part v s4/iee control phase from dmaic and application...

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Part V

S4/IEE Control Phase

from DMAIC

and Application Examples

Introduction

• This part (Chapters 34-44) addresses the implementation

of a control plan, along with other DMAIC tools.

• Project success at one point in time does not necessarily

mean that the changes will stick after the project leader

moves on to another project. Because of this. the control

phase is included in DMAIC.

• This part also describes engineering process control, 50-

foot-level control charts, pre-control, and error-proofing

(poka-yoke).

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Introduction

• This part also discusses reliability assessments, pass/fail

functional testing, and application examples, which have

broader implementation possibilities than often initially

perceived.

• Within this part of the book, the DMAIC control steps,

which are described in Section A.1 (part 5) of the

Appendix, are discussed.

• A checklist for the completion of the control phase is:

Introduction

Control Phase Checklist

Description Question

Tool/Methodology

Process Map/

SOPS/

FMEA/

Mistake Proofing

Control Plan

• Were process changes and procedures documented with

optimum process settings?

• Were mistake proofing options considered?

• Were control charts created at the 50-foot level on

appropriate KPlVs?

• Was the appropriate control chart used for the input

variable data type?

• Is the sampling plan sufficient?

• Is a plan in place to maintain the 30,000-foot-level control

chart using this metric and the process capability/

performance metric as operational metrics?

• Has the responsibility for process monitoring been assigned?

• Is there a reaction plan for out-of-control conditions?

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Introduction



Tool/Methodology

Assessment • Were any process improvements made?

• lf so, were they statistically verified with the appropriate

hypothesis tests?

• Did you describe the change over time on a 30,000-foot leveI

control chart?

• Did you calculate and display the change in process capability/

performance metric (in units such as ppm)?

• Have you documented and communicated the improvements?

• Have you summarized the benefits and annualized financial

benefits?

Introduction



Tool/Methodology

Communication

Plan

• Has the project been handed off to the process owner?

• Are the changes to the process and improvements being

communicated appropriately throughout the organization?

• Is there a plan to leverage project results to other areas of the

business?

Team

Team Members • Were all contributing members of the team acknowledged and

thanked?

Change

Management

• Has the team considered obstacles to making this change last?

• Has the project success been celebrated?

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Introduction



Next Phase

Final Approval

and Closure

• Is there a detailed plan to monitor KPIV and KPOV metrics over

time to ensure that change is sustained?

• Have all action items and project deliverables been completed?

• Has a final project report been approved?

• Was the project certified and the financial benefits validated?

• Has the project database been updated?

Chapter 34

Short-Run and

Target Control Charts

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Introduction

• A control chart is often thought to be a technique to control

the characteristics or dimensions of products, the thought

being that a controlled process will yield products that are

more consistent.

• It is typically more useful to focus on key product

characteristics and their related process parameters than on

a single product characteristic.

• Brainstorming techniques can help with this selection

activity.

• The most effective statistical process control (SPC) program

uses the minimum number of charts and at the same time

maximizes their usefulness.

Introduction

• General application categories for short-run charts include the

following:

• Insufficient parts in a single production run

• Small lot sizes of many different parts

• Completion time that is too short for the collection and

analysis of data, even though the production size is large

• If standard control limits are used when there are only a small

number of subgroups, there is a greater likelihood of

erroneously rejecting a process that is actually in control.

• Pyzdek (1993) includes tables that can be used to adjust

control limits when there are a small number of subgroups.

The examples included here do not consider this adjustment.

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34.1 S4/IEE Application Examples:


• Transactional 30,000-foot-level metric: An S4/IEE project

was to reduce DSO for invoices. Instead of total DSO for an

invoice, could make comparison to different due dates.

• Transactional and manufacturing 30,000-foot-level metric:

An S4/IEE project was to improve customer satisfaction for a

product or service. Could combine satisfaction surveys that

have different criteria or scales (1-5 versus 1-10).

34.1 S4/IEE Application Examples:


• Manufacturing 30,000-foot-level metric: An S4/IEE project

was to improve the process capability/performance metrics

for the diameter of a plastic part from an injection-molding

machine. Could compare different diameter parts off the

same molding machine by plotting differences relative to

center of specification.

• Product DFSS: An S4/IEE project was to improve the

process capability/performance metrics for the number of

daily problem phone calls received within a call center. Can

record differences to optimum number of calls that could

handle because of different call center sizes.

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34.2 Difference Chart

(Target Chart / Nominal Chart)

• Difference charts (also known as target chart and nominal

chart) permit the visualization of underlying process even

though it has short runs of differing products.

• The nominal value that is to be subtracted from each value

observed is specific to each product. This value can either

be a historic grand mean for each product or a product

target value.

• Specification targets depend upon the type of specification.

Symmetrical bilateral tolerances such as 1.250 0.005

would have the nominal value as the target. Unilateral

tolerances such as 1.000 maximum could have any desired

value-for example, 0.750.

34.2 Difference Chart

(Target Chart / Nominal Chart)

• Historical targets focus on the actual target value of the

process with less emphasis on specifications.

