Associate Professor, at Tianjin University, China.
Yanfen Shang is an Associate Professor in the Department of Industrial Engineering at Tianjin University. With a Ph.D. in Industrial Engineering & Logistical Management from the Hong Kong University of Science & Technology, she has dedicated her career to quality control and statistical process management. Her expertise spans both theoretical advancements and practical applications, leading to significant contributions in industrial engineering. Prof. Shang has authored over 20 SCI-indexed journal articles and co-authored a book chapter. She has led multiple research projects funded by the National Natural Science Foundation of China (NSFC). As a respected scholar, Prof. Shang plays a crucial role in improving industrial quality management practices. 📊
Profile
🎓 Education
Prof. Shang earned her Ph.D. in Industrial Engineering & Logistical Management from the Hong Kong University of Science & Technology (2011). Prior to that, she completed her Master’s degree in Management Science & Engineering (2007) and Bachelor’s degree in Industrial Engineering (2004) from Tianjin University. Her academic journey reflects a consistent focus on optimizing industrial systems and processes, laying the foundation for her future research and teaching excellence. 🏫
💼 Professional Experience
With extensive teaching and research experience, Prof. Shang has been an Associate Professor at Tianjin University for over a decade. She has led several high-impact projects funded by prestigious institutions like the NSFC. She also contributes as a reviewer for leading journals, including Technometrics and Computers & Industrial Engineering. Additionally, she actively engages in industrial consultancy, applying her research insights to real-world challenges in quality management and statistical process control. 🧑🏫
🔍 Research Interests
Prof. Shang’s research centers on Quality Control, Statistical Process Control (SPC), and process improvement strategies. She specializes in developing innovative SPC techniques for service industries and multistage processes. Her work addresses critical issues in handling binary and categorical data, with applications ranging from healthcare to manufacturing. Her current research explores the integration of machine learning into SPC, enhancing predictive accuracy and process efficiency. 📈
🏆 Awards and Achievements
Prof. Shang’s contributions have been recognized through various accolades. She has successfully led three NSFC-funded projects and received the Elite Scholar Award from Tianjin University. Her work has earned accolades at international conferences, reflecting her impact in the field of industrial engineering. She has been consistently acknowledged for her innovative solutions in statistical process management, furthering the advancement of quality control methodologies. 🏅
📚 Top Noted Publications
Prof. Shang has an extensive publication record, including over 20 SCI-indexed journal articles. Notable works include:
- Shang, Y., Wang, K., & Tsung, F. (2009).
Title: An improved run-to-run process control scheme for categorical observations with misclassification errors
Journal: Quality and Reliability Engineering International, 25(4), 397-407.
- Shang, Y., Tsung, F., & Zou, C. (2011).
Title: Profile Monitoring with Binary Data and Random Predictors
Journal: Journal of Quality Technology, 43(3), 196-208.
- He, Z., Wang, Z., & Shang, Y. (2016).
Title: A control scheme for autocorrelated bivariate binomial data
Journal: Computers & Industrial Engineering, 98, 350-359.
- Song, L., He, S., Zhou, P., & Shang, Y. (2022).
Title: Empirical likelihood ratio charts for profiles with attribute data and random predictors in the presence of within profile correlation
Journal: Quality and Reliability Engineering International, 38(1), 153-173.
Conclusion
Prof. Yanfen Shang is a strong candidate for the Best Researcher Award due to her significant contributions to quality management and statistical process control. Her impressive research output, leadership in funded projects, and active participation in the academic community make her highly deserving of this recognition. With continued emphasis on international collaboration and industry engagement, her influence in the field could reach even greater heights.