Forecasting Fast-Moving Consumer Goods Sales with Time Series Analysis
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School of Business |
Bachelor's thesis
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Date
2025
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Tieto- ja palvelujohtaminen
Language
en
Pages
22 + 6
Series
Abstract
This thesis This thesis evaluates the effectiveness of SARIMA and SARIMAX models for short-term sales forecasting in the Fast-Moving Consumer Goods sector. The study investigates the potential of these time series models by using historical sales data aggregated at weekly level and integrating customer volume as an exogenous variable in the SARIMAX model. Extensive preprocessing steps ensured data quality, and stationarity was validated using the Augmented Dickey-Fuller test. Model performance was evaluated using the mean absolute error (MAE) and the root mean square error (RMSE). The results demonstrate that SARIMAX, incorporating external predictors, significantly outperforms SARIMA in capturing sales patterns and variability. The findings highlight the importance of external variables in improving model accuracy, providing practical implications for inventory management and strategic decision-making in dynamic markets.Description
Thesis advisor
Seppälä, TomiKeywords
sales forecasting, SARIMA, SARIMAX, FMCG