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Record W2055448882 · doi:10.1108/17410380610707366

Dispersed network manufacturing: adapting SMEs to compete on the global scale

2006· article· en· W2055448882 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Manufacturing Technology Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsComputer scienceProduction (economics)Knowledge managementProcess managementBusiness

Abstract

fetched live from OpenAlex

Purpose To develop a conceptual framework for a new form of production system unique from many perspectives. The proposed system is based on the creation of a network of plants that are electronically linked so that the participating members focus on their specialized tasks yet also share their manufacturing and production resources to create a loosely structured and flexible enterprise. Design/methodology/approach To introduce dispersed network manufacturing (DNM) as a new business model, and to discuss dispersed manufacturing network (DMN) as a possible realization of DNM. To link DMN to complex adaptive systems and to provide a prototype as to how SMEs can form a dynamic and adaptive network to create competitive advantages on both collaborative and individual scales. Findings The notion of DNM advocates a reciprocal bonding among network members but calls for no obligatory egalitarian responsibility to one another. This research shows the feasibility of a network of plants that are electronically linked so that the participating members, spread geographically, focus on their specialized tasks yet also share their manufacturing and production resources to create a loosely structured and flexible enterprise. Research limitations/implications In the DNM universe, because of the network's requirement to re‐form itself to the needs of each unique incoming project, SMEs have the ability to rapidly develop and enhance their internal production capabilities. Each new incoming project offers the chance to reaffirm those processes that are highly effective while discarding those that are deemed ill‐suited. The DNM world is still in its infancy and many interesting and challenging questions have yet to be investigated empirically or otherwise. Practical implications The new production system discussed in this paper argues for a completely different form of SME collaboration from those already discussed in the literature. Originality/value DNM and DMN are new concepts that are evolving into an innovative production paradigm. It is likely that many companies and many managers have some intuitive grasp of the DNM world and the opportunities provided by forming a DMN. Few, however, might understand them thoroughly. This research provides further knowledge on SMEs DNM.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.193
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it