A Study on Determining the Appropriate Target Inventory Levels for Consignment Machining Tools in a Glass Manufacturing Plant

Chumpol Monthatipkul

doi:10.60027/iarj.2026.e289205

Authors

Chumpol Monthatipkul King’s Mongkut University of Technology Thonburi, Thailand https://orcid.org/0009-0005-3140-5638

DOI:

https://doi.org/10.60027/iarj.2026.e289205

Keywords:

Simulation, Target Inventory Level, Consignment Stock, Service Level, Pareto

Abstract

Background and Aims: In glass manufacturing, effective inventory control of consignment machining tools is critical due to their high value and the operational risk of stockouts. However, in practice, such control is often overlooked, leading to high inventory-related costs. This study addresses a gap in the literature by focusing on the development of a simulation-based framework for determining optimal target inventory levels for consignment machining tools—a topic with limited prior research in the glass manufacturing context. The research specifically aims to (1) classify inventory items using established techniques and (2) identify cost-effective inventory control parameters that maintain desired service performance under consignment conditions.

Methodology: The study applied the Pareto-based ABC classification to prioritize inventory items by consumption value, reflecting their operational criticality. Two representative items from Category A—with contrasting demand profiles—were selected for further analysis. A simulation model was developed under a periodic review system to determine optimal target inventory levels and review intervals, incorporating key real-world constraints such as urgent shipment costs due to stock-out. ABC was chosen for its proven effectiveness in prioritizing inventory management efforts, while simulation was used to address the system's complexity and demand variability, which are typical in machining tool consumption patterns.

Results: Simulation findings revealed that no universal formula could determine optimal settings; instead, parameters must be tailored to individual items. Frequent inventory reviews reduced safety stock needs but increased transportation costs, while less frequent reviews required more inventory to sustain high service levels. The simulation demonstrated that appropriate tuning of these parameters could yield measurable benefits—for example, reducing total inventory costs by up to 50% compared to the plant’s prior unmanaged system, while achieving over 95% service levels in critical tools.

Conclusion: Simulation is an essential tool for designing inventory policies in complex consignment contexts where demand variability and supplier ownership create unique challenges. The framework developed here is adaptable and can be applied to other manufacturing environments that rely on high-value consumables. Future research should extend the model to broader product sets or explore dynamic control policies responsive to real-time conditions.

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