Robust Management of Virtual Enterprises
Bibliographic record
Abstract
Management is one of the most challenging phases in life cycle of a Virtual Enterprise (VE). It has been found that nearly 70% of the VE cases disband prematurely. In many cases, this premature disbanding is caused by problems related to their management. In this paper, we present a study on analysis of robustness of the VE manufacturing system using the robust design technique. The objective of the study was to develop a methodology that allows for robustness analysis and show what and how variables of the VE system affect its robustness using this methodology. The study was a simulation-based experiment; in particular, the control factors were studied with an L18 experimental design, while the noise factors were with an L9 design. The Analytical Hierarchical Process (AHP) was used as the tool in this study to link the structure of a VE (namely a set of members) and the behavior of the VE (namely disband). The result of the experiment show the methodology is promising. In the science of the VE manufacturing system, this study concluded: (1) the best robustness can be achieved when the partners’ technical capabilities are considered more important than other criteria, (2) business-related criteria should be considered as equally important as other criteria, and (3) both controllable and uncontrollable factors are responsible for the robustness of the VE system in its life cycle.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".