Optimizing Retail Inventory Management Through Time Series Analysis

Ziyan Fu

doi:10.62051/dy4kaf37

Authors

Ziyan Fu

DOI:

https://doi.org/10.62051/dy4kaf37

Keywords:

Time Series Analysis; ARIMA Model; Seasonal Decomposition; Forecasting Model Optimization.

Abstract

The purpose of this study is to investigate the application of time series analysis in the management of retail inventory, with a particular emphasis on the precision of forecasting as well as the applicability of ARIMA models and seasonal decomposition techniques. This study aimed to evaluate these models' effectiveness in improving the scientific and precise nature of inventory management. This was accomplished by using legitimate retail data. According to the findings, the ARIMA model is effective in managing data that is stable or does not exhibit seasonal patterns. On the other hand, the seasonal decomposition technique demonstrates strong forecasting accuracy in managing data that exhibits seasonal fluctuations. Not only does this research improve the theoretical implementation of time series analysis in the retail sector, but it also provides retailers with practical strategies for mitigating economic losses that are caused by inventory imbalance. When it comes to the management of retail inventory, the findings of the research are significant for improving both overall cost control and customer satisfaction.

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