forecasts allow them to make operational decisions, such as procurement and inventory control. The main purpose of the thesis is to forecast sales of new products in the FMCG sector. Firstly, we acquired access to appropriate data and transformed it so that it can be used in different machine learning models. We solved the problem by implementing a method called DemandForest. Besides point forecasts the method can establish prediction intervals. We evaluated DemandForest multiple times with different number of clusters. On the basis of our experimental results, we discovered that DemandForest provides more accurate results than the benchmark method