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Abstract

In the dynamic landscape of the fashion industry, accurate sales forecasting is a challenge for small and medium-sized enterprises (SMEs). This challenge is compounded by rapidly changing consumer interests and short product life cycles. To address these complexities, SMEs employ innovative strategies to optimize inventory management, sustainability efforts, and brand equity preservation. The analysis conducted in this study revealed that while Moving Averages are effective in managing percentage errors and show strengths during stable sales conditions, XGBoost reduces total and absolute quantity errors. The IF model synergistically combines these methods, frequently surpassing their performances. The results emphasize the potential benefits of integrating traditional and advanced techniques for more robust and accurate sales predictions. This study contributes to SMEs seeking to enhance their forecasting approaches across diverse product lines.

Paper Number

1551

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Aug 16th, 12:00 AM

Advanced Sales Prediction: Harnessing Machine Learning for Dynamic Fashion Insights

In the dynamic landscape of the fashion industry, accurate sales forecasting is a challenge for small and medium-sized enterprises (SMEs). This challenge is compounded by rapidly changing consumer interests and short product life cycles. To address these complexities, SMEs employ innovative strategies to optimize inventory management, sustainability efforts, and brand equity preservation. The analysis conducted in this study revealed that while Moving Averages are effective in managing percentage errors and show strengths during stable sales conditions, XGBoost reduces total and absolute quantity errors. The IF model synergistically combines these methods, frequently surpassing their performances. The results emphasize the potential benefits of integrating traditional and advanced techniques for more robust and accurate sales predictions. This study contributes to SMEs seeking to enhance their forecasting approaches across diverse product lines.

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