Implementation of K-Means Clustering for Inventory Projection

Zulham  Sitorus; Irwan  Syahputra; Chairul   Indra Angkat; Dewi  Sartika

doi:10.46729/ijstm.v5i3.856

Zulham Sitorus Faculty of Postgraduate Program, Panca Budi University of Development, Medan, Indonesia
Irwan Syahputra Faculty of Postgraduate Program, Panca Budi University of Development, Medan, Indonesia
Chairul Indra Angkat Faculty of Postgraduate Program, Panca Budi University of Development, Medan, Indonesia
Dewi Sartika Faculty of Postgraduate Program, Panca Budi University of Development, Medan, Indonesia

DOI: https://doi.org/10.46729/ijstm.v5i3.856

Keywords: Inventory forecasting, K-means clustering, demand patterns, supply chain management, data-driven decision making and optimization.

Abstract

Inventory forecasting is crucial in effective supply chain management and cost reduction. However, traditional forecasting techniques face significant challenges due to the complexity and variability of demand patterns. This study explores the use of K-means clustering, a data-driven approach that can improve inventory forecasting accuracy. By grouping inventory items based on their unique demand profiles, we can create personalized forecasting models for each cluster. This technique enhances demand estimation, helping businesses make informed decisions and optimize their inventory. Our research delves into the use of K-means clustering to identify patterns and similarities within historical demand data. This clustering process divides inventory items into groups with similar demand characteristics. By applying specific forecasting models to each cluster, we achieve greater precision in our predictions. The proposed methodology is rigorously evaluated using real-world inventory datasets, and the results demonstrate its significant superiority in forecasting accuracy compared to conventional non-clustered methods. This study offers compelling evidence and valuable insights for practitioners seeking to improve their inventory management practices through data-driven techniques.

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