An Agility Reference Model for the Manufacturing Enterprise: The Example of the Furniture Industry
Bibliographic record
Abstract
AbstractThere is an extensive amount of research literature about the concept of agility, describing its drivers and capabilities, and even suggesting methodologies to develop agility. However, most of these efforts remain vague with respect to the characteristics and the expected contributions of the technologies involved or required. This paper proposes an agility reference model; a unifying conceptual representation of agility in terms of the necessary capabilities needed by every process involved in the enterprise seeking for agility. Agility is described using three capabilities which are believed to be the sources of competitive advantages; flexibility, responsiveness, and autonomy. It is shown that each capability addresses some specific issues and can only be thoroughly developed if the technologies used are characterized with some specific attributes or properties. The idea behind the proposed agility reference model was to derive a typology framework that emphasizes the taxonomy of the market interaction strategies for furniture products, and the competitive priorities that should be targeted by furniture enterprises aiming to be agile. Accordingly, the issues related to the different agility capabilities were discussed in the context of the furniture enterprise of the future. Then, the suitability of the proposed model for the derivation of the typology was explored based on case studies on two furniture manufacturing enterprises. The case studies analyze the context in terms of competitive priorities and customization strategies and investigate the agility properties of the technologies in use.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".