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Global Sustainable Supplier Selection

2019· book-chapter· en· W2988584092 on OpenAlexaff
Anjali Awasthi, Stefan Gold

Bibliographic record

VenueAdvances in logistics, operations, and management science book series · 2019
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsSupply chainDimension (graph theory)SustainabilityBusinessSelection (genetic algorithm)Environmental economicsSupplier relationship managementSupply chain managementTransparency (behavior)Yield (engineering)Product (mathematics)Industrial organizationEconomicsComputer scienceMarketingEcologyMathematics

Abstract

fetched live from OpenAlex

Supplier selection is critical for sustainability management in global supply chains. In this chapter, the authors present a content analysis based literature review for global sustainable supplier selection. The supplier selection is investigated along four dimensions, namely economic, environmental, social, and global. The results of the study yield that environmental and social criteria are often used together for supplier selection whereas global risk criteria are rarely used, even less as we move along multiple tiers of the supply chain. From the review, the authors also identified the top criteria along these four dimensions for global sustainable supplier selection. Along the economic dimension, the top three criteria are quality, cost, and general supplier characteristics. The social dimension has employees, transparency and engagement, and local communities' influence as the top criteria. Along the environmental dimension, the top criteria are pollution and hazardous emissions, standards and management systems, green product and design, and green competencies and processes. The top criteria along the global dimension are distance, other risks, and politics and economy.

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.008
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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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