The effect of digital supply chain on lean manufacturing: A structural equation modelling approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aimed to test the impact of digital supply chains on lean manufacturing, the digital supply chain was a multidimensional measurement composed of seven dimensions: Digital performance management, digital information technology and digital manufacturing, digital human resources, digital suppliers, digital logistics and inventory and digital clients. The electronic industries companies were targeted to represent the research population and collect the primary necessary data. According to the research budget and time constraints, a convenience sampling method was implemented in the data collection process. Structural equation modeling (SEM) was applied to test the research hypotheses through AMOS software. The results indicated that most of the digital supply chain dimensions had a positive impact on lean manufacturing, except digital suppliers and digital clients, which had no effect on lean manufacturing. Findings from this research help organizational managers make multiple decisions related to investing and allocating resources to increase profit and reduce expenses along digital supply chains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it