Activity Based Aggregate Job Costing Model for Reconfigurable Manufacturing Systems
Bibliographic record
Abstract
Manufacturing continues to face escalated cost challenges as the global economy grows. In order to gain competitive advantage among its rivals, manufacturing firms are in a constant strive to lower their manufacturing costs compared to their competitors. This paper introduces a mathematical optimization model based on Activity Based Costing (ABC) method for Reconfigurable Manufacturing Systems (RMS) taking into consideration the bi- directional relationship between hourly rates and annual hours on each machine/workcentre. The output from the model will be the optimum hourly rates, decision on which jobs to accept or reject and decision on the financial feasibility of reconfiguration. Reconfiguration in this paper describes both system-level reconfiguration (investing in additional machining equipment) and/or, machine-level reconfiguration (extra module to an existing equipment). The model will be applied on a real life case study of a global Original Equipment Manufacturer of Machinery. The novelty of the proposed model is the incorporation of the bi-directional relationship between hourly rates and annual hours on each machine and provides a managerial decision making tool in terms of investment level required to pursue new business, and gaining competitive advantage over rivals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".