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Record W2077897469 · doi:10.1108/01445150710724711

The cost of flexibility

2007· article· en· W2077897469 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

VenueAssembly Automation · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMerger and Competition Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFlexibility (engineering)Variety (cybernetics)Automotive industryProductivityScope (computer science)Industrial organizationProduction (economics)Economies of scaleEconomies of scopeOriginalityScale (ratio)Fixed costBusinessEconomicsOperations managementEngineeringMarketingComputer scienceMicroeconomics

Abstract

fetched live from OpenAlex

Purpose In the automobile industry, the variety of vehicles produced continues to increase. At the same time, historically firms have incurred a sizeable productivity penalty for producing more variety in their plants. The purpose of this paper is to answer the question: what actions have firms taken to control this productivity penalty and what were the costs? Design/methodology/approach Estimate a number of statistical models of the effect of variety on productivity for a sample that includes almost all assembly plants in North America from 1994 to 2004. Findings Evidence is found for fixed costs associated with activities that are complementary to producing variety and for a trade‐off between scale economies and flexibility. Research limitations/implications Provides evidence that while flexibility has an advantage to cope with increasing variety, there are non‐negligible costs as well. Originality/value A first systematic evaluation on the scale‐scope trade‐off and a quantification of the gains from production flexibility in the automotive industry.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.269
Teacher spread0.234 · 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