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Record W2976540634 · doi:10.1177/0972063419868552

A Decision-making Model for Supplier Selection in Indian Pharmaceutical Organizations

2019· article· en· W2976540634 on OpenAlexaff
Anirban Ganguly, Chitresh Kumar, Debdeep Chatterjee

Bibliographic record

VenueJournal of Health Management · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicMulti-Criteria Decision Making
Canadian institutionsConcordia University
Fundersnot available
KeywordsMultiple-criteria decision analysisAnalytic hierarchy processSelection (genetic algorithm)StructuringSupply chainProcess (computing)BusinessFuzzy logicComputer scienceProcess managementManagement scienceOperations researchRisk analysis (engineering)MarketingEconomicsEngineeringArtificial intelligenceFinance

Abstract

fetched live from OpenAlex

Supplier selection is the process by which firms identify, evaluate and contract with suppliers. The supplier selection process deploys a tremendous amount of a firm’s operational and financial resources and is considered as an important determinant of the success of its supply chain. In spite of being strategically important to organizations, the decision for supplier selection is often complex and unstructured. Furthermore, it is inherently a multi-criterion decision-making (MCDM) problem, which pertains to structuring and solving decision problems involving multiple criteria. The paper provides a framework to analyze and evaluate supplier selection in Indian pharmaceutical sector (IPS) using MCDM technique of fuzzy analytic hierarchy process. It intends to improve managerial decision-making in the IPS in developing a supplier selection strategy based on multi-criteria evaluation technique.

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)0.009
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.125
GPT teacher head0.497
Teacher spread0.373 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations16
Published2019
Admission routes1
Has abstractyes

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