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Record W2080891725 · doi:10.1108/09576050010378522

Fractal manufacturing partnership: exploring a new form of strategic alliance between OEMs and suppliers

2000· article· en· W2080891725 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

VenueLogistics Information Management · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsGeneral partnershipOriginal equipment manufacturerAutomotive industryManufacturing engineeringAgile software developmentBusinessAgile manufacturingVariety (cybernetics)Product (mathematics)Lean manufacturingProcess managementComputer scienceEngineering

Abstract

fetched live from OpenAlex

In today’s competitive environment, companies are paying more attention to product innovations, customized products, manufacturing agility, and lean operations. To support this mode of operations, companies are moving from highly vertically integrated organizations to leaner core business units supported by supplier networks. While partnership companies and their suppliers could take a variety of forms, “fractal manufacturing partnership” (FMP) is an emerging approach designed to maximize the logistical attributes of a lean production system and configured to provide the strategic merging of engineering network capabilities. FMP is most suitable for engineering assembly type work in which seamless operation is desired and where agile feedback is necessary. Based on the case study of the automotive industry, this paper will discuss the characteristics and attributes of FMP. However, more research is required to quantify the direct benefits of this approach.

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)0.000
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: none
Teacher disagreement score0.794
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.117
GPT teacher head0.267
Teacher spread0.151 · 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