Lean Six SIGMA Using Sigmaxl and Minitab
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Effectively Execute Lean Six Sigma Projects using SigmaXL and Minitab Written by a Six Sigma Master Black Belt and a Ph.D., this practical guide to Lean Six Sigma project execution follows the DMAIC (Define, Measure, Analyze, Improve, and Control) roadmap. The many real-world examples used in the book offer in-depth ...
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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Effectively Execute Lean Six Sigma Projects using SigmaXL and Minitab Written by a Six Sigma Master Black Belt and a Ph.D., this practical guide to Lean Six Sigma project execution follows the DMAIC (Define, Measure, Analyze, Improve, and Control) roadmap. The many real-world examples used in the book offer in-depth theoretical analyses and are implemented using the two most popular statistical software suites--SigmaXL and Minitab. This expert resource covers Lean topics ranging from basic data analysis to complex design of experiments and statistical process control. Harness the power of SigmaXL and Minitab and enable sustained positive operational results throughout your organization with help from this authoritative guide. Lean Six Sigma Using SigmaXL and Minitab explains how to: Define the project goals, project manager, value statement, stakeholders, and risk Schedule tasks using the Gantt chart, critical path analysis, and program evaluation and review technique Capture the voice of internal and external customers Assess the cost of quality Gather data and measure process performance Perform process capabilities analysis Apply Lean Six Sigma metrics to determine baseline performance Implement analysis techniques such as Pareto analysis, value stream mapping, failure mode and effect analysis (FMEA), and regression analysis Identify constraints via factorial experiments, and implement process improvements Monitor production performance using statistical process control
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