Overview
Consider your audience when writing this. Imagine a prospective industry partner is on our website and they want to know immediately what your research is about and how it might be relevant to their company. An abstract might be a useful place to start but should be edited to be succinct and written in plain English.
This project represents a collaborative research effort involving the University of Technology Sydney (UTS), Swinburne University of Technology (SUT), Queensland University of Technology (QUT), and Cook Medical. The primary objective is to validate a novel procedure model framework specifically developed for optimizing quality control processes in advanced manufacturing environments. The focus is on aortic stent production, comparing traditional human-based inspection methodologies against advanced 2D camera-based detection systems.
The methodological framework for the project is structured around the Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control) process. In the DEFINE phase, the team establishes validation criteria, experimental design, and comprehensive metrics for assessing performance. The MEASURE phase involves detailed Measurement Systems Analysis (MSA) to ensure data accuracy, followed by thorough data collection from both human inspectors and camera systems. The ANALYZE phase incorporates extensive statistical evaluations using SPSS, including t-tests, ANOVA, and chi-square tests to rigorously compare both inspection methods. The IMPROVE phase focuses on refining the procedure model based on statistical findings and conducting a detailed cost-benefit analysis, covering direct and indirect costs and benefits. Finally, the CONTROL phase ensures sustainability through robust documentation, standard operating procedures (SOPs), and ongoing monitoring protocols.
The collected data and analysed throughout this subproject will include defect-related data (type, frequency, severity, and root causes), comprehensive inspection data (pass/fail outcomes, inspector qualifications, and inspection duration), detailed rework data (activities, resources, and costs), waste measurement metrics, operational performance insights (cycle times, throughput, downtime incidents), economic assessments (ROI calculations), human factors (inspector proficiency and safety incidents), and structured datasets for statistical validation.
Outcomes
The anticipated outcomes of this research are multifaceted. First, the validation of the proposed framework is expected to significantly enhance defect detection accuracy, reduce manufacturing costs through minimized rework and scrap, and improve overall process consistency and efficiency. This will result in clear and actionable insights for Cook Medical, supporting informed decision-making for quality control strategies and technological investments.
Secondly, the project will deliver comprehensive resource planning guidelines, detailed ROI calculations, and implementation recommendations tailored specifically to Cook Medical’s operational environment. These outcomes are intended to not only directly benefit Cook Medical but also set new benchmarks and provide practical guidelines for quality management in the wider medical device industry, promoting broader adoption of data-driven, optimized quality control practices.
Associated Researchers