Failure Modes And Effects Analysis

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FMEA- All possible failures in a design or a process or even a product can be detected using a smart approach called the FMEA or Failure Mode and Effect Analysis. Using the FMEA approach, one can document the current knowledge and actions about the risk and failures and use it for continuous improvement. Almost all the big companies use FMEA to assess and prevent risks. Ford applied the same approach to processes (PFMEA) to consider potential process induced failures prior to launching production. FMEA prioritizes the seriousness of a risk based on its consequences, the frequency of their occurrence,and their detectability. The main goal here is to take actions to eliminate or reduce failures, starting with the ones at the highest-priority. FMEA is used during design to prevent failures. It is also used for control, before and during ongoing operation of the process.

Examples Of Failure Modes And Effects Analysis

Design FMEA

This is a group of tasks that is done to realize and identify potential systems, or products or failures. DFMEA finds the consequences and outputs of these failures and actions and removes them to provide a written history of the work performed. DFMEA is useful in areas of manufacturing, software, business processes, service industries, healthcare, and regulated industries

Process FMEA

Manufacturers and process engineers aim to make a process error free. Now errors can be made inevitable and this can be done using PFMEA. PFMEA looks at each process step and identifies its possible risks and outcomes from various sources. These sources are : man, method, machinery, material, mother earth (environment), measurement

Functional FMEA

FFMEA or functional FMEA aims to recognize and analyze potential issues thereby introducing new functionality or design ideas that can be added into the design system. It is a pro-active tool that identifies functionalities and features to make the product more robust and failure resistant.

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Lean Six Sigma Green Belt Course Content

Module 1: Introduction

Introduction to class
Introduction to Anexas

Module 2: Course Overview

Course purpose & objectives
What is Six Sigma
History of Six Sigma

Module 3: Why Six Sigma

Estimating process sigma
Understanding Variation

Module 4: DMAIC

Overview of DMAIC
Overview of DMADV
Case study
DMAIC storyboard template

Module 5: Start Your Project

Project Selection Criteria
Team charter
Project Charter
Elements of Project Charter

Module 6: Process Basics

Quality, systems, and processes
Work as a process

Module 7 : Understanding the Voice of the Customer

What is VOC and importance
Defining the customers
VOC data collection plan
Reactive/proactive data collection systems
Analyzing customer data – affinity diagram

Module 8: Understanding VOC

Kano model
Defining CTQs
Setting specifications for CTQs
VOC data collection plan

Module 9: Process

Understanding Processes
SIPOC
Examples on SIPOC

Module 10: Define

Define

Module 11: Measure

Basic statistics and introduction to Minitab
Normal distribution
Mean and standard deviation
P-values

Module 12: Data Collection

How data can help
Data characteristics
Five-step data collection process
Step 1: Clarify Data Collection Goals
- Stratifying data
- Types of data
Step 2: Develop Operational Definitions and Procedures
- Operational definitions
- Sampling approaches
- Determining sample size
- Population versus process samplingChecksheets
Step 3: Validate the Measurement System
- Problems with measurement
- Desired measurement characteristics for continuous variables
- Gage R&R study
- Gage linearity study
- Checking a measurement system for discrete data