deviation is reduced through process improvement, or the limits are opened up after negotiations with customers, such that the tolerances are higher than the 3 Sigma around the mean, then the predictable yield will be 99.9 percent or higher. It is at this point that the benefits of reduced inspection and testing become visible.
found that most events in nature are not equal. For example, most revenues of a company come from a few large accounts, most of the deaths occur due to a few diseases, and most problems in an organization stem from just a few causes.
The Pareto chart is designed to help businesses identify opportunities for improvement that cost more than the others and thus should be attacked first. The Pareto chart shows opportunity categories based on their impact or frequency.
People tend to work first on opportunities that are easier to attack rather than those most important to attack. The purpose of using the Pareto chart is to promote work on important opportunities rather than on the easy ones.
Cause-and-Effect Analysis. Once the most significant opportunities have been identified, a root cause analysis is performed. The cause-and-effect diagram is one tool used to diagnose the causes of a selected problem. Most failures are caused by problems with the machine, material, method, or mind (skills). In addition, the environment and measurement devices may cause failures. The cause-and-effect diagram is a great way to list potential causes. Once causes are listed, a cross-functional team can prioritize various causes and select a few on which to work. The cause-and-effect diagram is also called the Fishboneor Ishikawa diagram.
As shown in Figure 2-11, the main branches can be rela- beled according to the categories of causes to be investigated.
In the case of financial losses, categories such as machines, methods, and materials might not be the appropriate cate- gories to represent potential causes. In such cases, other caus- es can be fitted into the standard branches, or the branches can be relabeled.
Multivary Analysis. Multivary analysis is an excellent tool to apportion variance in the area where opportunities for improvement exist. It dissects the variance into positional, cyclical, and temporal categories. The positional variation is caused by the variables that affect the process performance at certain locations within the process or the product. The tem- poral variation is attributed to the changes between cycles of a process and represents trends over time, i.e., shift to shift, day
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to day, or week to week.
Apportioning the variation in a process puts focus on the variables related to a particular type of variation. For exam- ple, the positional variation is normally attributed to product or process design, as the defects recur at certain locations.
The cyclical variation is attributed to the variables related to the process setup that cause variation in performance from one process cycle to the next. The temporal variation can be related to the maintenance activities, whether daily, weekly, or monthly, as well as degradation in consumable items in the process, such as laser lamps, machine tools, chemical concentrations, or the limited shelf life of a chemical.
FMEA. Failure Mode and Effects Analysis(FMEA), as shown in Figure 2-12, is an excellent tool that has been used in mainly the automotive and aerospace industries, or where per- sonnel safety is a concern. As implied by the name, FMEA is used to anticipate potential failure modes during the product or process design or redesign, to determine the effects of fail- ure modes on performance, and to identify action items that will prevent anticipated failure modes. Each failure mode is ranked for severity of the effect on performance, frequency of
SIXSIGMA—ANOVERVIEW 33
Environment Methods Machines
Materials Personnel Measurements
Excessive Inventory
Incorrect Information
Excessive Office Space
No Training Program
Ineffective Recruiting Poorly Defined Goals
Financial Losses in a Business
Bad Economy
Hidden Costs Higher Breakeven
No Vision/Policies Old Equipment
Too Much Equipment
Long Setups FIGURE 2-11. Cause-and-effect diagram.
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34 CHAPTERTWO
Description of
Protection:The spreadsheets
System Potential
Failure Mode and (Design Subsystem
Component
Design Lead Key Date
Core Team
Item / Function Potential Failure Mode(s)
Potential Effect(s) of Failure
S e v
Potential Cause(s)/
Mechanism(s) of Failure
P r o b
Current Design Controls
Coolant containment.
Hose connection.
Coolant fill.
Crack/break.
Burst. Sidewall flex. Bad seal.
Poor hose retention
Leak 8 Over pressure 8 Burst, validation pressure cycle.
Likelihood - Write down the potential cause(s), and on a scale of 1–10, rate the Likelihood of each failure (10 = most likely). See Likelihood sheet.
Severity - On a scale of 1–10, rate the Severity of each failure (10 = most severe). See Severity sheet.
Write down each failure mode and potential consequence(s) of that failure.
FIGURE 2-12. Failure mode and effects analysis template.
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SIXSIGMA—ANOVERVIEW 35
FMEA Worksheet
are not protected or locked.
Effects Analysis FMEA)
FMEA Number Prepared By FMEA Date Revision Date
Page of
Action Results D
e t
R P N
Recommended Action(s)
Responsibility and Target Completion Date
Actions Taken
1 64 Test included in prototype and production validation testing.
J.P. Aguire 11/1/95 E. Eglin 8/1/96
Response Plans and Tracking
Risk Priority Number - The combined weighting of Severity, Likelihood, and Detectability.
RPN = Sev X Occ X Det
Detectability - Examine the current design, then, on a scale of 1–10, rate the Detectability of each failure (10 = least detectable). See Detectability sheet.
New RPN
New Det
New Occ
New Sev
FIGURE 2-12. (Continued) Failure mode and effects analysis template.
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occurrence of its cause, and detection of the failure mode based on the effectiveness of the control methods. A risk priority number (RPN) is calculated by multiplying the ranking for severity, occurrence, and detection. The RPN is used to priori- tize the failure modes and corrective actions related to the failure modes.