Abstract

In the pursuit of quality excellence within high-speed manufacturing, this study applies Six Sigma methodology to minimize bottle height variation in the production line of XYZ Company. The existing process exhibited noticeable deviations in bottle height, contributing to critical quality issues such as improper capping, fluid filling inaccuracies, and instability ultimately leading to increased rework, customer dissatisfaction, and loss of revenue. This research targets a reduction of height variation through a data-driven approach grounded in the DMAIC framework.

During the Measure phase, baseline performance was assessed using historical production data and statistical tools to evaluate current process behavior. The analysis revealed a high degree of variation and significant misalignment with quality standards, indicating that the existing process is incapable of consistently meeting specifications. The distribution of data also exhibited asymmetry, suggesting systemic issues affecting process stability. These insights emphasize the urgent need for process improvements to reduce variability and enhance manufacturing precision.

This project serves as a foundational benchmark in developing sustainable quality interventions in subsequent phases. The results underscore the critical role of data validation and process capability analysis in identifying root causes of variation. By reducing height inconsistency, the study not only aims to elevate first-pass yield and operational efficiency but also supports long-term customer satisfaction and business sustainability.

Keywords

Six Sigma, Process Capability, DMAIC, Manufacturing Quality, Statistical Process Control