A decision framework for product global outsourcing in small and medium-sized companies.
Bibliographic record
Abstract
This research will focus on small and medium-sized companies. A decision framework of Product Global Outsourcing (PGO) will be presented. This framework integrates and links the elements that could have impacts on product global outsourcing decision-making, and systematically analyzes the decision-making process step by step. The objective of this framework is to present a simplified and reified approach of PGO decision-making for small and medium-sized companies. The framework is organized in five main levels: (1) environment analysis - the identification of the actual situations surrounding and impacting upon the company, including the external and internal environment; (2) total cost analysis - a total cost mathematical model will be developed; (3) objective analysis - setup the product global outsourcing goals from five aspects; (4) strategy and business planning analysis - identify the company's strategy to achieve its objectives, and provide the business planning, such as supplier selection, contract negotiation and project execution management; and (5) risk analysis. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M46. Source: Masters Abstracts International, Volume: 44-03, page: 1476. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".