Digital Threads and IT Power: Decoding Their Combined Effect on Manufacturing Performance
Bibliographic record
Abstract
This research aims to evaluate the impact of digital supply chain on that of organizational performance in the manufacturing industry with IT capabilities as mediating factor. Quantitative approach is utilized to conduct this research, using a snowball sampling approach, focusing on the five largest organizations that manufacture oil and gas. Overall, a sample of 170 Digital Supply Chain Managers is gathered to provide valuable insight with the help of an online survey questionnaire. Findings suggest that funding in continuous training, nurturing culture of innovation, enhancing cybersecurity measures, adopting advanced IT infrastructure and leveraging data analytics are crucial for maximizing the advantages of digital supply chains. The aforementioned strategies facilitate organizations to attain substantial progress in strategic agility, and operational efficiency; thereby, maintaining competitive advantage in a technology-driven corporate environment. The discussion highlights pivotal role of IT capabilities in elevating supply chain processes through AI and IoT which enhance predictive analytics, supports strategic planning, improves organizational performance and improve operational efficiency. Survey data analysis revealed a strong positive relationship between digital supply chains, organizational performance and IT capabilities. Regression analysis also validated the relationship, with an adjusted R square of 0.934, signifying a vigorous correlation. Besides, ANOVA results confirmed minimal variance, supporting the consistency of data. The analysis offer valuable insights and emphasize on the significance of integrating digital supply chain with modern IT capabilities so as to drive organizational success particularly for manufacturing organizations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".