Exploring the key enabling role of digital technology for enhancing supply chain performance through supply chain collaboration, inventory management and supply chain resilience
Bibliographic record
Abstract
The company always tries to improve competitiveness by improving company performance. Using upgraded digital technology makes it easier for internal and external companies to determine business strategies quickly and precisely. This study surveyed expedition companies in East Java, as many as 104 companies with the criteria of having a transportation fleet. Data collection using questionnaires and dissemination in collaboration with the Association of Express Delivery Service Companies, Post and Logistics Indonesia (ASPERINDO). Research respondents consist of employees or unit leaders who are competent in the substance of the survey. Data analysis uses the partial least square (PLS) method. The results showed that digital technology positively and significantly impacts supply chain collaboration, inventory management, and supply chain resilience. The company's ability to build supply chain collaboration impacts improving inventory management optimization, supply chain resilience, and supply chain performance. Furthermore, inventory management with the ability to control inventory well and on-time delivery does not impact supply chain resilience. However, good inventory management has a positive impact on supply chain performance. Likewise, shipping companies, by increasing supply chain resilience, have an impact on supply chain performance. The results of this study contribute to the theory of supply chain management and resource-based view. The practical contribution enlightens the middle and top management on the importance of digital technology with the suitability of investment and benefits obtained in improving supply chain performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".