Improving the inventory management in PT Integra Indocabinet supply chain
Bibliographic record
Abstract
Integra Group is one of Indonesian largest vertically integrated wooden products manufacturer. It was established in 1989 and based in Sidoarjo – East Java. Moreover, Integra Group consists of 8 companies: 5 manufacturing companies (including an indirect subsidiary), 1 distribution company, 2 forestry concession companies and a non-operating holding company, which are fully consolidated under Integra Group. One of those subsidiary is PT Integra Indocabinet that its in charge of manufacturing process. The company produces and sells wooden furniture and other wooden products in the indonesian market and exports its products to the United States, Canada, and Europe. Every month they are able to export in the range of 300 400 containers.The company’s products include indoor furniture, bedroom/casegood furniture, panel furniture, and building components. It also holds forest concession rights. Even though PT Integra Indocabinet have been working in a good way, they are having some troubles about two things: overproduction and defective products, both problems increase the inventory cost and harms its efficiency. Therefore, in order to solve those issues we plan to make demand forecasting analysis through inventory management and apply some lean techniques to avoid shrinkages. That is the main reason why we firmly believe that both problems are inside the company supply chain. Keywords : Supply chain management, overproduction, efficiency, shrinkages, demand forecasting, Indonesia, inventory cost, lean techniques.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".