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Record W2050394446 · doi:10.1108/17410390610658487

Implementation and management framework for supply chain flexibility

2006· article· en· W2050394446 on OpenAlexaff
Vinod Kumar, Kamel Fantazy, Uma Kumar, Todd A. Boyle

Bibliographic record

VenueJournal of Enterprise Information Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsSt. Francis Xavier UniversityCarleton University
Fundersnot available
KeywordsSupply chainFlexibility (engineering)Supply chain managementProcess managementConceptual frameworkSupply chain risk managementService managementEmpirical researchRisk analysis (engineering)BusinessKnowledge managementComputer scienceMarketingEconomics

Abstract

fetched live from OpenAlex

Purpose The purpose of this research is to develop a conceptual framework for implementing and managing supply chain flexibility in supply chain organizations. The framework suggests that supply chain flexibility should be implemented and managed using a three‐stage approach: required flexibility identification, implementation and shared responsibility, and feedback and control. Design/methodology/approach The major components of the proposed framework are based on a review of research in the manufacturing flexibility literature as well as the limited research in supply chain flexibility. The strengths and weaknesses of these frameworks, combined with a published empirical study were analyzed to identify the important issues that must be considered when implementing and managing supply chain flexibility, and those components that need to be incorporated into a new integrated framework. Findings This framework was constructed by synthesizing the strengths of other conceptual frameworks. As a result, the major components of the framework are supported by the current research on the implementation and management of manufacturing flexibility, as well as the current literature on supply chain management. Research limitations/implications Empirical research is needed to examine the nature and level of responsibility sharing among different supply chain partners as suggested in this framework. It is also important to empirically investigate what constitutes flexibility in the supply chain taxonomy in various industries. Another issue of managerial interest concerns the way different supply chain flexibility types relate to one another, and whether supply chain organizations should acquire certain supply chain flexibilities as a pre‐requisite for developing others. Further studies are necessary to further explain the contribution made by key enablers, such as information technology and communication, the internet, process technology, and training and labor skills, towards the acquisition of supply chain flexibility. Practical implications The implication of this new conceptual framework for managers is that it is easy to understand and is based on best practices in the research literature on manufacturing flexibility and supply chain management. Originality/value To researchers, this framework provides a springboard for conducting exploratory and confirmatory research on the process of implementing and managing supply chain flexibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0030.014
Scholarly communication0.0090.010
Open science0.0040.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations195
Published2006
Admission routes1
Has abstractyes

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