Developing an integrated quality network for lean operations systems
Notice bibliographique
Résumé
Purpose Most successful companies have adopted some type of improvement methodology to achieve optimum performance, high quality, lower costs and productivity. Some of the structured methodologies employed indiscriminately are total quality management, quality control, agile, lean and Six Sigma which yield varied results. The purpose of this paper is to explore how to harness the power of an integrated system of quality tools and techniques to create operational excellence. An integrated framework involves matching quality tools and techniques to the multi-phases (input, transformation and output) of lean manufacturing or service ecosystem. Design/methodology/approach Current research of lean quality systems provides a conceptual understanding of core tools employed by manufacturing and service organizations. Interviewing domain experts from a series of manufacturing and service organizations highlighted a common challenge. The challenge was lean tools and methodologies were selected and employed arbitrarily for the different operational phases, which resulted in selective synergies of tools between operational phases. This limitation resulted in rework and duplication of quality efforts through the diverse phases of the transformation system. This study is based on the hypothesis that all phases of an operational system must be linked by common tools and methodologies which enables harnessing quality benefits and synergies throughout the entire operational system. The study methodology trailed through cooperative inquiry using a case study approach to design an integrated framework of tools that facilitates a common platform for manufacturing or service ecosystems. Findings This study suggests that quality systems in a complex competitive environment must consider an integrated iterative approach. An iterative development of lean quality tools for multiple phases produces an integrated quality system. Such systems employ blending and extending of lean quality tools to multiple phases of the transformation system to synthesize agile and versatile quality system. Research limitations/implications A limitation of this study is that the research of integrated framework is based on repertory grid technique only; it should be supplemented by other methods. Second, the proposed framework does not consider the complexity added by the internal and external stakeholders as they interface with the integrated system at different points with reference to phases of the system. Practical implications One of the advantages of this method is its generality, instead of delivering a monolithic system at the culmination of long transformation process we rely on smaller quality sprints which are implemented sequentially at each stage or phase of the transformation system. The phenomena of incremental clustering of time-series of quality sprints for different phases results in true integration from end to end for a transformation system. Social implications This study helps investigate the personal constructs that users and managers employ to interpret and select quality tools or methodologies for the different phases of lean transformational system. Originality/value This study aims to understand the impact of blending quality and business process improvement tools and methodologies to enhance outcomes. The basis of this study is “the power of multiplicity” through which a diverse collection of improvement paths is pooled into an integrated framework of quality tools for lean and efficient operations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,009 | 0,010 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».