System Engineering and Productivity

System Engineering and Productivity

An Integrated Optimization of Spare Parts Inventory Using Machine-learning–based Demand Forecasting, Taking into Account Safety Stock and Dual Lead-time Supply

Articles in Press

Document Type : Research Paper

Author
Omid Veisi
Assistant Professor, Department of Industrial Engineering, Faculty of Engineering and Flight, Imam Ali University (AS), Tehran, Iran
10.22034/sep.2026.2074035.1409
Abstract
One of the persistent challenges in industry is achieving a balance between uninterrupted production and minimizing inventory-holding costs—an objective that traditional inventory management methods often fail to meet due to their weaknesses in forecasting irregular demand and overlooking operational constraints. In this study, we attempt to provide an integrated solution to this problem by combining accurate demand forecasting with mathematical modeling. A key strength of the proposed model is its flexibility in selecting the procurement method; that is, the system can choose the optimal option between regular purchasing and emergency purchasing based on prevailing conditions. In designing the model, real-world constraints—such as budget limits (for both critical and non-critical items) and warehouse capacity—are precisely incorporated, and the safety stock level dynamically adjusts according to demand fluctuations. The results obtained from implementing the model on data from 60 items indicate that moving away from one-dimensional policies and adopting a hybrid strategy leads to cost reduction and improved system stability, even under financial and space limitations.

Highlights

  • Developing an integrated framework combining machine learning-based demand forecasting (SARIMA) with mathematical optimization, considering dual lead times, budget constraints, and warehouse capacity.
  • Achieving a 19.3% reduction in total system costs by replacing traditional policies with a hybrid sourcing strategy and intelligent management of safety stocks.

Keywords
Spare Parts Inventory Management
Demand Forecasting
Machine Learning
Inventory Optimization
Dual Delivery Lead Time
Subjects

Copyright © Omid Veisi

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity

Articles in Press, Accepted Manuscript
Available Online from 28 January 2026

  • Receive Date 11 October 2025
  • Revise Date 04 January 2026
  • Accept Date 28 January 2026
  • First Publish Date 28 January 2026
  • Publish Date 28 January 2026