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Armaya, Jhea
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Multiple Linear Regression Method in Product Stock Prediction at PT. Kartika Mandiri Abadi Armaya, Jhea; Ramadhan, Muhammad Hari
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.280

Abstract

Inaccurate inventory management often leads to stock shortages or surpluses, which impact operational efficiency and customer satisfaction in medium-sized distribution companies such as PT. Kartika Mandiri Abadi. This study aims to develop a website-based stock prediction system using the Multiple Linear Regression (MLR) method to produce more accurate stock estimates and support managerial decision-making. The research methods included collecting historical sales and product inventory data, designing a web-based system using the Unified Modeling Language (UML) model, implementing MLR to predict inventory levels based on independent variables such as monthly sales and initial inventory, and testing the functionality and accuracy of the system. The results show that the MLR-based inventory prediction system is capable of producing more stable and consistent estimates compared to manual methods, reducing the risk of stock shortages or surpluses, and facilitating management in inventory planning and distribution scheduling. The implementation of a web-based system provides real-time access, data visualization, and structured reports that support faster and data-driven decision making. These findings emphasize the importance of integrating statistical methods with information technology to improve operational efficiency and inventory planning in distribution companies. This research also opens up opportunities for further development by incorporating external variables or hybrid approaches to improve prediction accuracy in dynamic market conditions.
Co-Authors Ramadhan, Muhammad Hari