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The Need for Total Quality Control (Six Sigma and Statistical Tools), Part 2

To read part one of this series, click here.

Six Sigma

Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving toward six standard deviations between the mean and the nearest specification limit) in any process—from manufacturing to transactional, and from product to service.

The statistical representation of Six Sigma describes quantitatively how a process is performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A Six Sigma opportunity is then the total quantity of chances for a defect.

The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects. This can be accomplished through two Six Sigma sub-methodologies: Business Process Management Systems (BPMS) and Six Sigma Improvement Methodologies: DMAIC and DMADV (Figure 1).

The Six Sigma DMAIC process (define, measure, analyze, improve, control) is an improvement system for existing business processes falling below specification and looking for incremental improvement. The Six Sigma DMADV process (define, measure, analyze, design, verify) is an improvement system used to develop new processes, products, or defining customer needs at Six Sigma quality levels. It is also called Design for Six Sigma (DFSS). It can also be employed if a current process requires more than just incremental improvement. Both Six Sigma processes are executed by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six Sigma Master Black Belts.

According to the Six Sigma Academy, black belts save companies approximately $230,000 per project and can complete four to six projects per year. General Electric, one of the most successful companies implementing Six Sigma, has estimated benefits on the order of $10 billion during the first five years of implementation. GE first began Six Sigma in 1995 after Motorola and Allied Signal blazed the Six Sigma trail. Since then, thousands of companies around the world have discovered the far-reaching benefits of Six Sigma.

Many frameworks exist for implementing the Six Sigma methodology. Six Sigma consultants all over the world have developed proprietary methodologies for implementing Six Sigma quality, based on the similar change management philosophies and applications of tools. A partial list of 18 framework and methodologies are listed here. Eight will be detailed in this and future columns in this magazine. Additionally, I have include 37 tools and templates in this column and seven more will be detailed in the future. Definitions and examples of all are available at the Six Sigma website[1].

Frameworks and Methodologies

•  Balanced Scorecard
•  Benchmarking*
•  Business Process Management (BPM)*
•  Design for Six Sigma (DFSS)*
•  DMAIC
•  Harada Method
•  Hoshin Kanri
•  Innovation
•  Kaizen
•  Lean*
•  Metrics*
•  Plan, Do, Check, Act*
•  Project Management*
•  Robust Design/Taguchi Method
•  Theory of Constraints
•  Total Quality Management (TQM)*
•  VOC/Customer Focus
•  Work-out

Figure 1: Six-Sigma process improvement through the DMAIC and DMADV methods (also called an affinity diagram): define, measure, analyze, improve/design, control/verify.

Six Sigma Tools & Templates[1]

• 5 Whys
• 5S
• Affinity Diagram/KJ Analysis*
• Analysis of Variance (ANOVA)
• Analytic Hierarchy Process (AHP)
• Brainstorming*
• Calculators
• Capability Indices/Process Capability
• Cause & Effect (fishbone)
• Control Charts
• Design of Experiments (DOE)*
• FMEA*
• Graphical Analysis Charts
• Hypothesis Testing
• Kanban
• Kano Analysis
• Measurement Systems Analysis (MSA)/Gage R&R
• Normality
• Pareto
• Poka Yoke
• Process Mapping
• Project Charter
• Pugh Matrix
• QFD/House of Quality*
• RACI Diagram
• Regression*
• Risk Management
• SIPOC/COPIS
• Sampling/Data
• Simulation
• Software
• Statistical Analysis*
• Surveys
• Templates
• Value Stream Mapping
• Variation
• Wizards

Statistical Methods

The Need for Statistical Tools

The discussion of quality and customer satisfaction show how important yields are to printed circuit boards. Any loss goes to the bottom line. So what are some of the tools to help improve process yields? Process control comes to mind. Chemical processes have always been difficult to control in printed circuits. These uncontrolled factors can always creep into our processes.

All process control is a feedback loop of some sort. Nevertheless, the element that I want to focus on is the control block, or more precisely, the human decisions that make up process control.

Process Control

The first link in process control is the human link. The high-level objectives are to:

• Reduce variations
• Increase first pass yields
• Reduce repair and rework
• Improve quality and reliability
• Improve workmanship

The process control tools and methods that a person may have to work with have been listed already. Of particular importance for the engineer are the statistical tools, as seen in Figure 2. Traditionally, statistical tools have been rather cumbersome and not easy to learn. I have good news: You can now get a good statistics training from the Web, at everyone’s favorite price— free.

Even if your company has good statistical software available, like mine did with Minitab, it is only available as long as you work there. By downloading the NIST/SEMATECH e-Handbook of Statistical Methods[2] and the software Dataplot, you have an equally good tool at home that can travel with you wherever you may work. Your next job may not have any statistical tools!

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