Integration of management systems: A methodology for operational excellence and strategic flexibility
Bibliographic record
Abstract
Organisations employ various management systems (MSs) to systematically address the needs of their stakeholders. As the number of MSs is mushrooming, the need has arisen to integrate them into one holistic business management system that addresses various stakeholder requirements in an integrated manner. However, the dynamics of the integration process are not yet fully understood and research has yet to establish how the integration of MSs gives rise to various types of organisational improvements. This paper focuses on how the integration process unfolds in practice to give rise to a number of socio-technical changes essential to the integration of MSs. This research is based on four cases; it reveals that integration streamlines operational processes through a number of structural, functional, and operational changes. Integration reforms bureaucratic structures, further giving rise to operational excellence and strategic flexibility. The research also provides the extension of lean production practices bundles, and an operationalisation of Adler’s concept of enabling bureaucracy.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".