Lean Six Sigma Flashcards
DMAIC - Define
Prioritize projects based on business impact and alignment with organizational imperatives. Lean Six Sigma projects start by capturing the voice of the customer:
A) Organize a team to improve the process of interest, and develop a Charter to guide team actions.
B) Identify the customer(s) and critical customer requirements (Voice of the Customer).
C) Determine what “success” looks like for the improvement project.
D) Create a process map of the process to develop process knowledge - there are many formats.
DMAIC - Measure
Measurements drive the Lean Six Sigma improvement process - what gets measured gets done!
A) Define performance standards
B) Identify potentially significant Process Inputs (Xs)
C) Validate the measurement system
D) Establish baseline performance with regard to customer requirements (called Capability).
DMAIC - Analyze
Analytical tools are used to dissect the root cause of process variability and separate the vital few inputs from the trivial many.
A) Identify significant characteristics (inputs, or Xs) and establish process capability.
B) Define performance targets for significant characteristics (inputs, or Xs).
C) Identify the root cause of process variation.
D) Use statistical methods to verify the effectiveness of alternatives.
DMAIC - Improve
The Improve phase turns analysis into action.
A) Identify and evaluate potential solutions.
B) Implement short term countermeasures.
C) Implement long term corrective actions.
D) Identify systemic indirect effect and unintended consequences of improvement actions.
E) Establish operating tolerances for new processes.
DMAIC - Control
After implementing improvement actions, the Control step verifies results and consolidates the gains:
A) Verify corrective actions and validate new measurement systems.
B) Determine new process capability.
C) Establish and implement control plan.
D) Share best practices and lessons learned.
SIPOC Map
dentifies Suppliers, Inputs, Outputs, and Customers - High level starting point - used in most DMAIC projects
Flow Chart
Shows decision points and If/Then logic - Displays procedures and logic of process
Deployment Flow Chart
Swimlane Chart
Identifies functional responsibilities - Communicates who does what
Value-Added Flow Chart
Separates Value-Added from Non-Value-Added operations. - Identifies waste of many types. Stratifies time.
Spaghetti Diagram
Shows physical flow of material and/or information. - Illustrates physical complexity, distance traveled, cycle time
Value Stream Map
Identifies physical flow of materials and information. Quantifies inventory levels, process characteristics and control mechanisms. - Detailed map for lean improvement projects and Kaizen events.
System Diagram
Focused on systemic cause and effect - shows reinforcing and balancing forces along with unintended consequences. - Examines behaviors behind process performance - non-linear.
Cause and Effect Matrix
used to prioritize the process inputs (causes, or X’s) that have been identified as potentially contributing to an Effect (Y). This action usually leads to selection of inputs for data collection and subsequent analysis.
Benefits of C & E Matrix
1) The C & E Matrix provides a way to prioritize potential causes based on the number of CTQC’s affected and the strength of the relationship between potential causes (X’s) and effects (Y’s).
2) It appeals to team members who are more comfortable with ranking techniques than visual tools.
3) The matrix also brings together the CTQC’s from the Tree Diagram and the process steps identified during process mapping. This promotes “process thinking” and provides a logical linkage between the tools.
The steps required to complete a Cause & Effect Matrix
Step 1: Place the CTQC’s (from a Tree Diagram) along the left side of the cause and effect matrix. These are the Effects.
Step 2: Rank the importance of each of the Effects, or CTQC’s, from the customer’s point of view. (Note: the template provided below assigns the following values: H=5, M=3, L=1)
Step 3: Place the Process Inputs (X’s) along the top of the matrix. These are the Causes, and they will have been identified through process mapping and brainstorming, possibly with help from a Fishbone Diagram.
Step 4: Assign a correlation factor between the Causes and the Effects based on experience and process knowledge.
Step 5: Evaluate the output and check for consistency.
Step 6: Choose which factors you wish to analyze further. (Note: Overall Input Importance Score is the product of the Importance Ranking x the Correlation Factor)
Stability
refers to the capacity of a measurement system to produce the same values over time when measuring the same sample. As with statistical process control charts, stability means the absence of “Special Cause Variation”, leaving only “Common Cause Variation” (random variation).
Bias, also referred to as Accuracy
is a measure of the distance between the average value of the measurements and the “True” or “Actual” value of the sample or part. See the illustration below for further explanation.
