| AKA |
N/A |
| Classification |
Evaluating/Selecting (ES) |
The nominal prioritization tool is an easy and quick method for a team to team-prioritize from a list of items, proposed actions, or various options. It can also be used to team-select a particular problem or opportunity from a previously brainstormed list.
To prioritize from a list of brainstormed items or options.
To involve all team
To determine a "
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Select and define problem or opportunity |
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Identify and analyze causes or potential change |
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Develop and plan possible solutions or change |
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Implement and evaluate solution or change |
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Measure and report solution or change results |
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Recognize and reward team efforts |
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Research/statistics |
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Creativity/innovation |
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Engineering |
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Project management |
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4 |
Manufacturing |
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Marketing/sales |
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3 |
Administration/documentation |
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2 |
Servicing/support |
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Customer/quality metrics |
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1 |
Change management |
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before
Brainstorming
Brainwriting Pool
Crawford Slip Method
Double Reversal
Importance Weighting
after
Consensus Decision Making
Starbursting
Different Point of View
Run-It-By
Criteria Filtering
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Optional approach: A team facilitator may provide participants the opportunity to present and explain why they
STEP 1 The team facilitator displays a flip chart with a list of items or options. See example Prioritizing a Data Collection Method .
STEP 2 Team participants are asked to review the entire list and select the top three choices.
STEP 3 Every participant moves to the flip chart and marks the top or most important choice by writing a 3, marks the second choice a 2, and the third choice a 1. All
STEP 4 The team facilitator totals up the scores of all selected items and ranks the top three choices. The highest scored item is, therefore, also what the team considers the most important item.
STEP 5 In the next step, participants give a rationale for selecting their most important choice. This discussion should be limited to 2-3 minutes per participant.
STEP 6 Once all participants have a chance to explain why a particular change was made, participants are now given the opportunity to change their selections.
STEP 7 Lastly, the facilitator retotals, if needed, all selections, and lists the final top three choices on a flip chart and dates the chart.
| AKA |
Gaussian Curve |
| Classification |
Analyzing/Trending (AT) |
The normal probability distribution is used extensively in statistical process control (SPC) applications, the profiling and describing of various data distributions, and in the hypothesis testing procedures (inferential statistics) found in scientific research. The concepts of normally distributed sample data provide the basis for inferences made about a population based on samples taken from the source population.
To
To apply the "normal" pattern concepts to statistical process control activities.
To
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Select and define problem or opportunity |
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Identify and analyze causes or potential change |
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Develop and plan possible solutions or change |
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Implement and evaluate solution or change |
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Measure and report solution or change results |
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Recognize and reward team efforts |
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1 |
Research/statistics |
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Creativity/innovation |
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2 |
Engineering |
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Project management |
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Manufacturing |
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Marketing/sales |
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Administration/documentation |
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Servicing/support |
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3 |
Customer/quality metrics |
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Change management |
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before
Data Collection Strategy
Surveying
Frequency Distribution (FD)
Standard Deviation
Cluster Analysis
after
Descriptive Statistics
Process Capability Ratios
Analysis of Variance
Control Chart
Response Matrix Analysis
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The normal probability distribution is a symmetrical, bell-shaped distribution frequently used in statistical analyses. The arithmetic mean,
median
and mode
are of equal value and are located at the center and peak of the curve. These measures are averages and therefore
Please refer to Appendix, Table A, "Proportions of Area Under the Normal Curve," for a detailed table.
STEP 1 Collect a sample of data for the purpose of checking quality goals, process capability, or probability of defects (excessive variability). See example Normalizing Sample Data for SPC Applications .
STEP 2 Calculate the population mean ( μ ) and standard deviation ( σ ).
STEP 3 Transform any measurement, using the z -score equation as shown in this example.
STEP 4 Refer to the Proportions of Area Under the Normal Curve table to locate the percentage of probability of area under the curve (See Appendix, Table A.)