ERP Simulation Game: Establishing Engagement, Collaboration and Learning
Bibliographic record
Abstract
The importance of ERP (enterprise resource planning) systems as a major system for organisational change and transformation has been one of the main reasons they have created such excitement within the educational arena. This pap er examines the use of an ERP simulation game to prepare university graduates to meet the challenge of a global supply chain environment. It describes the novel approach taken to adapt the HEC Montreal ERP simulation game into a one day online inter-institutional competition. The competition involved teams of university students and lecturers from four Melbourne-based universities who, with the help of industry mentors, put their business skills to the test for an intensive simulation game by using a real world ERP system: SAP. The teams ran the full business cycle of a small manufacturing company, while interacting with suppliers and customers by sending and receiving orders, delivering the product and completing the entire cash-to-cash cycle. To develop a range of business and ‘soft’ skills, participants adopted individual business roles and made life-like decisions around the product they were selling: muesli bars. In general, participants felt that although their general knowledge of ERP systems was low, the game fully demonstrated the interaction of the supply chain. Additionally the game exceeded their expectations as they worked alongside an industry mentor in a team environment to achieve a common goal. --PACIS 2011 held: Brisbane, 7-11 July, 2011
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".