Identification of Implementation Lean, Agile, Resilient and Green (LARG) Approach in Indonesia Automotive Industry
Bibliographic record
Abstract
Indonesia's economic growth, which has developed in the last few decades, has contributed to an increase in the automotive industry sector. The automotive industry continues to increase the competitiveness of the automotive industry. The Lean, Agile, Resilient, Green (LARG) approach that has been applied by several global automotive industries, is able to increase competitiveness and performance in a sustainable manner. The purpose of this study is to analyze the implementation of the LARG approach in Indonesian automotive, as one of the bases for the automotive industry in Asia. The LARG value index is conducted to determine the level of application in the automotive industry in Indonesia. The mapping of the level of importance and performance of each of the LARG sub indicators was carried out using the Importance Performance Analysis (IPA) method. The results of this study confirm that there are several LARG sub indicators that have not been implemented properly. Green has the most sub-indicators that need to be improved, namely PG1 (ISO 14000 and OHSAS Certificates), PG2 (Collaboration with suppliers and customers in protecting the environment), PG6 (Carrying out industrial waste recycling), and PG8 (Product design that can reduce consumption of energy and raw materials). The calculation of the LARG implementation index value is 4.41.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".