About

Professor Yael Grushka-Cockayne is the Senior Associate Dean for the Full-Time (Residential) MBA at the Darden School of Business, where she also holds the Landmark Communication Professorship of Business Administration. She serves as the Academic Co-director of the LaCross Institute for AI and is a Special Advisor to the Provost on AI. Her research and teaching activities focus on data science, forecasting, project management, and behavioral decision-making. Her work has been published in numerous academic and professional journals, and she is a regular speaker at international conferences in decision analysis, project management, and management science. Prof. Grushka-Cockayne is an award-winning educator, having received multiple honors including the Darden Morton Leadership Faculty Award, the University of Virginia's Mead-Colley Award, and the Darden Outstanding Faculty Award in 2013, 2022, and other recognitions. She teaches the core 'Decision Analysis' course, an elective on project management, and an elective on data science. She has led open enrollment courses such as 'Project Management for Executives' and has designed and delivered custom programs for organizations including AARP. Before her academic career, she worked as a marketing director in San Francisco for an Israeli ERP company and has served as a consultant to international firms in aerospace and pharmaceutical industries. She is a member of INFORMS, the Decision Analysis Society, the Operational Research Society, and the Project Management Institute, and she is an associate editor at Management Science and Operations Research.

Selected publications

• Coding Algorithms with AI

  SSRN Electronic Journal · 2025-01-01

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• Decision-making with Ordinal Ratings

  SSRN Electronic Journal · 2025-01-01

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• Project Management Versus Product Management

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
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• A stakeholder theory perspective for project management

  International Journal of Project Management · 2025-09-12 · 6 citations

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• Wielding Occam’s razor: Fast and frugal retail forecasting

  Journal of the Operational Research Society · 2024-11-01 · 8 citations

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• Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts

  Manufacturing & Service Operations Management · 2024-10-30 · 5 citations

  articleSenior author

  Problem definition: We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory, and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and postlaunch distributional forecasts. Methodology/results: Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A prelaunch forecast is an ensemble with equal weights on the candidate models’ forecasts, whereas a postlaunch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel approach tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For postlaunch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. Managerial implications: In a data-driven operations environment, our model can produce accurate forecasts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0359 .

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• Introduction to the special issue on judgment and decision research on the wisdom of the crowds.

  Decision · 2024-01-01 · 3 citations

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• Managerial Mental Accounting and Downstream Project Decisions

  Management Science · 2024-03-04 · 7 citations

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  Project leaders are responsible for planning, controlling, and revising projects. As a project unfolds, the leader evaluates the project’s progress by comparing ongoing costs and scope to a baseline plan and considers potential revisions. We offer a general model of managerial mental accounting, which includes loss aversion, reference point updating, and narrow framing, and examine how it impacts downstream decisions. Our model predicts insufficient adjustments of project scope and cost at revision, resulting in reduced financial profit. We show that the choice of measure to quantify the project progress—planned, actual, or earned—affects the updating of reference points, and hence the downstream decisions. Thus, progress measures could be wisely employed to mitigate insufficient adjustments. It turns out that measuring progress via planned scope is often advantageous, whereas utilizing earned value for cost is never advisable. This paper was accepted by David Simchi-Levi, behavioral economics and decision analysis. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02929 .

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• Machine-Moderated Judgmental Forecasting to Improve Prediction Accuracy

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
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• Data-Driven Schedule Risk Forecasting for Construction Mega-Projects

  SSRN Electronic Journal · 2023-01-01 · 2 citations

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Awards & honors

• Darden Morton Leadership Faculty Award (2011)
• University of Virginia's Mead-Colley Award (2012)
• Darden Outstanding Faculty Award (2013, 2022)
• Faculty Diversity Award (2013, 2018)
• University of Virginia All University Teaching Award (2015)