The influence of supply chain management on competitive advantage and company performance
Bibliographic record
Abstract
At present, the condition of competition in a network is very tight due to rapid technological changes, economic and political stability in Indonesia which is experiencing uncertainty, and the large number of foreign investors entering, as well as new competitors. Companies themselves are required to always innovate in today's increasingly modern era. This competition must be able to create a good network in order to create competitive advantage and company performance in the formation of good Supply Chain Management. This study aims to determine the effect of supply chain management on competitive advantage and company performance. This study will examine supply chain management on competitive advantage and company performance in Small and Medium Enterprises (SMEs) in Bandung City, West Java. In this study, there are several differences from previous research, namely the indicators that will be used in this study. Namely, the measured supply chain management variables are indicators of technology use, supply chain speed, customer satisfaction, supply chain integration and inventory management. The variables of competitive advantage that are measured are the indicators of Price, Quality, and Time to market, and sales growth. Meanwhile, the measured company performance variables are indicators of financial performance and operational performance. The analytical method used in testing the hypothesis is to use Structural Equation Modeling (SEM) with the help of AMOS software version 20. Respondents in this study were 150 respondents in Small and Medium Enterprises (SMEs) in Bandung, West Java. The results of this study indicate that supply chain management has a positive and significant effect on the company's competitive advantage. Competitive advantage has a positive and significant effect on company performance. Supply chain management has a positive and significant effect on company performance. Supply chain management has a positive and significant effect on company performance through competitive advantage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".