Offshore vs. domestic sourcing in a retail environment : a hybrid decision model utilizing a total cost of ownership and analytic hierarchy process methodology
Bibliographic record
Abstract
In today's fast paced society, consumers expect their demand to be satisfied instantaneously; otherwise, they will go to the competition. Selecting a strategic supplier is an imperative undertaking, especially in a retail environment, to the company's success and profitability. Retailers have been steadily increasing their offshore penetration due to the low cost of goods. Global sourcing has become an important part of a retailer's strategy to achieve a competitive advantage. However, even though low cost is an important element of improving sales margins and profitability, additional criteria must be taken into consideration when determining the optimal sourcing strategy. This project addresses the issue of strategic supplier selection in a retail environment focusing on offshore versus domestic sourcing decisions. This is accomplished by developing a hybrid decision model which utilizes a total cost of ownership (TCO) model incorporated into an analytic hierarchy process (AHP) framework. This model is comprehensive and straightforward to apply in comparison to similar models within the literature. The model is applied to ABC Company, a major Canadian retailer. The analysis carried out in this project indicates that, for ABC Company, a domestic supplier is favorable.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".