Predicting Demand for MSME Products Using Artificial Neural Networks
(ANN) Based on Historical Sales Data

Predicting Demand for MSME Products Using Artificial Neural Networks
(ANN) Based on Historical Sales Data

Artificial Neural Network, Demand Forecasting, MSME, Sales Data Prediction, Supply Chain Optimization, Small Business Analytics

Accurate demand forecasting plays a crucial role in supporting inventory and sales strategies, particularly for Micro, Small, and Medium
Enterprises (MSMEs) that often face resource constraints. This study aims to develop a predictive model using Artificial Neural Networks (ANN)
to forecast product demand based on historical sales data. The ANN model is trained and evaluated using a structured experimental approach,
adjusting parameters such as the number of hidden layers, learning rate, and epochs to identify the best-performing architecture. Evaluation
metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) are used to measure model
performance. The results demonstrate that the ANN model is capable of capturing complex nonlinear relationships in multidimensional data and
producing accurate demand forecasts. The model particularly performs well in predicting demand trends for products in the Electronics and
Household categories. These findings provide valuable insights for MSME stakeholders in optimizing inventory planning and making data-driven
business decisions.

Les Endahti1,*
, Muhammad Shihab Faturahman2

https://ijiis.org/index.php/IJIIS/article/view/288/171

AMIK-YPAT Purwakarta, Indonesia

desember 2025

Fajar bagus W

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