Optimizing Quality System Operations: Exploring the Role of Data Automation in Business Efficiency and Sustainability

Abstract

Data analysis and business intelligence are important in today's enterprises. Stryker's Trauma and Extremities Quality Systems Division faces challenges in managing and analyzing vast volumes of data from diverse fields. Manual labor and time-consuming procedures stymie efficiency. The project intends to automate these procedures through the use of tools and services such as Power BI, Power Automate, and Azure Data Factory. These automation tools boost data management, accessibility, and reporting capabilities. The thesis investigates the use of these technologies to improve project tracking and data management within the Quality System Automation Project, thereby facilitating informed decision-making. Power BI, Power Automate, Power Apps, Azure Data Factory, and Data Lake are among the current automation tools used in the project. These solutions allow for the automation of recurrent tasks and the streamlining of operations, which leads to enhanced efficiency and resource optimization. Other challenge includes selection of right tools and methods for the mitigation of these problematic area from the big pool of resources. This project is an umbrella project involving several small and big projects under it, catering to the needs of whole Quality Systems which aims at benchmarking certain processes across Stryker and ultimately give its contribution in the organizational transformation. Many of such projects involves the use of modern day tools such as Power BI, Power Automate, Power Apps, Azure Data Factory, Data Lake that are extensively used in Workspaces to make many day to day recurring tasks more automated and save the precious resource of all time that is time which can be further used to make more informed and creative decisions. The application of automation tools, such as Power BI and other tools, to improve project tracking and data management within the Quality System Automation Project is examined in this thesis. tracking and project status visualization are made possible via the Project Tracker Dashboard. Other automation techniques are used to simplify operations and increase data accessibility and accuracy. In The Divisional Database project consolidated Data Lake is being created, and direct connections to live data sources are being made. The results demonstrate how these technologies improve data management and enable reporting and data-driven decision-making.