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Record W2091164709 · doi:10.1108/17410380610639470

Towards best management practices for implementing manufacturing flexibility

2006· article· en· W2091164709 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

VenueJournal of Manufacturing Technology Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsFlexibility (engineering)Process managementBest practiceManufacturingComputer scienceConceptual frameworkCompetitive advantageManufacturing engineeringRisk analysis (engineering)Knowledge managementOperations managementBusinessEngineeringMarketingEconomics

Abstract

fetched live from OpenAlex

Purpose The purpose of this research is to develop a framework and an initial list of best management practices for implementing manufacturing flexibility. Design/methodology/approach To identify these practices, recent frameworks (i.e. 1988 and onward) for implementing manufacturing flexibility in organizations are reviewed. Based on this review, the major management practices for implementing flexibility are identified and synthesized into a new framework. Findings This framework suggests that manufacturing flexibility should be implemented using a three‐stage approach, labeled: identifying required flexibility (i.e. identifying and justifying the flexibility types, measurements and tools needed to achieve the required manufacturing flexibility), achieving required flexibility (i.e. acquiring and implementing the organizational and technological tools needed to achieve the required manufacturing flexibility) and managing required flexibility (i.e. monitoring and changing the required flexibility types and levels, in light of changing uncertainty and competitive, manufacturing and marketing strategies). Based on this framework, a number of potential best management practices are identified. Research limitations/implications This report is conceptual in nature. Future research will focus on empirically testing the practices presented in order to develop a more complete and rigorous list of best management practices for implementing manufacturing flexibility. Practical implications This research provides manufacturing managers with a starting point for developing a formal process for identifying, implementing, and monitoring manufacturing flexibility, thus ensuring that the manufacturing flexibility that exists is continually meeting the manufacturing and competitive strategies of the organization. Various conceptual relationships are identified by the presence of arrows in the framework. As a result, the implications of the conceptual framework for researchers is that it provides a very good starting point for conducting exploratory and confirmatory research on the process of managing manufacturing flexibility. Originality/value This research synthesizes existing frameworks for implementing manufacturing flexibility in organizations, and addresses a gap in the research, specifically the need to identify and empirically test best management practices for implementing manufacturing 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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.033
GPT teacher head0.291
Teacher spread0.258 · 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