Process embedded design of integrated management systems
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a process‐based design of integrated management systems (IMS) implementation. Design/methodology/approach An extensive survey of peer‐reviewed literature was conducted. Based on the literature review, a comprehensive methodology for the design and implementation of an IMS was developed. Findings A critical review of the strategies employed and of difficulties encountered in IMS implementation reveals the need for a context‐ and process‐based design of IMS. At the operational level core activities are first designed from the perspective of stakeholders' requirements and then treated with operational excellence tools to strip away waste. The transformed core processes are then integrated with mainstream individual management systems to form one composite and holistic management system. The institutionalisation of IMS needs to be addressed in its design (through process embedded design) as well as at the users' level (through education and training of employees). Practical implications The paper provides the process‐based strategy for IMS implementation and institutionalisation. Originality/value The paper should be useful for practitioners searching for a recipe to integrate management systems, for government regulatory agencies seeking to facilitate the integration of management systems, and for researchers as a future area of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".