Inventory Management and Operational Performance of Manufacturing Firms in South-East Nigeria
Bibliographic record
Abstract
This study aims to ascertain the relationship between inventory management and operational performance of quoted manufacturing firms in the south-east; one of the geographic regions with high industrialization prospects in Nigeria. To achieve this, operational performance of manufacturing firms and their association with components of inventory management; inventory cost, just-in-time approach, materials requirement planning and strategic supplier partnership, was examined through a questionnaire. Three hundred and seventy-one copies of a questionnaire issued to five hundred and thirty-eight sampled respondents of four quoted manufacturing firms in the south-east region of Nigeria were properly filled and found relevant to the study. The study used SPSS and Excel-based descriptive statistics to analyze the data collected. Regression analysis was used to test the hypotheses of the study. Study results conclude that there is a positive significant relationship between; inventory cost, just in time approach, materials requirement planning and strategic supplier partnership and operational performance of quoted manufacturing firms in the south-east region, Nigeria. The study recommends among others that, manufacturing firms in south-east Nigeria should adopt inventory practices such as strategic supplier partnership, just-in-time approach, materials requirement planning and inventory cost control due to the significant effect these practices have on operational performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".