Identification Material Distribution Process to Improve Material Handling Performance Using Risk Matrix Analysis (Case Study at Paper Manufacturing)
Bibliographic record
Abstract
Material handling activities within a company have a very important role, especially in warehousing activities ranging from the process of receiving goods, storing goods to product distribution. To reduce the waste that occurs in material handling, a lean manufacturing approach is used with the Value Stream Mapping (VSM) method for mapping the flow of material handling activities and the flow of information on material handling activities. Waste that occurs in the material handling work process in the printing sheets area includes waiting, excess processing, and transportation. With Root Cause Analysis and Risk Matrix, it is known that the most influential root causes of waste are inadequate human resources, material handling tools in preparing goods and process break quantity from standard pallets, and distribution of areas far from the printing sheets warehouse area. Recommendations for improvement proposals offered are, providing additional material handlers, material handling tools for activities within one department by considering distance and time, reducing break quantity requests and temporary locations for receiving printing sheets in the production area, as well as collaborating with suppliers to arrange the arrival schedule of printing sheets material by communicating and updating regularly with the forwarder if the frequency of delays is too frequent. So that if it is repaired and applied it will improve the performance of the material handling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".