The effect of digital ERP implementation, supply chain integration and supply chain flexibility on business performanc
Bibliographic record
Abstract
Globalization entails manufacturing companies improving their competitiveness to be superior to competitors. This study investigates the role of ERP implementation in improving business performance through supply chain integration, external supply chain integration, and flexibility. The research surveyed manufacturing companies that were implementing ERP technology adequately. Data was collected from 99 manufacturing companies in East Java that have implemented ERP. The study used judgmental sampling with criteria for employees who have worked for two years and permanent employees and have a role as a critical user or end user of one of the ERP modules in the company department. Data analysis used SmartPLS software version 4.0. The results showed that ERP implementation enhances internal supply chain integration by 0.708, external supply chain integration by 0.491, and supply chain flexibility by 0.244. By responding quickly to interdepartmental needs and integrating systems between functions, internal supply chain integration affects external supply chain integration by 0.373, supply chain flexibility by 0.249, and business performance by 0.196. External supply chain integration affects supply chain flexibility by 0.445 and performance by 0.360. Moreover, supply chain flexibility, described by the flexibility of employee working hours as needed, on-time product delivery, and production processes, impacted business performance by 0.378. The study results provide practical contributions for corporate information technology managers to invest in upgrading ERP software and hardware to maintain integration with a single database in making quick and appropriate decisions. A theoretical contribution to increase competitiveness with supply chain strategy and technology integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".