A new method to measure production spoilage and its effect on cost reduction
Bibliographic record
Abstract
The current study proposes a new method to account for production spoilage in process costing system, not previously discussed in cost accounting literature and/or textbooks. It differs from traditional methods discussed in cost accounting textbooks in determining normal spoilage units and assignment of production cost. The study used data from a real factory that makes men’s suits for January 2018 to illustrate and explain the proposed method and its impact on cost reduction. The obtained results prove the study proposition that traditional methods to account for production spoilage overstate normal spoilage cost, and hides or understate actual abnormal spoilage. The proposed method reduced normal spoilage cost by 27%, compared to traditional methods. Thus, the significant reduction in normal spoilage resulted also in a cost reduction of good units manufactured. In addition, the abnormal spoilage cost under the proposed method increased by 35% thus, it would be noticeable by management to focus on, control and eliminate. The study recommends that manufacturing firms adopt the proposed method to account for production spoilage as it is more accurate and helps management focus on production spoilage and take corrective actions to control and eliminate.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".