Objective of the course aims at imparting knowledge on quality management and Six Sigma tools. Participants will appreciate the importance of quality and relevance of the quality improvement tools. Six Sigma is a structured methodology for solving problems with tools that can be applied to the problem-solving processes in all functional areas of management, i.e., in finance, marketing, HR, operations and information systems. Although initially used to improve quality, Six Sigma is now used by many companies to make cost-saving improvements. Participants of the course will learn to integrate Lean concepts with Six Sigma both deliver faster results and achieve the best competitive position by concentrating on the use of tools that will have the highest impact on the already established performance levels.
II Course Contents
Introduction: Defining quality; product based criteria, user based criteria, value based criteria, Garvin’s 8 quality dimensions, Cost of Quality, The views of quality Gurus (Crosby, Juran, Deming), TQM, Average and Variation, Six Sigma as a Statistic, Six Sigma as a Management Philosophy, TQM vs Six Sigma, Process Mapping, Process Improvement, Process Capability Indices, Statistical Quality Control & QC tools, Process Capability, Process Capability Indices, 5 ‘S’ & Kaizen, ISO 9001 Standard, Lean Concepts, Lean Six Sigma, Six Sigma Methodology, DMAIC, Role of Management in implementation of Six Sigma; Qualitative Six Sigma Tools: Quality Function Deployment (QFD), Failure Modes and Effects Analysis (FMEA), Fault Tree analysis (FTA), Fishbone Diagram, Process Flow Diagram, Qualitative Correlation Test; Foundations for using Statistical Six Sigma Tools: Getting good samples & data, Random vs. Assignable Causes, Use of probability in detecting assignable causes, Data Plots and Distribution, Anderson-Darling Test for Normality, Testing for other distributions; Statistical Six Sigma Tools: Testing for Statistically Significant Change in the Process using variables data: (i) sample vs. population, (ii) between two samples; Testing for Statistically Significant Change in the Process using proportional data: (i) sample vs. population, (ii) between two samples; Testing for Statistically Significant difference in two or more number of processes/treatments using ANOVA and ANOM, One -Way ANOVA, Factorial ANOVA, Testing for Statistically Significant Change with Non-Normal Distribution Processes; Design of Experiment (DOE): DOE, Orthogonal Array, Problem-Solving, Applications in Manufacturing and Services; Quality Control & acceptance Sampling: Advanced Control Charts, Type-I and Type-II Risks, OC Curve, ARL, Acceptance Sampling, Special-Purpose Control Charts; Taguchi Methods for Robust Design: Taguchi's Loss function, Off-line Quality Control, Application of DOE with orthogonal array, Signal-to-Noise (S/N) Ratio Analysis, System Design, Parameter Design, Tolerance Design, Robustness in Designing; Reliability: Introduction to Reliability, Series and Parallel Systems, Other Complex Systems, Reliability Analysis and Prediction, Applications in Product Design Created By: Lingaraj Pattanaik on 07/30/2009 at 12:17 PM Category: ExPGP-III Doctype: Document