About: DMAIC is a research topic. Over the lifetime, 1706 publications have been published within this topic receiving 19151 citations.
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TL;DR: In this paper, the authors analyze the principles and results of lean production and compare the lean production philosophy with the six sigma quality process and the principles of total quality management (TQM).
Abstract: Purpose - The authors analyze the principles and results of lean production and compare the lean production philosophy with the six sigma quality process and the principles of total quality management (TQM). At the end of the paper, it is discussed how to build the necessary company culture for having success with these principles/management philosophies. Design/methodology/approach - Literature search and comparative analysis complemented with a Danish case on wastage in a core process. Findings - It is shown that the lean production philosophy and the six sigma steps are essentially the same and both have developed from the same root - the Japanese TQM practices. The improvement process from six sigma, the DMAIC process, can be regarded as a short version of the Quality Story, which was developed in Japan in the 1960s as a standard for QC-circle presentations. We conclude that the roadmaps of lean production and six sigma quality are examples of new alternative TQM roadmaps. We also conclude that especially with lean production and six sigma quality there seems to be too much focus on training people intools and techniques and at the same time too little focus on understanding the human factor, i.e. how to build the right company culture. Originality/value - The detailed and historical analysis of six sigma quality, lean production and TQM combined with a focus on the human factor and the needed corporate culture. © Emerald Group Publishing Limited.
13 May 2014
TL;DR: In this paper, the authors describe the six-sigma infrastructure, six- Sigma goals and metrics, and how to select and track Six-Sigma Projects, as well as how to maintain control after the project.
Abstract: Preface Introduction PART I Chapter 1: Building the Six Sigma Infrastructure Chapter 2: Six Sigma Goals and Metrics Chapter 3: Creating Customer-Driven Organizations Chapter 4: Training for Six Sigma Chapter 5: Six Sigma Teams Chapter 6: Selecting and Tracking Six Sigma Projects PART II Chapter 7: Introduction to DMAIC and Other Improvement Models Chapter 8: Problem Solving Tools Chapter 9: Basic Principles of Measurement Chapter 10: Measurement Systems Analysis Chapter 11: Knowledge Discovery Chapter 12: Statistical Process Control Techniques Chapter 13: Process Capability Analysis Chapter 14: Statistical Analysis of Cause and Effect Chapter 15: Managing Six Sigma Projects Chapter 16: Risk Assessment Chapter 17: Design of Experiments (DOE) Chapter 18: Maintaining Control After the Project Chapter 19: Design for Six Sigma (DFSS) Chapter 20: Lean Manufacturing and Six Sigma Appendix - 21 Tables References Index
26 Oct 2000
TL;DR: In this paper, the authors present a glossary of basic statistical terms for short run control charts, including the area under the standard normal curve, critical values of the t-distribution, Chi-square distribution, and Poisson probability.
Abstract: Part I: Six Sigma Implementation and Management 1.Building the Responsive Six Sigma Organization 2.Recognizing and Capitalizing onOpportunity 3. Data-Driven Management 4. Maximizing Resources Part II: Six Sigma Tools & Techniques 5.Project Management Using DMAIC and DMADV 6. The Define Phase 7. The Measure Phase 8. Process Behavior Charts 9. Measurement Systems Evaluation 10.Analyze Phase 11.The Improve/Design Phase 12. Control/Verify Phase Appendices Glossary of Basic Statistical Terms Area Under the Standard Normal Curve Critical Values of the t-Distribution Chi-Square Distribtion F Distribution Poisson Probability Sums Tolerance Interval Factors Control Chart Constants Control Chart Equations Table of d Values Factors for Short Run Control Charts Sample Customer Survey Process Levels and Equivalent PPM Quality Levels Black Belt Effectiveness Certification Green Belt Effectiveness Certification AHP Using Microsoft Excel
TL;DR: In this article, the authors proposed a Lean-Sigma framework to reduce the defect occurring in the final product (automobile accessories) manufactured by a die-casting process, which integrates Lean tools (current state map, 5S system, and total productive maintenance) within Six Sigma DMAIC methodology to enhance the bottom-line results and win customer loyalty.
Abstract: Lean and Six Sigma are two widely acknowledged business process improvement strategies available to organisations today for achieving dramatic results in cost, quality and time by focusing on process performance. Lately, Lean and Six Sigma practitioners are integrating the two strategies into a more powerful and effective hybrid, addressing many of the weaknesses and retaining most of the strengths of each strategy. Lean Sigma combines the variability reduction tools and techniques from Six Sigma with the waste and non-value added elimination tools and techniques from Lean Manufacturing, to generate savings to the bottom-line of an organisation. This paper proposes a Lean Sigma framework to reduce the defect occurring in the final product (automobile accessories) manufactured by a die-casting process. The proposed framework integrates Lean tools (current state map, 5S System, and Total Productive Maintenance (TPM)) within Six Sigma DMAIC methodology to enhance the bottom-line results and win customer loyalty. Implementation of the proposed framework shows dramatic improvement in the key metrics (defect per unit (DPU), process capability index, mean and standard deviation of casting density, yield, and overall equipment effectiveness (OEE)) and a substantial financial savings is generated by the organisation.
TL;DR: The DMAIC (Define-Measure-Analyze-Improve-Control) method in Six Sigma is often described as an approach for problem solving, but its generality limits the methodological support it provides, and which fails to exploit task-domain specific knowledge.
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