Operation Engineering-Data Analyst & Manufacturing System Manager, Kulicke & Soffa
Wint joined Kulicke & Soffa in the year 2009 and currently serves as the Manager of Equipment Operation Engineering. In her role, she leads and manages a team of Data Analysts and Data Scientists. Her primary responsibilities involve overseeing Smart Manufacturing Digital Transformation and Data Analytics to enable business data insights for informed decision-making and strategic planning.
With more than 15 years of experience in semiconductor manufacturing, spanning both front-end and back-end markets, Wint possesses extensive technical expertise in Systems Integration in Manufacturing, Data-Driven Solutions, and Change Management. Over the past 5 years, her team successfully implemented an in-house developed Manufacturing Execution System (MES) across all major business lines, enhancing operational productivity and efficiency through improved data connectivity and streamlining. Furthermore, her team is actively engaged in the development of advanced data analytical tools and solutions such as Machine Learning algorithms, aimed at improving product quality, accelerating time to market, and reducing manufacturing cost.
Wint holds a Degree in Electrical Engineering from the National University of Singapore and a Diploma in Mechatronics from Nanyang Polytechnic.
Presentation Title: Harnessing AI and Machine Learning in Operations for Quality and Efficiency Improvement
Kulicke & Soffa (K&S) strategically leverages a Digital Transformation Framework and embarks on an industrial 4.0 journey by integrating machine learning into Smart Manufacturing. This presentation delves into how AI and machine learning algorithms in predictive analytics models enhance the quality of manufacturing operations, improving overall processes. By refining existing machine learning models, K&S enhances its infrastructure to support automatic machine learning architecture and prepare it for upcoming data analytics advancements. The application of Big Data analytic methodology in real-time process data contributes to cost optimization and yield improvement. Effective AI analytics tactics lead to improved overall quality and efficiency, enabling the production of top-notch quality products and ultimately enhancing customer satisfaction