Breadth vs. depth: How to start deploying the daily management system for your lean transformation
Bibliographic record
Abstract
For a health care organization that has adopted Lean methodologies, a Lean Management System provides the means to sustain the Lean transformation. The Lean Daily Management System (LDMS) is an element within the comprehensive Lean Management System; it addresses the management of daily operations and centers around continuous improvement at the process level. This paper proposes a framework for organizations about to start their deployment of LDMS; it focuses on how to introduce LDMS and how to manage its dissemination. After a literature review, a deployment model that addresses these key points is produced. In developing the model, the question of whether to concentrate on deploying a comprehensive LDMS in one area at a time or to introduce a simplified version of LDMS to the entire organization at once is examined. Who is expected to play the greater role in deploying LDMS in any area is also studied; whether it is executives, middle managers, or front line staff. In order to test the validity and applicability of the deployment model, field research was conducted in three Canadian hospitals that have already begun their deployment of a management system.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".