Target Costing Evolution: A Review of the Literature from IFAC’s (1998) Perspective Model
Bibliographic record
Abstract
During the last two decades, with increasing competition in highly changing business environment, literature has cautioned against the efficiency and capability of traditional management accounting techniques in providing sufficient information needed for decision making. In coping with continuing changes in business environment and increasing pressure of competition, cost and management accounting techniques have been changed and new techniques have been developed, especially in the last two decades. This is to enable organizations to stay competitive by producing better quality products at lower costs. As Target Costing (TC) has been innovatively adopted to achieve this objective by Japanese companies in the 1960s, this paper attempts to show the historical development of TC. Premised on the review of forty (42) refereed journal articles published in various accounting journals during the periods of 1984 to 2013, this paper shows how, over times, the TC practices have undergone a three-phase evolution from cost reduction and control to cost and profit management and to quality and functionality improvement. Specifically, the paper examines the evolutionary facet of TC by using the 1998 IFAC-based management accounting evolution framework.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".