Supply Chain ERP Simulation: A unique learning \nexperience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Simulation games are an effective educational tool that offer life-like scenarios and develop valuable functional business \nknowledge and decision-making skills. These skills have been shown to be invaluable in the current job market. \nThis paper describes a novel adaptation of the HEC Montreal Enterprise Resource Planning Simulation Game into an interinstitutional \ncompetition. Teams of university students and academics from five Melbourne-based universities, aided by \nindustry mentors, put their business skills to the test for an intensive simulation game using a real world ERP system: SAP. \nTeams were required to complete the full cash-to-cash cycle, by interacting with suppliers and customers, sending and \nreceiving orders, and delivering a range of muesli bar products. To develop a range of business and „soft‟ skills, participants \nadopted individual business roles and made life-like decisions in a competitive, time-constrained environment. Participants \nfound the game exceeded expectations by fully demonstrating the interaction of a life-like supply chain.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it