The influence of supply chain integration on firm performance through lean manufacturing, green supply chain management and risk management
Bibliographic record
Abstract
The rapid development of technology has enabled companies to integrate internal and external partners working together in the supply chain network. Supply chain integration allows fast information to facilitate real-time and reliable decision-making. This study investigates the role of supply chain integration on firm performance through adopting lean manufacturing, green supply chain management, and risk management. The study surveyed manufacturing companies implementing ISO 14000 to represent green supply chain management and integrated information technology as a form of integration. The questionnaires were distributed using a Google form, and 93 valid responses were obtained. Data analysis employed a partial least square approach with SmartPLS software 4.1 version. The data processing results found that supply chain integration increased lean manufacturing by 0.684, green supply chain management by 0.451, and supply chain risk management by 0.333. Lean manufacturing companies using a continuous process control system and process improvements significantly improve green supply chain management by a path coefficient of 0.477, supply chain risk management by 0.206, and firm performance by 0.370. Green supply chain management significantly impacts supply chain risk management by a coefficient of 0.416 and firm performance by 0.189. Supply chain risk management with a system for detecting operational process risks and emergency procedures in overcoming changes in customer orders affects the increase in firm performance by 0.354. The practical contribution of research provides insight for practitioners to invest in information technology and adopt ISO 14000 implementation. Theoretical contributions in developing resources-based view theory in adopting green supply chain management and lean manufacturing.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".