Sales forecasting of fast moving consumer goods: Comparison of traditional time series techniques with an artificial neural network model approach

Demand forecasting is a crucial part of managing any supply chain network, since inaccurate forecasting often leads to inventory mismanagement which in-turn amounts to big losses for companies.Though most of the companies have some forecasting techniques in place, it is equally important to know if the forecasting techniques being used are best suited for their requirements. This thesis provides a comparative study of traditional time series methods namely: Holt Winters, Exponential smoothing and ARIMA with an artificial neural network model in order to forecast inventory levels of multiple SKUs at the last mile of the supply chain, which is a retail store. Comparison is performed using various forecasting accuracy measures. The study provides insights to the company that manufactures number of SKUs, as to which forecasting techniques would best suit their need for managing inventory at the store level and why. It also sheds light on the factors that affect the performance of models, for example sequence of events linked to sales like promotions, holiday season, weekends etc. or the granularity at which the forecasting is being done, whether it is weekly or monthly. Modelling and analysis was performed in R programming environment. The data used for this study is real world point of sales data provided by a leading fast moving consumer goods company. It is an industry based research to help the company improve their demand forecasting techniques.Mémoire de master [120] : ingénieur civil en informatique, Université catholique de Louvain, 2015La diffusion de ce mémoire n'est pas autorisée par l'institutio