A new method to measure production spoilage and its effect on cost reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it