Tiffany is an Assistant Professor at the Ivey Business School. Her research interests include applying predictive and prescriptive analytics to designing engaging and effective business analytics pedagogy. As well, she has worked on applications of optimization modeling to supply chain and healthcare management problems.
Tiffany earned her PhD and MASc in Applied Operations Research from the University of Waterloo, and a BASc in Industrial and Manufacturing Systems Engineering from the University of Windsor. Before pursuing her graduate degrees, she worked as an Industrial Engineer (EIT) at Chrysler Windsor Assembly Plant and Syncreon Automotive, where she implemented line balancing, facility layout and design initiatives, and championed kaizen events.
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Maclean, K.; Bayley, T., (Forthcoming), "That’s Incorrect and Let Me Tell You Why: A Scalable Assessment to Evaluate Higher Order Thinking Skills", INFORMS Transactions on Education
Abstract: We introduce a novel type of assessment that allows for efficient grading of higher order thinking skills. In this assessment, a student reviews and corrects a technical memo that has errors in its formulation or process. To overcome the grading challenges imposed by essay-type responses in large undergraduate courses, we provide a Visual Basic for Applications Excel tool for instructors, ensuring efficient grading of student submissions. We report our experience using this type of assessment in a multisection introductory business analytics course over several years and present survey-based evidence indicating that students perceive it to be clear and beneficial for learning. Supplemental Material: Data is available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials
Link(s) to publication:
http://dx.doi.org/10.1287/ited.2023.0020
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Wheatley, D.; Bayley, T.; Araghi, M., 2022, "Able Construction: A Spreadsheet Activity for Teaching Bayes’ Theorem", Springer Nature, December 3(1): 1 - 18.
Abstract: Using classroom activities to motivate the teaching and learning of Bayes’ theorem is not new. However, many of the textbook exercises and published simulations gloss over how the requisite probabilities are determined. In our case study, Able Construction is a fictional company hoping to exploit historical bidding data to inform its own bidding strategy on a municipal construction project. Unlike most other classroom activities, we challenge students to calculate the necessary probabilities directly from a given dataset. In our experience with implementing this case in introductory business analytics courses at the undergraduate- and graduate-level, we find that this spreadsheet activity gives students the opportunity to exercise their own judgement regarding data manipulation and definition of states of nature. This autonomy in analysis develops in students a deeper appreciation for practical skills required for possible analytics careers after graduation, and leads to engaging discussions of the applicability of Bayes’ theorem in practice.
Link(s) to publication:
https://link.springer.com/article/10.1007/s43069-021-00119-3#article-info
http://dx.doi.org/10.1007/s43069-021-00119-3
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Begen, M. A.; Bayley, T.; Rodrigues, F.; Barrett, D., 2022, "Relative Efficiency of Radiation Treatment Centres: An Application of Data Envelopment Analysis", Healthcare, June 10(6): 1033 - 1033.
Abstract: We evaluate a number of cancer treatment centres in Ontario and determine their relative efficiency so that their performance can be compared against the provincial targets by taking into account the differences among them. These differences can be in physical and financial resources, and patient demographics. We develop an analytical framework based on a three- step data envelopment analysis (DEA) model to build efficiency metrics for planning, delivery, and quality of treatment at each centre, use regression analysis to explain our efficiency metrics, and demonstrate how our findings can inform continuous improvement efforts.
Link(s) to publication:
https://www.mdpi.com/2227-9032/10/6/1033
http://dx.doi.org/10.3390/healthcare10061033
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Bayley, T.; Wheatley, D.; Hurst, A., 2021, "Assessing a novel problem‐based learning approach with game elements in a business analytics course", Decision Sciences Journal of Innovative Education, July 19(3): 185 - 196.
Abstract: Business education has traditionally relied on case-based learning as its main form of active learning. However, this method is not always appropriate in introductory undergraduate business analytics courses, which require students to first master analytical techniques, best taught through examples of numerical problems. Building on established problem-based learning (PBL) pedagogy, we propose a new approach in which students solve well-structured problems in a gamified environment. The learner is challenged to solve a series of numerical problems at their own pace in a self-directed manner. The series of problems are designed such that the student must find the correct solution to the first problem to unlock and progress to the next problem, and so on. To assess our method, both student outcomes and experience were evaluated in a controlled study that compared it to traditional lecturing. While student outcomes were similar, students perceived traditional lectures as more effective. Our results indicate that the game elements in our approach did not sufficiently increase student engagement to counteract negative student perceptions of PBL, which are well-documented in the literature. We conclude with a discussion of advantages and drawbacks of this new approach, considerations for adapting it to virtual settings, and opportunities for expanding game elements to increase student engagement.
Link(s) to publication:
https://onlinelibrary.wiley.com/doi/full/10.1111/dsji.12246
http://dx.doi.org/10.1111/dsji.12246
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Bayley, T.; Hurst, A., 2018, "Teaching Line Balancing through Active and Blended Learning*", Decision Sciences Journal of Innovative Education, April 16(2): 82 - 103.
Abstract: The design of balanced assembly lines, especially when considering workforce, material, and cycle time factors, is an important managerial decision-making activity in manufacturing firms. Numerous active learning activities are available to assist instructors in teaching assembly line balancing to students. While effective in improving student engagement, they require considerable planning and expense on the part of instructors, and they may be difficult to implement in inflexible teaching spaces and lecture-oriented curricula. We present a new approach to teaching line balancing using online videos depicting an assembly process. Students design an assembly line by determining themselves how to separate and time tasks, rather than by modifying an existing configuration. To save valuable classroom time, students complete a portion of the activity outside of class. This blended learning approach allows for all students to be engaged in the activity, both in and out of class. Furthermore, a controlled study showed that compared to the traditional lecture format, it better equips students to address less tangible aspects of line balancing, such as ergonomic and workforce factors, material handling considerations, and changing cycle time. With the online content for this activity completely developed and available, other instructors can easily implement this approach within their courses.
Link(s) to publication:
http://dx.doi.org/10.1111/dsji.12148
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Bayley, T.; Süral, H.; Bookbinder, J. H., 2018, "A hybrid Benders approach for coordinated capacitated lot-sizing of multiple product families with set-up times", International Journal of Production Research, February 56(3): 1326 - 1344.
Abstract: We examine a coordinated capacitated lot-sizing problem for multiple product families, where demand is deterministic and time-varying. The problem considers set-up and holding costs, where capacity constraints limit the number of individual item and family set-up times and the amount of production in each period. Using a strong reformulation and relaxing the demand constraints, we improve both the upper and lower bounds using a combination of Benders decomposition and an evolutionary algorithm, followed by subgradient optimisation. Through computational experiments, we show that our method consistently achieves better bounds, reducing the duality gap compared to other single-family methods studied in the literature.
Link(s) to publication:
http://dx.doi.org/10.1080/00207543.2017.1338778
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