Academic Position

• KAIST Business School, Korea Advanced Institute of Science and Technology

  • Assistant Professor, September 2012 - February 2020

  • Associate Professor (with Tenure), March 2020 - Present 

Education

• Judge Business School, University of Cambridge, Ph.D. in Management Science.

• Judge Business School, University of Cambridge, M.Phil. in Management Science.

• Imperial College London, B.Eng, M.Eng. 

Research Interests

• Behavioral operations management

• Judgmental forecasting

• Decision making

• Management science

Publications

• Park, Y. S., J. Na & Y. S. Lee (2024) "Effect of customer concentration on firm's operating performance during the COVID-19 pandemic." International Journal of Production Research. 

• Koo, M. S., Y. S. Lee & M. Seifert (2023) "Investigating laypeople's short-and long-term forecasts of COVID-19 infection cycles." International Journal of Forecasting. 

• Kim, H. Y., Y. S. Lee and D. B. Jun. (2020) Individual vs. group: Advice taking in judgmental forecasting adjustments. Journal of Behavioral Decision Making. 33(3). 287-303.  

• Kim, H. Y., Y. S. Lee and D. B. Jun (2019) "The effect of relative performance feedback on judgmental forecasting accuracy." Management Decision. 57(7). 1695-1711. 

• Lee, Y. S., D. Ribbink and S. Eckerd (2018) "Effectiveness of bonus and penalty incentive contracts in supply chain exchanges: Does national culture matter?" Journal of Operations Management. 62. 59-74. 

• Kim, H., Y. S. Lee and K. S. Park (2018) "The psychology of queuing for self-service: Reciprocity and social pressure." Administrative Sciences. 8(4). 75. 

  • Lee, Y. S., Y. W. Seo and E. Siemsen (2018) "Running behavioral operations experiments using Amazon's Mechanical Turk." Production & Operations Management. 27(5). 973-989.

• Lee, Y. S. and E. Siemsen (2017) "Task decomposition and newsvendor decision making." Management Science. 63(10). 3225-3245. 

• Kang, H., B. W. Kim and Y. S. Lee (2017) "Supplier's corporate ability and consumer evaluation of a manufacturer." International Journal of Services and Operations Management. 27(1). 19-34.

• Lee, Y. S. (2014) "Management of a periodic-review inventory system using Bayesian Model Averaging when new marketing efforts are made." International Journal of Production Economics. 158. 278-289.

• Lee, Y. S. (2014) "A semi-parametric approach for estimating critical fractiles under autocorrelated demand." European Journal of Operational Research. 234(1). 163-173. 

• Lee, Y. S. and S. Scholtes (2014) "Empirical prediction intervals revisited." International Journal of Forecasting. 30(2). 217-234. 

Domestic

• 구문수, 이윤신, 박영수 (2023) "국가 간 무역분쟁에 따른 주식시장의 반응: 한일 무역분쟁 사례를 중심으로." 학국생산관리학회지. 34(2). 243-257. 

• 정동기, 이윤신, 김효영 (2018) "집단 내 개인의 영향력 및 책임감이 주관적 예측 조정에 미치는 영향." 한국생산관리학회지. 29(3). 299-329. 

Others

• Lee, J.Y., K. Kim, Y.S. Lee, H.Y. Kim, E. J. Nam, S. Kim, S. W. Kim and J. W. Kim (2017) "Treatment preferences for routine lymphadenectomy versus no lymphadenectomy in early-stage endometrial cancer." Annals of Surgical Oncology. 24(5). 1336-1342. 

• Lee, J.Y., K. Kim, Y.S. Lee, H.Y. Kim, E. J. Nam, S. Kim, S. W. Kim and J. W. Kim (2016) "Treatment preferences of advanced ovarian cancer patients for adding bevacizumab to first-line therapy." Gynecologic Oncology. 143(3). 622-627.

Working Papers

• "Aggregating Judgmental Demand Forecasts in Environments with Structural Breaks" with Matthias Seifert and Shijith Kumar. 

• "Counteracting Partner Errors: Mechanism of Task Division for Interdependent Operational Tasks" with Young Soo Park and Enno Siemsen

• "Predicting the Uncertain Future: Construal Level and Prediction Accuracy" with Moon Su Koo and Chan Jean Lee

• "Guiding Supervisors in AI-Enabled Forecasting", with Naghmeh Khosrowabadi and Kai Hoberg

• "Stock Market Reactions to Supply Chain Disruptions and Recovery from the 2022 Shanghai Lockdown," with Kyunghee Song and Moon Su Koo

Teaching

• Operations Strategy and Supply Chain Management (MBA), KAIST Business School, Fall 2012 ~

• Service Management (MBA), KAIST Business School, Spring 2012 ~

• Quantitative Management (MBA), KAIST Business School, Fall 2014 ~

• Management Statistical Analysis (MBA), KAIST Business School, Spring 2018 ~

• Business Analytics (MBA), KAIST Business School, Spring 2019 ~

• Behavioral Operations Management (MS/PhD), KAIST Business School, Fall 2013 ~

Awards and Fellowships

• 우수 교육상, 카이스트 경영대학, 2019. 

• 유민 이상문 신진생산관리학자상, 생산관리학회, 2019.

• 개교기념표창 학술상, 카이스트, 2019.

• 우수연구상, 카이스트 경영대학, 2018.

• Economic and Social Research Council (ESRC) Fellowship, 2011-2012.

• Finalist, INFORMS Case Competition, 2010. 

• Mellon Sawyer Dissertation Fellowship, 2009-2010. 

• Laing O'Rourke Doctoral Scholarship, 2007-2010.

• Governor's Prize, Imperial College London, 2006. 

Students

• 정동기 (MS) 한국토지신탁

• 김한영 (MS) NICE신용평가

• 이다빈 (MS) EY한영

• 정하준 (MS)  전문연구요원

• 송경희 (MS) EY한영

• 김지인 (current MS) 

• 김효영 (PhD) 차의과대학교 데이터 경영학과 조교수

• 박영수 (PhD) 국민대학교 경영학과 조교수 

• 구문수 (current PhD)

• 박찬희 (current PhD)

• Rachel Tyree (current PhD) 

연구 소개

For more information, send an email to yunshin@kaist.ac.kr

Empirical Operations Management

This is the field of operations management that applies empirical research methods. It involves collecting and analyzing empirical data (either collected from an industry or database) to gain insights into the various aspects of operational processes. Here, the aim is to develop a regression model to understand the relationships between different variables and make evidence-based decisions to improve operational performance. It can help bridge the gap between theory and practice by providing empirical evidence based on data from the real world. 

Examples of Research Questions

During the COVID-19 Pandemic, is it better to diversify/concentrate a customer base to build supply chain resilience? 

How do stock markets react to supply chain disruptions such as the COVID-19 lockdown? 

Behavioral Operations Management

This is a subset of empirical operations management where the focus is on understanding and influencing the behavior of individuals and groups involved in operational processes. It considers the behavioral aspects of operations, including decision-making, motivation, teamwork and coordination. For example, we can mathematically model and predict how groups make decisions and interact within operational settings. If  player A in a group faces inequality aversion (a preference to avoid the inequality in profits), we can write down his utility function as 

using its own profit and its partner's profit. We can then test such a model in a specific operations setting using a behavioral lab study. Based on the findings, we can develop strategies to improve performance and decision-making processes. 

Examples of Research Questions

How can we effectively divide tasks in the operational process? 

When can we improve forecasting performance when forecasters rely on AI? 

Photos