Management Cybernetics as a General Framework to Successfully Implement Lean Management in Several Industries: Observations From the Construction Industry, Mechanical Engineering, and Healthcare
Bibliographic record
Abstract
Numerous lean transformation projects fail or do not achieve the desired performance and/or viability that is necessary for a sustainable implementation of lean management. However, there are some factors that support the successful implementation of lean management in day-to-day business. In the opinion of the authors, these include management cybernetics and, in particular, Stafford Beer’s viable system model (VSM) as a structuring element. Due to its characteristics as an evolution-based organizational system, the VSM appears to be well suited to forming a sustainable basis for an efficiency-focused method such as lean management. In particular, the evolutionary ability of the model to constantly adapt to the requirements of its environment offers a well-suited basic structure for a sustainable connection with the methods of lean management. In order to examine this approach in more detail, a synopsis of three case studies from different industries was created: the construction industry, plant and mechanical engineering, and healthcare. The unifying element of these three case studies is the combination of lean tools to increase efficiency with the VSM as a structural basis for ensuring the needed effectiveness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".