Enhancing Operational Performance of Indonesia Pine Chemical Industry Through Delivery Improvement
Bibliographic record
Abstract
Pine chemical products, namely gum rosin and turpentine oil have become one of the main exported non-timber forest products (NTFPs) from Indonesia. As the biggest pine chemical producer in Indonesia, a State Owned Enterprise named Perum Perhutani is required to operate at its optimum capability. Hence, this study aims to identify and measure some dimensions of operational capability that have the most influence on increasing the export performance of Perhutani. The data are collected through online survey from 207 Perhutani employees who work in pine chemical production lines. Four-point Likert Scale questionnaires are employed to collect the data regarding respondent’s perception on seven dimensions of operational capability, namely Cost, Quality, Delivery, Flexibility, Service, Innovation, and Sustainability. R statistics software is selected to run the tests. By adopting Tukey HSD, a multiple pairwise-comparison technique, Delivery is rated as the most important dimension to enhance export performance. It is significantly proven to have the highest mean score. Another finding indicates that Delivery is found to have fair to moderate correlation to other dimension which directly involved in supply chain activities, i.e. Cost, Quality, Flexibility, and Service. Therefore, this study demonstrates a need for Perhutani top management to set minimum and expected targets of some key indicators of Delivery performance such as on-time delivery, delivery accuracy, length of lead-time, and safe inventory level. The higher the rate of on-time and in-full (OTIF) delivery fulfilled, the higher the opportunity to achieve a better export performance of pine chemical products.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".