Product Cost Management via the Kaizen Costing System: Perception of Accountants
Bibliographic record
Abstract
Managing cost of production is at the heart of survival of present day corporations, as increased competition, uncertainty and risk present in the operating environment pose challenges for 21st century managers. This empirical paper examines product cost management strategies by adopting and implementing the Kaizen Costing System. The sample size of the study was 60 respondents comprising accountants distributed across product and service sector organisations in Anambra State, south-eastern Nigeria. Three hypotheseswere formulated and tested in the study. In addition to descriptive statistics, Mann-Whitney U, Kolmogorov-Smirnoff and Multivariate Analysis of Variance (MANOVA) were used in analyzing the primary data. The results ofthe data analysis showed that implementing a Kaizen Costing System would provide managers with strategies for reducing material procurement and usage costs, and also reduce labour utilization cost. Respondents also perceived a link between cost reduction strategies at the minimal batch production phase and the maximum batch production phase. Based on this, the study recommends that Nigerian manufacturing firms should adoptand implementthe Kaizen Costing System, this should however not be done in isolation as such a system would require corporate strategy overhaul and alignment, a kaizen culture was suggested as counterpart in the system implementation for incorporation at the strategic level.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".