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Record W4224922037 · doi:10.3390/su14095208

Exploring Lean HRM Practices in the Aerospace Industry

2022· article· en· W4224922037 on OpenAlexaffabout
Amal Benkarim, Daniel Imbeau

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBusinessLean manufacturingWorkforceLean project managementHuman resource managementContext (archaeology)Best practiceSustainabilityJob securityKnowledge managementHuman resourcesWork (physics)Process managementMarketingPublic relationsManagementEngineeringPolitical science

Abstract

fetched live from OpenAlex

Lean places people at its core, acknowledging their contribution to the company’s growth and the fundamental role human resources management (HRM) practices play in the success and sustainability of Lean transformations. However, the relationship between HRM practices and Lean remains largely unexplored in the literature. The purpose of this work is therefore to investigate the challenges and contributions of HRM practices in a Lean company, and identify those practices that are required for successful and sustainable Lean implementations. Based on a sample of thirty employees (15 production and 15 office workers) of a Canadian aerospace company who participated in our interviews, we performed a qualitative analysis to identify prominent HRM practices. We found seven HRM practices that are of major importance in the context of Lean (i.e., job security, communication, fairness, supervisor/manager support, training, occupational health and safety, and respect). Our findings show that these practices are equally relevant to both production and office workers, and suggest that managers play a decisive role in implementing these practices, and in providing the right environment to effectively promote workforce commitment.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.130
GPT teacher head0.312
Teacher spread0.183 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2022
Admission routes2
Has abstractyes

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