A Machine Learning-Enhanced Software Framework for Intelligent Inventory Monitoring and Demand Forecasting of Perishable Goods: Evidence from Developing Economy SMEs
Domain:
agricultureRecord type:
softwarepaper
Creator:
PauAdaMoh
Publisher:
Int
Host:
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.
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