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

  • Introduction to class
  • Introduction to Anexas

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

  • Estimating process sigma
  • Understanding Variation

  • Overview of DMAIC
  • Overview of DMADV
  • Case study
  • DMAIC storyboard template

  • Project Selection Criteria
  • Team charter
  • Project Charter
  • Elements of Project Charter

  • Quality, systems, and processes
  • Work as a process

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

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

  • Understanding Processes
  • Examples on SIPOC

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

  • 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

  • Time-ordered data
  • Understanding variation
  • Variation versus specifications
  • Taguchi Loss Function
  • Types of variation, appropriate responses
  • Special cause, common cause
  • Plots of variation
  • Control charts
  • Specifications and control limits

  • Develop a focused problem statement
  • Identify potential causes
  • Brainstorming, Five Whys
  • Understand relationships between potential causes
  • Might-cause check
  • Cause-and-effect diagram
  • Tree diagram
  • Prioritizing Input Variables

  • Verifying cause-and-effect data
  • Histograms
  • Scatter plots
  • Pareto Charts
  • Box Plots

  • Process maps
  • Identifying Value-Added path
  • VA/NVA analysis
  • Identifying flow issues

  • 8 wastes
  • Cycle time
  • Bottlenecks
  • Value Stream Mapping (VSM)

  • Involving people
  • Lean solutions
  • Generating solution alternatives
  • Evaluating solutions
  • Prioritization matrix, Pugh matrix
  • Task and timeline planning
  • Budget and resource planning
  • Planning for change
  • Planning to check
  • Potential problem analysis
  • Piloting
  • Implementing the plans

  • Quantifying results
  • Evaluation and reactions

  • Link improvement to management system
  • Methods for monitoring and control
  • Ongoing data collection, process control
  • Variation and individuals charts
  • Control charts for high-volume processes with subgroups: x bar and r charts
  • Control charts for discrete data: p, np, c, and u charts

  • Project closure
  • Closure checklist

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