Linking supply chain management practices with supply chain performance and food and beverage: Evidence from SMEs' competitive advantage
Bibliographic record
Abstract
The development of technology and science in the Industrial Revolution 4.0 era requires companies to increase effectiveness and efficiency to maintain a competitive advantage. Supply chain management is the integration of business processes involving end customers and key suppliers whose function is to provide value to customers and stakeholders by providing products, services, and information. Implementation of supply chain management is considered an operational function or company activity that greatly determines the effectiveness and efficiency of the supply chain. This research aims to analyze the relationship between the implementation of supply chain management and the performance of small and medium enterprises (SMEs), the relationship between supply chain management, and the relationship between competitive advantage and the performance of SMEs. The research uses a quantitative descriptive approach. The quantitative approach is data in the form of numbers which are generally arranged through structured questions. The questionnaire contains statement items designed using a Likert scale from 1 to 7. The data in this study uses cross-sectional data because the data collection was carried out in a certain period. The data was obtained from distribution of online questionnaires via social media. The unit of analysis used in this research is the owners/managers of SMEs in Indonesia. The sampling technique used in this research is a non-probability sampling technique, namely purposive sampling. The total sample for this research was 432 respondents. The data management used in this research is the Structural Equation Model (SEM) method, which is a collection of statistical testing techniques on a series of relatively complex relationships, simultaneously. The data processing tool is SmartPLS 3.0. The SEM technique is used to examine and justify different hypotheses of the survey. Hypothesis testing is carried out by comparing the p-value with a confidence level (alpha) of 5% (𝛼 = 0.05). The results of this research show that the implementation of supply chain management has a positive effect on the performance of SMEs. In addition, supply chain management has a significant effect on competitive advantage while competitive advantage has a significant effect on SME performance. This research shows that supply chain management has a positive influence on competitiveness both with performance and competitiveness. Descriptive analysis found that supply chain management indicators have sufficient value and have a big impact on performance and competitiveness.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".