Game—Rolling the Dice on Global Supply Chain Sustainability: A Total Cost of Ownership Simulation
Bibliographic record
Abstract
Sustainability in management education is a potential solution to societal challenges, influencing students’ worldviews and attitudes to contribute to a more profound social change. Through this innovative dice-based classroom simulation, students are exposed to supply chain sustainability, total cost of ownership (TCO), and risk management while also understanding their linkages through effective instructor debrief. Student teams compete by selecting sourcing options such as supplier location, transportation methods, and sustainability reputation from a menu, then see how their decisions fare as the product-line life cycle is simulated with a dice. The debrief facilitated by the instructor compares and contrasts results across the teams, generating insights into the interrelationships between supply chain sustainability choices, TCO, and risk management. Successfully conducted by multiple instructors, in multiple countries, and across all levels of management education (undergraduate, master of science, and executive master of business administration), survey results (n = 350) plus a pilot study (n = 31) confirm that this dice-based simulation accomplishes multiple learning objectives while also providing a highly engaging experiential learning classroom environment for this sample.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".