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Six Sigma Primer

Overview

Six Sigma is a business management strategy originally developed by Motorola, USA in 1986.  It improves the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in processes. It uses a set of quality management methods, including statistical methods. Each Six Sigma project is carried out within an organization follows a defined sequence of steps and has quantified financial targets.  The maturity of a process can be described by a sigma rating indicating its yield, or the percentage of defect-free products it creates.  A six sigma process is one in which 99.99966% of the products manufactured are statistically expected to be free of defects (3.4 defects per million).

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The DMAIC method

Define the problem, the voice of the customer, and the project goals.  Some of the deliverable at this stage are:

  • Project Contract

  • Gantt Chart

  • Financial Savings Review

  • Voice of the Customer

  • Stakeholder Analysis

  • Affinity Diagram

  • Critical To Quality Linkage

  • SIPOC Diagram

  • Pareto Diagram

  • Kano Model

  • Spaghetti Diagram

  • Process Maps

  • Surveys

  • Data Collection

  • Data Classification

Measure key aspects of the current process and collect relevant data.  Some of the deliverable at this stage are:

  • Process Maps

  • Data Sampling - Population vs. Sample, Classification

  • Charts to assess Measurement System Stability

  • Measurement System Analysis

  • Statistical Process Control Charts

  • Root Cause Analysis

  • 5-WHY

  • Fishbone Diagram (Ishakawa Diagram / Cause and Effect Diagram)

  • Correlation Matrix

  • FMEA - Failure Mode Effects and Analysis

  • Overall Equipment Effectiveness

  • Spaghetti Diagram

  • Baseline Measurement

  • Defects per Unit

  • Defects per Opportunity

  • Defects per Million Opportunities

  • Process Yield Metrics

    • Final Yield 
    • Throughput Yield 
    • Rolled Throughput Yield 
    • Normalized Yield 
  • The meaning and formula around sigma scores

    • Sigma, Z-statistic, Z-value 
    • Cost of Poor Quality

Analyze the data to investigate and verify cause-and-effect relationships. Determine the relationships, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.

 

Improve or optimize the current process based data analysis, and standard work to create a new, future state process. Set up pilot runs to establish process capability.

 

Control the future state process to ensure that any deviations from target are corrected before they result in defects and implement control systems

The SIPOC model

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