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Record W2792123558 · doi:10.1080/17509653.2017.1387821

Investigating critical criteria for supplier quality development

2018· article· en· W2792123558 on OpenAlex
Khosrow Noshad, Anjali Awasthi

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

VenueInternational Journal of Management Science and Engineering Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsQuality (philosophy)Process managementSupplier relationship managementQuality managementOrder (exchange)Product (mathematics)BusinessQuality management systemProcess (computing)Computer scienceQuality policyRisk analysis (engineering)Operations managementSupply chain managementService (business)Supply chainMarketingEngineering

Abstract

fetched live from OpenAlex

Identifying critical criteria for supplier quality development is vital for improving the performance of suppliers. In this paper, we determine critical criteria for supplier quality development based on review of literature and discussion with supplier quality experts from industry. The criticality of the criteria is determined based on: (a) Kano’s model (must haves, satisfiers, dissatisfiers); (b) Hill’s manufacturing strategy (order winners and order qualifiers); and (c) criteria weights. The results of the study yield the following as the must-be and order winning elements for supplier quality evaluation: price; delivery performance; service; environment; health and safety; ISO 9000; European Foundation for Quality Management; quality management policy; understanding of customer requirements; supplier product quality; process control; quality inspection programs; process for handling complaints; and a system for corrective action. For supplier quality development, the must-haves and order winning elements are: honoring outstanding suppliers; involving suppliers early in product and process development; and sending instructors and technical consultants to the supplier’s site. The proposed results have strong practical applicability and can be used by decision makers for supplier performance evaluation, setting targets for performance improvement, resource allocation for supplier quality development, and designing necessary operations for improving the performance of their suppliers.

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)0.004
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: none
Teacher disagreement score0.911
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.040
GPT teacher head0.322
Teacher spread0.282 · 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