Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".