Application of throughput accounting in production mix decisions for a small metallurgical enterprise
Why this work is in the frame
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Bibliographic record
Abstract
Micro and Small Enterprises are a critical catalyst for socio-economic development in Brazil. However, financial and technical limitations frequently hinder the access and implementation of management tools by Micro and Small Enterprises. This study addresses this challenge through a case study that applies the Throughput Accounting to determine the most profitable production mix for the small enterprise Bianfer Indústria Metalúrgica. The company manufactures and commercializes parts and components for agricultural machinery and equipment in Brazil. Production mix decisions are currently based on the owners’ experience, sales history, and Absorption Costing. This approach, however, generates additional costs and inventory thereby compromising the profitability of Bianfer Indústria Metalúrgica. The pursuit of enhanced profitability led to the formulation of three hypothetical scenarios to compare the production mix proposed by Absorption Costing and Throughput Accounting concerning the Return on Assets (ROA). Mathematical modeling and scenario simulations were conducted using the Microsoft Office Excel 365. The results indicate that Throughput Accounting is readily adaptable, solves the problem more quickly, and provides superior financial gains (ROA from 1.36% to 2.71%). This study addresses an important practical gap that can guide students, professionals, and researchers in the application of Throughput Accounting. The main contribution of this study is empirical evidence that Throughput Accounting is an effective management tool for Micro and Small Enterprises. The implementation of Throughput Accounting through a simple Microsoft Office Excel model can significantly improve production mix decision-making in Micro and Small Enterprises.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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