Management by Principle for the Make-To-Order SME’s
Bibliographic record
Abstract
This paper describes a new model developed for make-to-order (MTO) sectors namely SHEN Principles, which aims to fill a gap in the world class manufacturing (WCM) literature that concentrates on the characteristics of the larger traditional make-to-stock (MTS) sector. Using evidence from the literature, especially the MTO literature and the more comprehensive WCM models, a new principle was devised. This was then modified in the light of case study evidence collected from the four MTO companies ranging from small to medium size MTO companies. The model presented in this paper known as “SHEN Principles”, comprises of fourteen principles and categorized into four main sections for ease of reference such as generate enquiries/sales, operations and capacity, human resources and general continuous improvement. SHEN principle is a descriptive approach, with a five point progressive scale for each principle. Level one is the first step on the road to improvement and level five relates to current best practice performance. Its intended purpose is to aid MTO companies to determine their strengths and potential areas for improvement so that they can continue to be competitive in the future.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".