Degree

Master of Science (MS)

Department

Systems Technology

Document Type

Thesis

Abstract

This study focuses on integrating simulation modeling with Lean Six Sigma (LSS) within the DMAIC (Define, Measure, Analyze, Improve, Control) framework for process optimization in textile manufacturing industry. Although traditional LSS framework such as Value Stream Mapping (VSM) and Root Cause Analysis are effective in identifying waste, they mainly rely on static and historical data which make their capability limited for real analysis or predictive decision making. As a result, many textile manufacturing processes still face challenges such as production delays, excessive work-in-process (WIP), high cycle time, and inefficient resource utilization. To address this issue, this research proposes a simulation-based LSS framework that enables virtual analysis and evaluation of improvement strategies before real implementation. A detailed case study of a woven shirt sewing line is done, where VSM is used to evaluate the current system and ARENA simulation is applied to model and evaluate process performance. Two improvement models including line balancing, work sharing, and elimination of Lean wastes are implemented and tested through simulation. The results show that production increases from 1087 to 1166 pieces per shift, while line efficiency improves from 64.61% to 76.48%, with a significant reduction of bottlenecks from 6 to 2 and a 44.24% decrease in process variation, indicating a much more stable and balanced production system. Overall, the study shows that integrating simulation with LSS improves decision-making, increases productivity, and supports sustainable and efficient textile manufacturing systems.

Date

2-6-2026

DOI

https://proquest.com/docview/3347813125

First Committee Chair

Jim Lee

First Committee Member

Shelton Houston

Second Committee Member

Zixian Zhu

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