Productivity Measurement and the Relationship between Plant Performance and JIT Intensity
Bibliographic record
Abstract
The management accounting and operations management literatures argue that the adoption of advanced manufacturing practices, such as JIT, necessitates complementary changes in the firm's Management Accounting and Control Systems. This study uses a sample of JIT and non-JIT plants operating in the Canadian automotive parts manufacturing industry to study the interaction between performance outcomes, intensity of JIT practices and productivity measurement. This study provides evidence that productivity measurement mediates the relationship between performance outcomes and intensity of JIT practices. Specifically, both JIT and non-JIT plants that use a broader range of productivity measures are more efficient and profitable. Also, plants that employ industry driven productivity measures are more profitable and efficient relative to plants that employ idiosyncratic productivity measures, especially if the former are more JIT intensive. Furthermore, plants that employ quality productivity measures are less efficient and less profitable, especially if they use more intensive JIT practices. The latter result is consistent with JIT intensive plants over-investing in quality. This study also finds that plants that invest more in buffer stock are less efficient and less profitable, especially if they use more intensive JIT practices. Despite the fact that plant profitability and efficiency are highly correlated, JIT intensive plants are more profitable but less efficient relative to plants that are not JIT intensive, after controlling for productivity measures, plant size and buffer stock. This result suggests that despite wasting resources, JIT intensive plants are still able to generate superior profits relative to plants that are not JIT intensive.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".