Unraveling the dynamics of lean manufacturing enablers on operational performance
Bibliographic record
Abstract
Purpose Lean manufacturing (LM) is essential for businesses to remain competitive in today’s global economy and to meet the needs of consumers from three separate perspectives: price, dependability and production schedules. A fundamental goal of this research is to how lean management in manufacturing organization may improve product value for the customer, address customer concerns, minimize costs and boost the firm’s profitability. Design/methodology/approach The extensive literature analysis identified a number of LM enablers and manufacturing industry factors that might favorably affect the organizations operational performance. Initially, 16 enablers of LM and 16 factors operational performance were identified, which were later reduced to 8 factors each. After that, Grey-DEMATEL technique was applied to investigate the relationships between the factors by categorizing elements into two groups (cause and effect) and ranking them within each category. Findings The results show that F4 (Work Force Development) and F7 (Six Sigma) were the key enablers of LM. Similarly, F12 (Maintain Better inventory control/optimize inventory level) and F14 (Reduce conversion cost) are the key effect factors of operational performance. It eliminates inefficiencies in the production process and internal storage requirements while retaining a high level of dependability and flexibility in response to customer demands. Originality/value LM has unquestionably been a popular method for improving the production efficiency of industrial sectors for the last two decades. Despite the fact that LM has helped several firms reduce waste and thereby improve a range of efficiency metrics, many businesses are still struggling to effectively transform into lean firms. While previous studies have explored LM’s significance and its influence on different aspects of organizational metrics in various industries, this research pioneers in probing into the nuanced relationship between LM enablers and OP in a critical and procedure-intensive industry.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".