Optimizing Sales and Inventory Management with Machine Learning: Applications of Neural Networks and Random Forests

Tantik Sumarlin; Nanik Qosidah

doi:10.27824/jmi.v3i2.35

Authors

Tantik Sumarlin Universitas Sains Dan Teknologi Komputer, Semarang, Indonesia, 50192 https://orcid.org/0009-0009-2372-0450
Nanik Qosidah Universitas Sains dan Teknologi Komputer, Semarang, Indonesia, 50192 https://orcid.org/0009-0002-5555-0926

DOI:

https://doi.org/10.27824/jmi.v3i2.35

Keywords:

Sales Prediction, Inventory Management, Machine Learning, Operational Efficiency, Data Quality

Abstract

This study examines the implementation of machine learning (ML) in predicting sales performance and managing inventory in manufacturing companies. The results indicate that ML, particularly neural networks, significantly improves sales prediction accuracy, reaching 92.5%, while enhancing operational efficiency through a 20% reduction in storage costs and faster distribution. The study also highlights the positive impact of ML on profitability, with companies reporting a 15% increase in net profits after adopting ML. However, challenges such as data quality and system integration remain, requiring companies to focus on data cleansing and technological infrastructure. The results suggest that ML can provide substantial long-term benefits in improving business operations, especially when quality data and appropriate algorithms are used, making it a crucial tool for companies aiming to enhance competitiveness in the digital era.

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Optimizing Sales and Inventory Management with Machine Learning: Applications of Neural Networks and Random Forests

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