Looking into Raw Material Costs Through Machine Learning to Improve Efficiency

• DOI: 10.20944/preprints202502.1861.v1
• Publication Date: 2025-02-27T05:19:51Z
• Authors: Adruce Bin Khairudin; Lee Jia Fong; Wadthanak Khongsuwan; Teh Yu Xiang; Bryan Thong Khai ...; Noor Ul Amin

This document is meant to show how machine learning can analyze the raw material costs that are affecting business operations of small-to-medium enterprises. Currently, SMEs face challenge rising costs, cost inefficiencies and lack predictive insights into management, which create problems for trying operate a small smoothly in Malaysia. Machine help find solutions those operational through using regression forecast materials general. To do that, three models used Linear Regression (LR), Decision Tree (DTR) Random Forest (RFR). By analyzing dataset by prices, it shown DTR outperforms other forecasting prices general with an R2-score 0.91, followed RFR at 0.87, LR 0.79. With these findings mind, could serve as enhancing adapt factors implementations management strategies, helping decision-making ensure competitiveness profit sustainability, framework serving solution both practical scalable offer opportunities respond market shifts mitigate risk.