An Agility Reference Model for the Manufacturing Enterprise: The Example of the Furniture Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it