Integrated ABC-FSN Analysis of Inventory Classification and Demand Forecasting: A Case Study of Welding Industrial Engineering Program Warehouse

Natdhanai Supattananon; Naratip Supattananon; Nopporn Deethum; Satayu Srichinlert; Jirapat Wanponthong; Rattanaphon Saiyakit; Raknoi Akararungruangkul

Authors

Natdhanai Supattananon Faculty of Business Administration, Northeastern University
Naratip Supattananon Faculty of Technical Education, Rajamangala University of Technology Isan Khon Kaen Campus
Nopporn Deethum Faculty of Business Administration, Northeastern University
Satayu Srichinlert Faculty of Technical Education, Rajamangala University of Technology Isan Khon Kaen Campus
Jirapat Wanponthong Faculty of Technical Education, Rajamangala University of Technology Isan Khon Kaen Campus
Rattanaphon Saiyakit Faculty of Technical Education, Rajamangala University of Technology Isan Khon Kaen Campus
Raknoi Akararungruangkul Faculty of Engineering, Khon Kaen University

Keywords:

ABC Analysis, FSN Analysis, Demand forecasting, Inventory classification

Abstract

This study employed a quantitative case-study approach to improve inventory management in the Welding Industrial Engineering Programme warehouse, Faculty of Industrial Education, Rajamangala University of Technology Isan. The study analyzed the complete inventory population of 17 stock-keeping units (SKUs). Purchasing records from 14 academic semesters (2019/1–2025/2) were used for ABC Analysis, while inventory withdrawal records from six semesters (2023/1–2025/2) were used for FSN Analysis. The integrated ABC-FSN classification identified 6 Class A, 4 Fast-moving, 9 Slow-moving, and 4 Non-moving items. The Grinding wheel 4"×6" mm was selected as an illustrative AF-class forecasting case. Moving Average, Single Exponential Smoothing, and Holt-Winters' Multiplicative Exponential Smoothing were evaluated using 14 semester-level demand observations. Holt-Winters achieved the lowest MAPE of 35.06%, compared with 47.53% for Moving Average and 51.41% for Single Exponential Smoothing, indicating comparatively better performance among the evaluated methods. The resulting safety stock and reorder point were 62 and 147 units, respectively. However, the forecasting results are subject to limitations arising from the single-item forecasting scope and the relatively short demand series.

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