Adaptive Cost Accounting Control: Issues in Realizing Deming Synergy
Bibliographic record
Abstract
We report on a consultation addressing the re-configuration of a Standards Cost Accounting System of a major MNC. We identified two fundamental theoretical issues pertinent to this re-configuration: Their Standards Cost Accounting [SCA] System was (1) not adaptive within their control time frame, and (2) the holistic systemic protocols espoused by W. Edwards Deming were not used to condition the decision-making framework addressing control. We developed an adaptive Decision Support System [SCA:DSS] that offered the following integrated systemic features: (i) The SCA:DSS is parametrized using the Marketing/Sales sub-budget as approved by corporate-level management and (ii) is used to set the control standards for direct Materials & Labor costs and ABC related allocations, (iii) A detailed interactive profiling of production activity is produced at a time when adaptive corrective actions would still be reasonably possible, and (iv) Adaptive: Best, Stasis and Corrective Action Cases regarding the effect of these corrective actions on the contribution margin are displayed. However, even given the adaptive design features and the explicit designs to effect holistic integration over the pilot division and the central headquarters of the firm, the SCA: DSS failed to be implemented. We offer valuable insights into this failure-to-launch that may be indispensable in effecting a synergetic environment where adaptive holistic cost control may be realized. In this paper all of the technical functionalities of the SCA: DSS are detailed and a working illustration is provided. The SCA: DSS is offered as a free download without restrictions to its use.
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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 | 0.036 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".