Efficiency in Production Operations Management: Impact on Corporate Competitiveness and Strategic Positioning
Bibliographic record
Abstract
This article examines the correlation between the optimization of production operations and both the competitiveness and strategic positioning of firms in a globalized economic context. Drawing on the Resource-Based View (RBV) theory, Porter’s Value Chain model, and the principles of Industry 4.0, the study employs a mixed-methods approach, combining quantitative and qualitative analyses. The sample includes 150 manufacturing firms of various sizes operating in Douala, Cameroon.The findings reveal a significant positive correlation (r = 0.65, p < 0.01) between operational efficiency and competitiveness, with a regression model indicating that 56% of the variance in competitiveness is explained by operational efficiency. Furthermore, the impact is particularly pronounced in technology-intensive industries. Respondents’ testimonies emphasize the critical role of digital transformation via Industry 4.0 as a lever for strategic differentiation.The study concludes that optimizing operational processes is vital for enhancing competitiveness and strategic positioning, recommending the adoption of methodologies such as Lean and Six Sigma, as well as investment in advanced technologies. Finally, it proposes avenues for future research to further explore these dynamics across various industrial sectors.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".