Lean Bundles and Performance Outcomes in the Pharmaceutical Industry: Benchmarking a Jordanian Company and Operational Excellence International Project
Bibliographic record
Abstract
This study benchmarks the implementation levels and performance outcomes of lean bundles of a Jordanian pharmaceutical manufacturing company with the results of the Operational Excellence (OPEX) model. The OPEX model is an ongoing international research project in the pharmaceutical industry. The results of the OPEX project were obtained, along with permission to use them in the present study. The same question items used in the OPEX project were used to prepare a questionnaire to collect data from the Jordanian pharmaceutical company. Fifteen managers from the Jordanian company with responsibilities related to lean management completed the questionnaire. The results demonstrated that the implementation levels of lean practices and the lean performance outcomes of the Jordanian pharmaceutical company varied in comparison to the OPEX project. The overall assessment showed that the Jordanian company is excellent in the total quality management bundle (at or above the levels of the benchmarked data), good in the human resource bundle (almost at the levels of the benchmarked data), acceptable in the just-in-time bundle (below the levels of the benchmarked data), and weak in the total preventive maintenance bundle (considerably below the levels of the benchmarked data). There are few benchmarking studies in the pharmaceutical industry in the area of lean management. In particular, this area is under-investigated in the developing world. The current study provides insights into the value of benchmarking key lean metrics against leading companies. This approach is expected to support pharmaceutical industry managers in the developing world to evaluate their current lean state, estimate the desired state based on the benchmarking results, and set appropriate strategies to promote lean management and operational excellence in their companies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".