International Journal Of Science, Technology & Management (IJSTM)
Vol. 5 No. 3 (2024): May 2024

Implementation of K-Means Clustering for Inventory Projection

Sitorus, Zulham (Unknown)
Syahputra, Irwan (Unknown)
Indra Angkat, Chairul (Unknown)
Sartika, Dewi (Unknown)

Article Info

Publish Date
30 May 2024

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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Journal Info
International Journal Of Science, Technology & Management (IJSTM)
Website

Abbrev

ijstm

Publisher

CV. Inara

Subject

Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

Description

International Journal of Science, Technology & Management (IJSTM) is an international scholarly refereed research journal which aims to promote the theory and practice of science, technology, innovation, engineering and ...