Microfactories and the new economies of scale and scope
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the microfactory model, the elements that enable it and its implications. The authors argue that microfactories reduce the risks and costs of innovation and that they can move various industries toward more local, adaptive and sustainable business ecosystems. Design/methodology/approach This conceptual paper explores several processes and practices that are relatively new; hence, it uses online secondary sources (e.g. interviews with CEOs, videos, blogs and trade magazine articles) extensively. Findings Given its versatility and high automation levels, the microfactory model can fill the gap between artisanal and mass production processes, boost the rate of innovation, and enable the local on-demand fabrication of customized products. Practical implications Currently, manufacturers generally need to make large investments when launching a new product, despite high uncertainty about customer acceptance, thus risking considerable losses. The microfactory model offers a safer alternative by allowing a firm to develop and fabricate new products and test their acceptance in a local market before mass producing them. Microfactories also enable the local on-demand fabrication of highly customized products. Originality/value This paper contributes to the discussion on the economic advantages and disadvantages of scale and scope, which have been insufficiently explored in the digital domain.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".