Ensuring the accuracy and reliability of glucose test strips is essential for effective diabetes management, as even minor deviations in enzyme stability can lead to critical diagnostic errors. This study presents a Six Sigma–based approach to optimize the manufacturing parameters of glucose test strips, with a focus on enhancing the stability of Glucose Dehydrogenase–Flavin Adenine Dinucleotide (GDH-FAD) enzymes. The project identifies key process variables—such as post-oven drying temperature, cooling rate, and process uniformity—that significantly influence enzyme activity and test accuracy.
Using the DMAIC (Define, Measure, Analyze, Improve, Control) framework, the research systematically investigates enzyme instability through statistical tools including ANOVA, Pareto charts, fishbone diagrams, and control charts. Root causes of performance variability are identified, enabling the implementation of process improvements that reduce scrap, rework, and cost while maintaining enzyme stability across production batches.
The optimized process parameters and validated Six Sigma model provide a structured pathway toward sustained process control and improved product quality. Ultimately, this work contributes to the medical device manufacturing sector by establishing data-driven methods for enhancing enzyme stability and ensuring the reliability of glucose measurement systems, supporting safer and more accurate patient outcomes.