The purpose of the next phase—to measure—is to describe the opportunity for improvement and quantify the baseline perfor- mance. When changes are made for improvement, then the business can verify the effectiveness of the changes. To analyze data, basic statistical techniques such as averages, standard
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Commitment Management Purchasing Engineering Production Sales
Passionate Needed Needed
Positive Needed Needed
Neutral Available Available
Resistance Available Available
Destructive
FIGURE 2-6. Commitment matrix.
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deviation, and probability distributions (i.e., the Normal distrib- ution and the Poisson distribution) are critical for understanding the nature of excessive variation in the process.
Variation. W. Edwards Deming said that “variation is evil.”
This premise underlies the method for achieving dramatic improvement in any process. Walter Shewhart classified varia- tion as random or assignable. Deming called it common or special variation. The nature of variation depends upon its causes, which could be random or assignable.
Randomcauses of variation, such as ambient temperature, supplier-to-supplier variation in parts, or operator-to-operation variation, are inherent in the process. Assignable causes are those that change for specific reason, such as machine break- down, an untrained operator, use of the wrong material, an incorrect setup, or some design-related issue. The random causes are difficult to diagnose, and many of them act concur- rently, while the assignable causes are known, specific, and introduced in the process. In statistical terms, random causes are the ones that are more likely to happen as a routine (about 95 percent of the time), while assignable causes occur less often (about 5 percent of the time) and are exceptions. In developing statistical thinking, it is not as important to learn many statis- tical techniques as it is to understand the nature of variation.
Cost of Quality. Another measure of performance is the cost of quality. The traditional cost of quality consists of four cate- gories: internal failures, external failures, appraisal, and pre- vention. The goal is to increase the preventive cost of quality and to reduce the internal failures, external failures, and appraisal components. Typically, not all the costs of poor quality are measured in a company’s accounting system; therefore, it takes both effort to understand and courage to measure accu- rately the cost of poor quality.
Generally, management’s initial reaction is that the cost of poor quality is not significant. For example, a company wants to improve customer satisfaction by reducing the number of defects reaching customers, so it adds inspection and test points. Over time, this inspection and testing becomes a stan- dard process. This process, however, is an activity that doesn’t
SIXSIGMA—ANOVERVIEW 27
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add value to the product. The goal, then, is to reduce the level of inspection or testing as much as possible, as it is a wasteful activity. Figure 2-7 lists some measures of the cost of poor quality (COPQ) that must be targeted for reduction.
The objective must be to reduce COPQ and increase investment in the prevention costs. Figure 2-8 shows typical ratios of the cost of poor quality, illustrating that a lot more effort must be committed to improve the prevention cost.
Surveys have found that employee training is still the best investment to make in order to add value.
Measurement System Analysis (MSA) is a method used to assess repeatability and reproducibility of the measurement method. The goal is to ensure that the measurement system does not add to excessive variability, thus leading to false con- clusions and, therefore, false starts. Normally, it is known as Gage Repeatability and Reproducibility (Gage R&R). The Gage R&R can be performed on any measurement method to appor- tion variances. The measurement method must have resolution an order of magnitude higher than the measurement itself.
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Internal Failures
External
Failures Appraisal Prevention
Failure Reviews Customer Dissatisfaction
Drawing Checking
Planning
Redesign Equipment
Downtime
Final Inspection Capability Studies Reinspection Excess Inventory In-Process
Inspection
Design Reviews Repair Costs Excess Travel
Expense
Laboratory Testing
Field Testing Retesting Excess Material
Handling
Personnel Testing
Vendor Surveys and Evaluation
Rework Penalties Receiving
Inspection
Procedure Writing Scrap
Allowances
Pricing Errors Product Audits Training Engineering
Changes
Shipping Inspection
Market Analysis
FIGURE 2-7. Cost of poor quality (COPQ) measures.
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Six Sigma Measurements. A defect is defined as any attribute of a product that does not provide total customer satis- faction. The Six Sigma methodology measures defects in two key ways: Defects per Unit (DPU) and Defects per Million Opportunities (DPMO). A unit is defined as the output of a process. For example, a unit for the accounts payable depart- ment may be an invoice; for the assembly area it might be a sub- assembly; and for the packaging department, it may be a package that will be shipped to the customer. The DPU can be calculated as follows:
DPU
The DPU measurement uses total defects instead of total defective units. For example, when a cell phone is inspected and five defects are found, all defects must be counted, recorded, and included in the DPU calculation. Once the DPU is calculated, the first-pass yield can be calculated according to the following formula:
First-pass yield eDPU
Normally, the yield is calculated according to the number of good units produced over the total units started. If utilized correctly, the yield number will appear more accurate.
However, because the focus for improvement is on defects or errors, the number of defects must be measured; they point to the opportunities for improvement.
Total number of defects
Total number of units inspected or verified
SIXSIGMA—ANOVERVIEW 29
COPQ Category Estimated Contribution, %
Internal Failures 25 – 40
External Failures 25 – 40
Appraisal 10 – 50
Prevention 0.5 – 5
FIGURE 2-8. Cost of poor quality (COPQ) contributions.
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When using measurement or variable data, one needs to look at the distributions of data and utilize an appropriate dis- tribution to determine probabilities of producing good product within specifications. The following steps can be used to predict yields based on the variable data:
STEP 1. Gather variable data.
STEP 2. Calculate the average and the standard deviation.
STEP 3. Calculate the probability of producing the product within specification, using the Normal distribution table (in any business statistics book or software).
STEP 4. Add probabilities of producing the product within specification on both sides of the target.
STEP 5. Subtract from 100 to determine the defect rate. The defect rate can be converted to parts per million.
For example, if the process, as shown in Figure 2-9, demonstrates standard deviation such that the 3 Sigma dis- tance is equal to the specification limits, then the 99.73 percent process output will be acceptable. If, however, the standard
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68.26%
95.44%
99.73%
Upper Specification Limit Lower Specification Limit
FIGURE 2-9. Probability of producing products within limits at specified standard deviations.
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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.