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Record W2478022240 · doi:10.1108/tqm-10-2015-0131

Employees factors importance in Lean Six Sigma concept

2016· article· en· W2478022240 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

VenueThe TQM Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsBombardier (Canada)
FundersScience and Engineering Research BoardMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsSix SigmaStructural equation modelingKanbanBusinessSupply chainMultinational corporationSample (material)AbsenteeismLean Six SigmaMarketingProcess managementOperations managementBusiness administrationLean manufacturingComputer scienceControl (management)PsychologyMathematicsStatisticsEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Purpose – Lean management and Six Sigma concepts are derived from two different points of view, but it is evident that the role of employees is crucial in both concepts. The purpose of this paper is to survey which employees’ behaviour dimensions can lead organization to better concepts integration and how Lean Six Sigma activity contributes to employees’ performance. Design/methodology/approach – Research methodology is designed to empirically check, on large sample of companies in multinational company supply chain, if employees’ factors are both predictor and response variables of Lean Six Sigma concept. To check stated hypothesis factor, reliability and multiple regression analysis are used. Findings – The first finding of this study is that reward system and training are significant predictors of Lean Six Sigma activities. The second part of findings shows that Lean Six Sigma dimensions, such as Define, Measure, Analyze, Improve, and Control/Define, Measure, Analyze, Design, and Validate, 5S and Kanban positively influences employees’ performance, described by employee satisfaction, absenteeism, salaries and benefits, employees’ commitment and employee turnover rate. Research limitations/implications – Poka-Yoke application is not found as a significant predictor of employees’ performance. Accordingly, to explore that interesting finding, possible future research topic is more detailed analysis of Poka-Yoke application in similar supply chains. A longitudinal analysis using structural equation is possible direction of future work, too. Practical implications – This survey answers the need for Lean and Six Sigma unified methodology achievement in soft factors area and gives applicable results for companies in supply chain that produces low-volume, high-complexity products. Originality/value – Original and valuable conclusion is that employees’ factors are both predictor and response variables of Lean Six Sigma concept application.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.249
Teacher spread0.217 · 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