A hybrid algorithm of BSC and QFD to determine the criteria affecting implementation of successful outsourcing
Bibliographic record
Abstract
Successful organizations share some identical factors that pave the way for their success.Among these factors, strategic management is the key to success for organizations to contribute more to the competitive world market of today.In this respect, the pivotal role of outsourcing cannot be denied.This research parallelizes the criteria affecting the outsourcing success as presented in Elmuti model with the Balanced score card method in the Tose'e Ta'avon Bank.In this research, questionnaires and interviews with experts helped determine the strategic goals at four perspectives of balanced score card method (at Tose'e Ta'avon Bank) and the relative weights were computed for each of balance score card (BSC) perspectives by using AHP method.As the next step, the indexes were prioritized by applying the quality function development(QFD) technique and considering strategic goals at four perspectives in section "WHAT" and the outsourcing success criteria of Elmuti model in section "HOW".At the end of algorithm, the results are compared with the Elmuti method.Based on the results, the hybrid proposed technique seems to perform better than Elmuti.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".