Narragansett Brewing Company 'Build a Brewery' Case Study Competition, Spring 2012
Bibliographic record
Abstract
Over the past 10 years we have utilized a variety of empirical assignments to add elements of real-world problem solving and decision making in the curricula of the core undergraduate and graduate Operations Management (OM) courses at Bryant University. Some of these assignments have included a customer satisfaction project (Wicks & Visich, 2006), a production game (Roethlein et al., 2002) originated from The Production Game (Denton, 1990), an A3 project for process improvement in health care (Visich et al., 2010), and a web-based mass customization assignment (Visich et al., 2012). While there are varying degrees of creative thinking designed into these projects, they all have a high level of structure in how the students should approach the requirements. This past spring 2012 we decided to add a highly unstructured case study competition to the core OM curricula: a team project to build a brewery for the Narragansett Brewing Company (NBC), where the student teams would compete against each other to see who had the most comprehensive plan. While a case competition was new to our OM curriculum, this format has been used in the past at Bryant University for other courses. The core Computer Information Systems course has a data analysis competition where the teams analyze data from a real financial services company using Excel. The capstone strategy course hosts the Target Case Competition where students analyze a current business problem faced by the Target Corporation. Past projects have included the location of a distribution center to service the New England region and a business strategy for international expansion into Canada. Since this would be our first case study competition, we used our knowledge of these competitions to help us design our case study. In this article, we discuss our Build a Brewery project and the lessons we learned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".