The impact of configuration management decisions on firm resilience: Integrating resource configuration, operational flexibility, and collaborative supply chain strategies
Bibliographic record
Abstract
Global competition requires the company's appropriate rules and policies. The company is trying to build strong, firm resilience to maintain sustainability. The research aims to analyze the influence of management configuration decisions on company resilience through the integration of resource configuration, operational flexibility, and collaboration in the supply chain. This study was conducted on 462 manufacturing companies in Indonesia that were experiencing changes in operational systems and global competitive pressures. Data was collected through questionnaires and analyzed using the Partial Least Square (PLS) method. The research results show that management configuration decisions influence resource configuration and supply chain collaboration integration but do not directly influence operational flexibility. Resource configuration is proven to increase operational flexibility and supply chain collaboration integration, which in turn strengthens company resilience. Operational flexibility also plays an important role in supporting the integration of supply chain collaboration and company resilience in the face of external disruptions. This research provides a theoretical contribution to the development of a tough and practical supply chain strategy for management in building an organization that is adaptive to disruption.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".