Impact of lean principles on operational performance in high uncertainty
Bibliographic record
Abstract
Purpose The purpose of this research is to study the impact of perceived adoption of Lean principles on operational performance in Lebanese pharmaceutical industries. Design/methodology/approach A quantitative method was implemented using a questionnaire that targeted 253 respondents working in eight good manufacturing practices (GMP) certified Lebanese pharmaceutical companies. Reliability analysis was performed using SPSS, and the research hypotheses were tested using regression analysis. Findings The results demonstrated that Lean principles positively and directly affected operational performance. It also positively affected operational performance factors of quality, cost and time. However, the analysis of each of Lean principles impact on operational performance cost was analyzed perfection, value, and value stream mapping (VSM) significantly increased operational performance. In addition, pull only positively augmented the cost reduction, whereas flow did not show any effects on any of operational performance’s factors. Practical implications In addition to enhancing operational performance, the positive effect of the perceived adoption of Lean principles on performance is also explained by managers’ efforts in studying the flow of actions in their processes to reduce wastes. To face uncertainty, training and building a workforce that is able to implement Lean principles, equipping this workforce with needed artifacts, and promoting a high-performance culture are crucial for the successful implementation of Lean principles. Originality/value Lean approach has become a major pathway of improvement especially in pharmaceutical companies. Few studies analyzed the impact of each of the Lean principles on the operational performance in companies that operate in era of uncertainty. Furthermore, the perceived adoption of Lean principles is under investigated in the Middle East in general and in Lebanon in particular.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".