Advanced decision support tool by integrating activity-based costing and management to system dynamics
Bibliographic record
Abstract
In today's global and competitive business environment cost control and cost management have become a decisive variable in the firm's financial success. This requires reliable tools and techniques to estimate business expenses and enhance the understanding about business operation costs. The ultimate reason for firms to adopt activity-based costing and management (ABC/M) is to manage and control its costs, to reduce them, and thus to improve their financial performance. Several studies have proven the capability of ABC/M in generating valuable cost information in supply chain management and production problems. The ABC/M advantages can be utilized as well in developing system monitoring, controlling, and analyzing tools. A prevailing decision support and monitoring system should analyze and project the effect of each change in the business operation environment. System dynamics (SD) is an approach to investigate the dynamic behavior in which the system status alterations correspond to the system variable changes. This paper is pioneer in introducing a general approach of integrating ABC/M information with SD simulation modeling technique which results in a more reliable and responsible decision support system. This new tool will enhance cost monitoring and cost control in supply chain management and production process.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".