The effect of lean on the operational performance of medium-sized Thai manufacturing companies
Bibliographic record
Abstract
Lean practices appear to have aided organisations in improving their operations, especially manufacturing companies. This paper reports on an in-depth study comparing Thai medium-size manufacturing companies who use Lean with those which have not yet adopted Lean. In this paper the word “Lean” is used to refer to “lean practices” as well as “lean strategies”. Lean methods include just-in-time (JIT), total productive maintenance (TPM), automation, value-stream mapping (VSM), Kaizen, material requirement planning (MRP), Kanban, 5S and waste elimination. Primary data were obtained through questionnaires which were analysed using a number of statistical processes including factor analysis (CFA) and structural equation modelling (SEM). Some 230 medium-sized manufacturing companies were the focal target of this study. Three latent variables, Lean (exogenous variable), product customisation (endogenous) and operational performance (endogenous) are formed in this study. There were 24 parameters and the findings provide further evidence regarding the effects which Lean practices have on product customisation and operational performance of the companies, for example. The methods for analyzing the data favour the proposed structural equation model. The results show that Lean strategies are linked to improved operational performance in the firms which practice them. Management teams in Thai medium-size manufacturing companies which have resisted Lean are encouraged to adopt Lean in order to improve operational performance, particularly in the present economic climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".