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Record W2025605202 · doi:10.1108/09544780610685502

Managing cost of quality: insight into industry practice

2006· article· en· W2025605202 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

VenueThe TQM Journal · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsMcGill UniversityPolytechnique Montréal
Fundersnot available
KeywordsBenchmarkingQuality (philosophy)Quality costsMultinational corporationActivity-based costingQuality managementBusinessOriginalityTotal quality managementComputer scienceProcess managementOperations managementMarketingQualitative researchEconomicsSociologyFinance

Abstract

fetched live from OpenAlex

Purpose The objective of this paper is to present results of the study of the quality costing practices at four large successful multinational companies. Design/methodology/approach The method of benchmarking was used for the purpose of this study. Company representatives, who were invited for a benchmarking session, described the quality management programs running at their companies. Direct observation and archival records data collection were also used to extract more precise information for the following analysis and discussion. Findings The findings of the study show that all four companies use systematic quality initiatives; however, a formal cost of quality (CoQ) methodology was only employed at one of them. This is in agreement with the literature findings arguing that a CoQ approach is not utilized in most quality management programs. Originality/value This paper discusses and compares the quality programs of four companies and explains the benefits of the eventual adoption of a CoQ approach in each case. The analysis provides a new insight into company practice, useful both for academic research and 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)0.002
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 score0.698
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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