Authors : Paul Conteh; Adamsay Turay; Mohamed Thoronka

Volume/Issue : Volume 11 - 2026, Issue 5 - May

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Google Scholar : https://tinyurl.com/2932np34

Scribd : https://tinyurl.com/5x6p8br3

DOI : https://doi.org/10.38124/ijisrt/26May367

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : In many developing countries, poor management of perishable goods causes significant economic and nutritional losses. For example, food waste rates in sub-Saharan Africa are over 50%. Most smart inventory systems are designed for large companies with plenty of data and strong infrastructure, so small and medium-sized businesses (SMEs) often have limited options. This study introduces a machine learning-based software framework for smarter inventory monitoring and demand forecasting of perishable goods, tailored for retail SMEs with limited resources. The framework was tested using real-world data from Sierra Leone. Five forecasting models were compared: Linear Regression (as a baseline), Random Forest (ultra-tuned), XGBoost (ultra-optimised), a Stacking Ensemble, and a Hybrid XGBoost-LSTM model.

Keywords : Perishable Goods; Demand Forecasting; Machine Learning; Inventory Management; XGBoost; Developing Economies; SMEs; Supply Chain Optimisation.

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