Comparative analysis of the least squares method and double moving average technique for forecasting product inventory
Abstract
The cosmetics industry necessitates efficient inventory management to balance customer demand with stock control. This case study explores how Liza Cosmetics Shop optimized inventory for Lip Cream Implora 01, a popular product, using data-driven forecasting techniques. Traditional trend-based methods often resulted in inaccurate forecasts. This study proposed implementing the SDLC Waterfall Model to apply two forecasting techniques: Least Squares and Double Moving Average. Historical sales data (April 2021 - June 2022) was analyzed to identify demand patterns, seasonality, and trends. The Least Squares method was chosen for its suitability in capturing stable, linear relationships between sales and time, while the Double Moving Average method catered to data exhibiting both long-term trends and short-term fluctuations. Rigorous testing using white-box and black-box methods ensured the accurate functionality and system behavior of the implemented models. The Mean Absolute Percentage Error (MAPE) determined the method best suited for predicting July 2022 demand. This case study contributes insights into data-driven inventory management in cosmetics, highlighting benefits such as optimized stock levels, reduced costs, and enhanced customer satisfaction through improved demand fulfillment. This studys’ limitations including unforeseen marketing campaigns and economic fluctuations impacting forecasts were acknowledged. Despite these challenges, the study emphasizes the potential of data-driven techniques to optimize inventory management and meet customer demands effectively.
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