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Setup Time Reduction for Electronics Assembly: Combining Simple (SMED) and IT‐Based Methods

2005· article· en· W2159737632 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

VenueProduction and Operations Management · 2005
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsSheridan College
FundersAlfred P. Sloan FoundationUniversity of California, San DiegoNational Science Foundation
KeywordsKey (lock)Computer sciencePrinted circuit boardElectronicsReduction (mathematics)Simple (philosophy)Process (computing)Manufacturing engineeringReliability engineeringEmbedded systemElectrical engineeringMathematicsEngineeringComputer securityOperating system

Abstract

fetched live from OpenAlex

As much as 50% of effective capacity can be lost to setups in printed circuit board assembly. Shigeo Shingo showed that radical reductions in setup times are possible in metal fabrication using an approach he called “Single Minute Exchange of Dies” (SMED). We applied SMED to setups of high speed circuit board assembly tools. Its key concepts were valid in this very different industry, but while SMED typically emphasizes process simplification, we had to add modern information technology tools including wireless terminals, barcodes, and a relational database. These tools shield operators from the inherent complexity of managing thousands of unique parts and feeders. The economic value of setup reduction is rarely calculated. We estimate a reduction of key setup times by more than 80%, and direct benefits of $1.8 million per year. Total cost of the changes was approximately $350,000.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.110
Threshold uncertainty score0.467

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 communication0.0000.000
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 head0.015
GPT teacher head0.294
Teacher spread0.278 · 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