Local on-demand fabrication: microfactories and online manufacturing platforms
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
Purpose This article explores a particular on-demand fabrication unit, the microfactory (MF). It identifies and contrasts several MFs and proposes a taxonomy. This research also explores online manufacturing platforms (OMP) that complement certain MFs. Design/methodology/approach This research implements a multiple case study (71 cases in 21 countries), triangulating data available on the web with interviews, virtual/physical tours and experiential research. Findings The results suggest that automation and openness are the main dimensions that differentiate the MFs. Using these dimensions, a taxonomy of MFs is created. MFs with relatively low automation and high openness tend to be innovation-driven microfactories (IDMFs). MFs with high automation and low openness levels tend to be customization-driven microfactories (CDMFs). And MFs with relatively low automation and low openness tend to be classic machine shops (MSs). There are two types of OMP: closed (COMPs) and multisided (MOMPs). MOMPs can be low-end or high-end. Practical implications In a world where online platforms are becoming central to the reinvention of manufacturing, multisided online platforms and small fabricators will become strongly symbiotic. Originality/value This paper offers a clearer conceptualization of MFs and OMPs, which may help to better understand the reality of local on-demand fabrication. Moreover, it explores a new type of experiential research, which tries to describe and interpret firms through transactional activities. Many details of a firm that are difficult to capture via interviews and netnography can be revealed this way.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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