Measuring Digital Lean Success: A Framework Using Core Process Metrics for Apparel Manufacturing

Year 2025, Volume: 35 Issue: 3, 196 - 209

Bülent Koç , Selin Hanife Eryürük

Abstract

This study investigates the impact of digitalization on Lean manufacturing within the apparel industry, emphasizing the integration of key performance metrics through technologies such as the Internet of Things (IoT), cloud computing, and data analytics. It highlights critical indicators including Lead Time, First Time Yield, Overall Equipment Effectiveness (OEE), On-Time Delivery Efficiency, and Total Cost of Product, showcasing their increasing relevance in a digital context. The research demonstrates that real-time data collection and analysis empower manufacturers to make data-driven decisions, improve operational visibility, and foster continuous improvement. Findings reveal that the integration of digital tools with Lean practices can lead to a reduction in lead times of up to 20%, a decrease in unit costs by 30%, and an enhancement of OEE by 35%. Additionally, companies report improvements in on-time delivery ranging from 10% to over 30% and increases in First Pass Yield between 20% and 50%. Ultimately, leveraging these metrics within a digital Lean framework promotes waste reduction, enhances product quality, and increases agility in responding to market challenges.

Keywords

Apparel Industry , Lean Metrics , Digitalization , Digital Lean Manufacturing , Process Monitoring , Continuous Improvement

Ethical Statement

The research related to human use has been complied with all the relevant national regulations, institutional policies and in accordance with the tenets of the Helsinki Declaration, and has been approved by the author's institutional review board or equivalent committee.

Supporting Institution

Istanbul Technical University Scientific Research Project Unit

Project Number

project MDK-2022-44123.

Thanks

The authors gratefully acknowledge the financial support provided by the Istanbul Technical University Scientific Research Project Unit under project MDK-2022-44123. We extend our sincere appreciation to ITM Tech Soft Company for their invaluable support and for providing the essential facilities that made this research possible.

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