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Record W4254603053 · doi:10.1109/wsc.1989.718753

Watmins Jit/Kanban Benchmark Summary and Recommendations

2005· article· en· W4254603053 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

Venue1989 Winter Simulation Conference Proceedings · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsKanbanBenchmark (surveying)Computer scienceQueueProcess (computing)Work (physics)Production (economics)Queueing theoryOperations researchInventory controlManufacturing engineeringIndustrial engineeringControl (management)Production controlWork in processOperations managementEngineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Just-In-Time/KANBAN manufacturing concepts result in models that must simulate pre-process inventories at each stage, delayed processing until downstream operations indicate work should be performed, and comprehensive analysis on production orders (make/move Kanbans) versus inventory and production activities. This style of manufacture is different from the normal push environment where machines will work on anything in the work queue, send the finished parts to the next stage, and keep working until the pre-process inventory queue is exhausted; the material arriving is itself an order to make parts. The two styles imply different control logic and statistics. Many packages support push production directly, and easily provide the necessary controls and information, but there are no packages known to the authors that directly support JIT-pull. It is the purpose of this paper to report on a JIT-pull benchmark comparison of eight simulation tools and the resulting methodological recommendations.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.106
GPT teacher head0.404
Teacher spread0.298 · 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