التكامل الاقتصادي
Volume 13, Numéro 3, Pages 223-239
2025-09-30

Evaluating The Effectiveness Of Arima And Radial Basis Function Neural Network Models In Sales Forecasting For A Cheese Supply Chain



Authors : Ahmed Fouatih Mohamed . Kahoui Hacen .

Abstract

Sales Forecasting is pivotal for improving logistics and enhancing the efficiency of supply chain management. Various forecasting methodologies have been developed, each with its strengths and limitations. This study aims to evaluate and compare the performance of two demand forecasting approaches: the Autoregressive Integrated Moving Average (ARIMA) model and the Radial Basis Function Neural Network (RBFNN). The analysis is based on daily cheese sales data obtained from a cheese factory in Maghnia. Model performance is assessed using the Root Mean Square Error (RMSE) metric. The experimental results indicate that the ARIMA model slightly outperforms the RBFNN model in forecasting accuracy for this dataset, underscoring the potential superiority of statistical methods in certain contexts

Keywords

Supply chain management ; Sales Forecasting ; autoregressive integrated moving average (ARIMA) Models ; Radial Basis Function Neural Network (RBFNN).

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