MétaCan
Menu
Back to cohort
Record W2091804335 · doi:10.1080/0740817x.2011.593609

A waste relationship model and center point tracking metric for lean manufacturing systems

2011· article· en· W2091804335 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

VenueIIE Transactions · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsLean manufacturingMetric (unit)Production (economics)Process (computing)Industrial engineeringPoint (geometry)Work (physics)Value stream mappingPareto principleEngineeringComputer scienceOperations researchManufacturing engineeringOperations managementMathematicsEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

Lean manufacturing is about eliminating waste, which requires the creation of waste metrics that are tracked in order to create the conditions for its elimination. In this article, metrics used to monitor the seven traditional non-value adding wastes types of overproduction, defects, transportation, waiting, inventory, motion, and processing are explored and a “center point metric pair” is proposed that can give systematic insight into system waste performance and trade-offs. For example, lower work-in-process levels (inventory waste) may require more replenishment (transportation waste) in order to maintain production. A waste relationship model is proposed that can be used to derive the relationship between different wastes in a Pareto-optimal waste-dependent lean system. The trade-off relationships are statistically verified using simulation experiments across different system configurations, complexities, and planning scenarios.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.889
Threshold uncertainty score0.572

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 communication0.0000.001
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 head0.097
GPT teacher head0.248
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