JOURNAL ARTICLE
Enhancing Efficiency and Effectiveness in MRO Inventory Management Using Lean Six Sigma.
Published In: IUP Journal of Operations Management, 2025, v. 24, n. 1. P. 19 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Gomaa, Attia Hussien 3 of 3
Abstract
Effective Maintenance, Repair, and Operations (MRO) inventory management is vital for sustaining plant productivity and minimizing process downtime. Achieving efficiency in this area requires robust practices supported by continuous improvement methodologies. Lean Six Sigma (LSS) integrates Lean's focus on waste elimination--such as overstocking and delays--with Six Sigma's emphasis on reducing variability and errors, offering a powerful approach to process optimization. This study applies key LSS tools, including DMAIC, process mapping, Pareto analysis, value stream mapping, Kanban, and root cause analysis, to streamline MRO inventory management. The study proposes a unified LSS framework for continuous improvement in MRO inventory processes. The findings demonstrate that the framework significantly reduces order fulfillment times, material handling, and inventory costs, while enhancing customer service, operational efficiency, and process effectiveness. Additionally, it strengthens supply chain performance, improves plant productivity, and minimizes process downtime, delivering measurable advancements in maintenance efficiency and effectiveness. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:IUP Journal of Operations Management. 2025/02, Vol. 24, Issue 1, p19
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0972-6888
- DOI:10.71329/IUPJOM/2025.24.1.19-55
- Accession Number:183770478
- Copyright Statement:Copyright of IUP Journal of Operations Management is the property of IUP Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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