Cracking the Bullwhip: Team Collaboration and Performance within a Simulated Supply Chain
Bibliographic record
Abstract
The current study explored the role of collaboration in team performance using a computer-based simulation of a supply chain called the Beer Game developed by the Massachusetts Institute of Technology (MIT). In SCM simulations, as in real life, a ‘bullwhip’ effect leads to a drop in profitability of the supply chain. The inclusion of Human Factors knowledge within the domain of SCM provides a rich source of understanding of bullwhip-related phenomena experienced by managers. In this paper we describe a technique called ‘Cognitive Network Tracing’ which is used to examine the processes by which supply chain members make decisions and engage in communication in such scenarios. We examined the influence of different levels of Situation Awareness (SA) information given to supply chain members, and the influence of individual- or team-focused instructions, on a variety of measures of performance, communication, and SA. Results showed that team-focused groups of participants achieved better supply chain management performance than individual-focused groups of participants, but only when they were given information about current demand level in the supply chain. It is concluded that “Management Flight Simulators”, such as the Beer Game, have validity as tools to examine team collaboration and performance in management scenarios.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".