Lean Application and Efficiency of manufacturing firms: An empirical study of manufacturing firms in Rivers State, Nigeria
Bibliographic record
Abstract
The increasing demand for speedy delivery of quality products at lower production cost have resulted to new trend in manufacturing to review the gap between input resources inventory and production output inventory. More so, creating competitive niche in the current market environment is now difficult for manufacturers than ever in meeting competitiveness. Consequently, many manufacturing firms are becoming flexible to catch up with the current challenges so as to simultaneously improve quality and productivity. This paper examined the relationship between lean manufacturing and efficiency of 53 manufacturing firms listed with the Manufacturers Association of Nigeria in Rivers State, Nigeria. The questionnaire was used to collect data from respondents and analysed using, mean scores, standard deviations and t-statistic in testing stated hypothesis. It was observed that lean manufacturing has a very strong positive and significant influence on efficiency of manufacturing firms. We recommend that: management of manufacturing firms should set up clear policies on lean implementation and communicate same to staff. Also, managers of manufacturing firms are encouraged to increase its resource commitment to staff training and development so as to inculcate in them skills and knowledge necessary to implement lean practices in within their organisations; therefore, professionalism should be encouraged at all levels of the organization. Again, manufacturing firms should pursue quality consciousness through capability surveillance, in having constant monitoring of suppliers/throughput process to ensure production outputs conforms to product specification and quality standards, should be constantly advocated.
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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 | 0.001 | 0.002 |
| 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".