Linearity
is a measure of the consistency of Bias over the range of the measurement device. For example, if a bathroom scale is under by 1.0 pound when measuring a 150 pound person, but is off by 5.0 pounds when measuring a 200 pound person, then the scale Bias is not constant and it will be important to understand if this Bias changes in a linear fashion or not. Linearity may be expressed as an index, or as a percentage of the process variation.
Location (Average Measurement Value vs. Actual Value)
1) Stability
2) Bias, or Accuracy
3) Linearity
Variation (Spread of Measurement Values - Precision)
1) Repeatability
2) Reproducibility
Repeatability
assesses whether the same appraiser can measure the same part/sample multiple times with the same measurement device and get the same value.
Reproducibility
assesses whether different appraisers can measure the same part/sample with the same measurement device and get the same value.
Population
The entire process output, or collection of objects about which we wish to draw a conclusion.
Sample
The subset chosen to represent the population.
Random Sample
A sample drawn such that every member of the population has an equal chance of being selected.
Parameter
A quantity associated with the population. Designated by Greek or capital Roman letters.
Examples: P (proportion), “delta” (average), “sigma” (standard deviation)
Statistic
A quantity calculated from the sample. Designated by lower-case Roman letters.
Example: p (proportion), ybar (average), s (standard deviation).
Descriptive statistics
aim to describe and summarize the important features of a population or process. (i.e. pie, bar and line graphs.)
Inferential statistics
use sample data to help make comparisons among, or draw inferences about the effects of different solutions or treatments on the overall population. When the entire population cannot be measured, a smaller sample of data is used to infer, or estimate, the characteristics of the wider population. (i.e. Regression analysis, hypothesis tests and experimental design)
Enumerative Studies
which aim to answer questions about the current population like “how many?” or “in what proportion?” The focus is historical rather than predictive.
Analytical Studies
seek to answer questions like “why?” or “what are the causes of?” and aim to generalize the results to future states of the population. Analytical studies are conducted to understand the behavior of a process over time with the intention of identifying relationships between cause and effect which could impact future performance (predictive as well as historical perspective).
Levels of the data hierarchy
Level 1: Nominal
Level 2: Ordinal
Level 3: Counts
Level 4: Continuous
Discrete measurement, often termed as Attribute data
Sort or count items based on attributes, such as the presence/absence of defects, quality perceptions, occurrence of an event, etc.
Continuous measurements, often termed Variable data
Can take on infinite values within any two fixed points
Discrete Nominal
Groups are labels, no order: (i.e., profession, region, color, type of defect, etc.
Discrete Ordinal
Groups are a logical order: (i.e., small, medium, large)
Discrete Counts
Number of items or events: (i.e., accidents, sales per week, errors on page, etc.)
Continuous
Measurements are made along a continuum: (i.e., gas milage, concentration in a sample, speed of pitch, etc.)
Genichi Taguchi’s - Quality Loss Function
stipulates that the total loss to society from poor quality increases in a geometric fashion as variability increases.
Efficiency metrics
measure the amount of input necessary to achieve a given output.
Effectiveness metrics
a direct measure of how well customer expectations are met.
Xbar & S Chart
Xbar & S charts allow you to track both the process level and process variation at the same time, as well as detect the presence of special causes.
Xbar & R Chart
Xbar & R charts allow you to track both the process level and process variation at the same time, as well as detect the presence of special causes.
XmR or I & MR Chart
XmR charts allow you to track both the process level and process variation at the same time, as well as detect the presence of special causes.
C Chart
C charts track the number of defects and detect the presence of special causes.
Used to chart thenumber of defectswhen the subgroup size isconstant(although usually greater than 50). This chart assumes that the process has aPoisson distribution.
nP Chart
nP charts track the number of defectives and detect the presence of special causes.
Used to chart thenumber of defective unitswhen the subgroup size isconstant(usually greater than 50). This chart assumes that the process has abinomial distribution.
U Chart
U charts track the number of defects per unit sampled and detect the presence of special causes.
Used to chart thenumber of defects per unitwhen the subgroup size isvariable or constant. This chart assumes that the process has aPoisson distribution.
P Chart
P charts track the proportion defective and detect the presence of special causes.
Used to chart thefraction defectivewhen the subgroup size isvariable or constant(although usually greater than 50). This chart assumes that the process has abinomial distribution.
Application of Multiple Proportion Chi-Squared Test
This type of inference is used during the Analyze or Improve phases of a Lean Six Sigma project when it is desired to know if there is a difference among multiple groups with respect to a binary (Yes/No) characteristic or attribute.
