Exploratory case studies on manufacturing agility in the furniture industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the agility of advanced manufacturing technologies (AMTs) in furniture enterprises, and explores the appropriateness of a typology framework that correlates the technology infrastructure of the enterprise with its manufacturing strategy. Design/methodology/approach This paper uses a clear and rigorous case study design and protocol. Empirical data are collected using structured surveys of two strategically selected furniture enterprises. The collected data are used to analyze the fit between the technology infrastructure of the enterprise and its strategic goals, and how this fit correlates with the theoretical categories stated by the typology. Findings The case studies suggest that enterprise performance could be maximized if the competitive priorities and the customization strategy put in practice are in conformity with the available technology. Research limitations/implications The findings of the case studies corroborate the all inclusive hypothesis suggested by the typology. The lack of triangulation of multiple data sources for more confidence about the results or the typology framework itself remains a limitation in this study. The two cases were representative to a certain extent of two out of the three theoretical ideal types stated by the typology. Practical implications The explored typology can serve as a supporting tool for managers when making strategic investment decisions in their pursuit of a mass customization strategy within a specific market. Originality/value The originality comes from the way the properties that should be displayed by the technologies used in furniture manufacturing enterprises to develop agility are drawn together.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".