The central role of IT capability to improve firm performance through lean production and supply chain practices in the COVID-19 era
Why this work is in the frame
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Bibliographic record
Abstract
Today, global competition entails companies having an advantage in supply chain networks to pursue superior performance. This work examines the link between information technology (IT) capability with the firm performance by adopting a lean production approach, vendor-managed inventory, and supply chain practices. The study has surveyed the population of the manufacturing company in East Java, Indonesia, using a questionnaire with a five-point Likert scale. A total of 111 manufacturing companies (medium and large) were selected from 5420 manufacturing companies listed in the Industrial Department of East Java. The partial least square (PLS) technique was used to analyze the data, using the SmartPLS software version 3.3. Thirteen hypotheses in this study were developed to investigate. The result revealed that all hypotheses of direct relationship were supported. IT capability directly affects lean production, vendor managed inventory, and supply chain practices. Moreover, lean production, vendor-managed inventory, and supply chain practices improve firm performance. Further analysis also indicated that all hypotheses of indirect hypotheses were supported except hypothesis one hypothesis (H9). IT capability indirectly improves firm performance through lean production, vendor-managed inventory, and supply chain practices. The result provides insight for managers and policymakers on enhancing firm performance by improving its IT capability, adopting lean production, vendor-managed inventory, and supply chain practices. This research contributes to reinforcing the supply chain management theory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it