• Applications include situations where the target value is preferred over the specification or there is a single specification

(maximum or minimum) limit. • General application rules of the difference chart are:

• Constant subgroup size

• Twenty data points for control limits

• Same type of measurement • Similar part-to-part range

• If the average ranges for the products are dramatically different or the types of measurements are different, it is better to use a

Z chart.

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34.3 Example 34.1:

Difference Chart

• Table 34.1 shows the measurements from three parts designated as a, b, and c that have different targets. The

subgroup measurements for each part are designated as M1, M2, and M3. Measurement shifts from the target are

designated as M1 shift, M2 shift, and M3 shift. The method for

calculating these control limits is similar to that for typical 𝑥 and

𝑅 charts. • The control chart in Figure 34.1 indicates that the process is in

control/predictable. Process capability/performance metric assessments can also be made from these data.

34.3 Example 34.3 Difference Chart

Seq. Part Target M1 M2 M3 M1Shift M2Shift M3Shift 𝑥 R

1 a 3.250 3.493 3.496 3.533 0.243 0.246 0.283 0.257 0.040

2 a 3.250 3.450 3.431 3.533 0.200 0.181 0.283 0.221 0.102

3 b 5.500 6.028 5.668 5.922 0.528 0.168 0.422 0.373 0.360

4 b 5.500 5.639 5.690 5.634 0.139 0.190 0.134 0.154 0.056

5 b 5.500 5.790 5.757 5.735 0.290 0.257 0.235 0.261 0.055

6 b 5.500 5.709 5.743 5.661 0.209 0.243 0.161 0.204 0.082

7 c 7.750 8.115 7.992 7.956 0.365 0.242 0.206 0.271 0.159

8 c 7.750 7.885 8.023 8.077 0.135 0.273 0.327 0.245 0.192

9 c 7.750 7.932 8.078 7.958 0.182 0.328 0.208 0.239 0.146

10 c 7.750 8.142 7.860 7.934 0.392 0.110 0.184 0.229 0.282

11 c 7.750 7.907 7.951 7.947 0.157 0.201 0.197 0.185 0.044

12 c 7.750 7.905 7.943 8.091 0.155 0.193 0.341 0.230 0.186

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34.3 Example 34.3 Difference Chart

34.4 Z Chart (Standardized Variables Control Chart)

• With this Z chart, multiple processes can be examined at the

same time.

• A chart can even be set up to track a part as it goes through

its manufacturing operation.

• This charting technique can be used to monitor the same

chart measurements that have different units of measure

and standard deviations. The control limits are also fixed, so

that they never need recomputing (the plot points are

standardized to the limits, typically 3 units).

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• Caution should be exercised when applying these charts

because more calculations are required for each point.

• In addition, they require frequent updating of historical

process values.

• Also, the value that is tracked on the chart (𝑍 value) is not

the unit of measure (e.g., dimension of a part), and the user

can become distant from individual processes.

• This charting technique is based on the transformation

𝑍 =𝑆𝑎𝑚𝑝𝑙𝑒 𝑠𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐 − 𝑃𝑟𝑜𝑐𝑒𝑠𝑠 𝑎𝑣𝑒𝑟𝑎𝑔𝑒

𝑃𝑟𝑜𝑐𝑒𝑠𝑠 𝑠𝑡𝑎𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛

• The method can apply to both attribute and continuous data,

but only the 𝑍𝑚𝑅 chart will be shown here.


• Short-run charts can pool and standardize data in various ways. The most general way assumes that each part or batch produced by a process has a unique average and standard deviation. If the average and standard deviation can be obtained, the process data can be standardized by subtracting the mean and dividing the result by the standard deviation.

• When using a 𝑍𝑚𝑅 chart, consider the following when determining standard deviation: • When all output has the same variance regardless of

size of measurement, consider using a pooled estimate of the standard deviation across all runs and parts to obtain a common standard deviation estimate.

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• When the variance increases fairly constantly as the measurement size increases, consider using a natural log transformation to stabilize variation.

• When runs of a particular part or product have the same variance, consider using an estimate that combines all runs of the same part or product to estimate standard deviation.

• When you cannot assume that all runs for a particular product or part have the same variance, consider using an independent estimate for standard deviation from each run.

34.5 Example 34.2: 𝑍𝑚𝑅 Chart

• The observations were taken in a

paper mill for different grades of

paper made in short runs.

• The ZmR chart was calculated

where standard deviation was

determined by pooling all runs of

the same part.

• The chart shows no out-of-control

condition. MeanA 1.7847

StddevA 0.0673

MeanB 1.5015

StddevB 0.0527

MeanC 1.3917

StddevC 0.0429

Seq. Grade Thickness Z 1 B 1.435 -1.2628 2 B 1.572 1.3388 3 B 1.486 -0.2943 4 A 1.883 1.4603 5 A 1.715 -1.0346

6 A 1.799 0.2129 7 B 1.511 0.1804 8 B 1.457 -0.8450 9 B 1.548 0.8830

10 A 1.768 -0.2475 11 A 1.711 -1.0940 12 A 1.832 0.7029 13 C 1.427 0.8245 14 C 1.344 -1.1123 15 C 1.404 0.2878

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34.5 Example 34.2: 𝑍𝑚𝑅 Chart

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