Managing highly flexible facilities: an essential complementary asset at risk
Bibliographic record
Abstract
Purpose Twenty first century problems are increasingly being addressed by multi technology solutions developed by regional entrepreneurial and intreprepreneurial innovators. However, they require an expensive new type of fabrication facility. Multiple technology production facilities (MTPF) have become the essential incubators for these innovations. This paper aims to focus on the issues. Design/methodology/approach The authors address the lack of managerial understanding of how to express the value and operationally manage MTPF centers through the use of investigative case study methods for multiple firms in the study. Findings Owing to the MTPF centers' novelty and outward similarity to high volume semiconductor fabrication (HVF) facilities, they are laden with ineffective operation and strategic management practices. Metrics are the standard for both operational and strategic management of HVF facilities, yet their application to this new type of center is proving ineffectual. Research limitations/implications These new types of regional economic resources may be at risk. A new approach is needed. Practical implications The authors develop an operational and strategic metrics management approach for MTPFs that are based on these facilities' unique nature and leverages both the HVF and R&D metrics knowledge base. Social implications Innovations at the interface of micro technology, nanotechnology and semiconductor micro fabrication are poised to solve many of these problems and become a basis for job creation and prosperity. If a new management technique is not developed, then these harbingers of regional economic development will be closed. Originality/value While there is an abundance of research on metrics for HVF, this is the first attempt to develop metrics for MTPFs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".