Integration between open records and target cost to effectively manage supply chain costs
Bibliographic record
Abstract
During the past few years, there have been tremendous attempts among various companies to implement the principles of supply chain management to increase their capabilities. Reduction in supply chain cost is an important element to support competitive advantage. However, to manage the cost of supply chain, managing target and order costs is essential. Reduction in both target cost and order cost is crucial to maintain reasonable supply chain cost, which directly influences the competitive advantage. Thus, the primary objective of this study is to support competitive advantage by the help of integration between target cost and order cost. To achieve this, quantitative research technique was adopted based on a survey technique and 300 questionnaires were distributed among the managerial employees of supply chain companies in Iraq. While analyzing the data through Smart PLS 3, it was revealed that any reduction in target cost and order cost could decrease the overall supply chain cost and this helps to sustain competitive advantage. Therefore, supply chain cost plays the mediating role to enhance competitive advantage through integration of the target cost and the order cost. Finally, this study is beneficial for supply chain companies to enhance competitive advantage through reduction in supply chain cost.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".