Implementation of strategic cost management in manufacturing companies: overcoming costs stickiness and increasing corporate sustainability
Bibliographic record
Abstract
Abstract In today's competitive world, three factors: price, quality and time have critical roles in the success of the companies to achieve success in the competition. For this purpose, the companies have to also adapt themselves to changes in technology and environment. Strategic cost management is the best way to improve the sustainable management models in the manufacturing companies. Strategic cost management has solved many of the problems and shortcomings of traditional accounting system and by accurate determination of costs, their proper allocation to products and elimination of waste, tries to create value for shareholders by using continuous improvement. The objective of this paper was to develop a management model called strategic cost management that reduced costs stickiness and increased corporate sustainability. Using strategic cost management approach can create competitive advantage for the companies, because it provides accurate cost price information so that the users can easily understand the information. The aim of the paper by introducing strategic cost management was to contribute toward accurate pricing, which could result in the increased profitability and competitiveness of the manufacturing companies in a highly competitive global market and at a market‐based price. Also, due to the growing competition among companies in providing high quality products with reasonable prices, a precise system of measurement of the cost of the product is necessary.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".