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Analyze Flashcards

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1
Q

What question does the ANALYZE phase answer?

A

What causes the problem?

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2
Q

What methods are used to collect potential causes & develop hypotheses?

A

Fishbone, FEMA, 5-why, GEMBA

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3
Q

What are the parts of a Fishbone? Describe each part.

A

6 bones (‘M’s’) + Head

Machined
Measure
Man
Mother Nature
Method
Material

Head - Why don’t we fulfill CTC’s?

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4
Q

What are the steps of the fish-bone process?

A

Complete fishbone - brainstorm & cluster ideas (categories)
Rank ideas - Constant (c), Noise (n), Variables (x)
5-why

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5
Q

What is the result of fishbone & 5-why?

A

Hypothesis & potential causes

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6
Q

What is used to prioritize potential causes?

A

Tool 3

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7
Q

What is Tool 3?

A

Table to prioritize potential caused determined from fishbone, 5-why, etc.

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8
Q

What are the parts of Tool 3? What is the process for using it?

A

Table: Output measurements vs. potential causes

Prioritize each
- 9 High Impact
- 3 Med Impact
- 1 Low Impact
- 0 No Impact

Sum up each column.
Creating ranking based on sum

Handle the ‘verification’ process - this is the Process door & Data door.
Review results from process & data door.
Determine true root cause

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9
Q

What is GEMBA? What are the steps?

A

An opportunity to capture topics & concerns related to how effectively the process is performing. Purpose is to allow managers/leaders to observe the actual work process, engage with employees, gain knowledge about the work process, and explore opportunities for continuous improvement.

A concept that can be used in any part of the DMAIC cycle or anywhere that value is created.
1. Choose process.
2. Determine hypothesis.
3. Measurements & Data
4. Observations (often video taped)
5. Insights & conclusions.

Rules
1. Go & see
2. Ask why
3. Respect people

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10
Q

What are ways to ‘verify’ potential causes through process (process door)?

A

Process mapping
Value analysis
Time analysis
Capacity analysis

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11
Q

What are the ways to ‘verify’ potential causes through data (data door)?

A

Data Stratification
Hypothesis testing
Design of Experiment (DOE)

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12
Q

What are the different types of processing mapping?

A

Flow charts
Swimlanes
Spaghetti diagrams
Value stream diagrams

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13
Q

What are the different types of value analysis? Describe each type.

A

Value adding - customer pay
Non-value adding - waste
Value enabling - company pay

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14
Q

What is the tool used to identify waste in a process? Describe the tool.

A

TIM WOODS

Transport
Inventory
Motion
Waiting
Over Production
Over Processing
Defects
Skills

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15
Q

Describe the ‘Data Stratification.’

A

Find graphical evidence that a potential cause (hypothesis) may be valid by splitting the data into logical subcategories - what, when, how, where, who?

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16
Q

What are the calculations for the ‘Time Analysis’ verification step (process door)?

A

Determine process lead time (PLT).
Determine exit rate (ER).
Determine work in process (WIP)
Calculate Little’s Law (WIP/ER)
Calculate process cycle efficiency (PE).

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17
Q

What is Process Lead Time? What is its symbol? How is it calculated?
(Note: Little’s Law is a separate question)

A

PLT = sum of all process time
E.g waiting, processing, setup/change-over time, transportation, inspection, etc.

2 options
1. Measure
2. Little’s Law

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18
Q

What is the exit rate? How it is calculated? What is its symbol?

A

Exit rate calculates the number of pieces over time for that process.
ER = # pieces/time
Determined by the process step with the lowest capacity (aka the bottleneck)

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19
Q

What is WIP?

A

Work in Progress - material waiting to be finished in the process.

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20
Q

What is Little’s Law?

A

A way to calculate Process Lead Time (PLT).
PLT = WIP (item) / ER (item/time)

Concept - reduce either WIP or ER to improve process.

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21
Q

What is the Basic Principle of Hypothesis Testing?

A

Compare the confidence intervals of different data sets to see if there is a significant difference.

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22
Q

If you compare the confidence intervals of 2 different sets of data and the data sets overlap, what does that tell you? What if they do not overlap?

A

Overlap = no significant difference in the data sets
Gap = significant difference in the data sets

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23
Q

How is Process Cycle Efficiency calculated? What is its symbol?

A

PE = VA/PLT

VA = total processing time of all value-adding steps. To get this, evaluate the process steps using value analysis. Reflection of waste level.

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24
Q

What are the primary parts of Capacity Analysis (process door)?

A

Takt rate calculation
Takt time calculation
Understanding bottlenecks & constraints

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25
What is Takt Rate? What is its symbol? How is it calculated?
TR = # units to be produced per customer need / available production time
26
What is Takt Time? What is its symbol? How is it calculated?
TT = Available production time / # units to be produced per customer need
27
True/False: If the Exit Rate is bigger than the Takt Rate, the process can satisfy customer demand.
True
28
True/False: If the Exit Rate is smaller than the Takt Rate, the customer demand can be fulfilled?
False
29
What is the difference between bottlenecks & contraints?
Bottleneck: slowest process step; there is always a bottleneck Constraints: if the bottleneck prevents fulfilling customer demand
30
What are the different types of hypothesis options for hypothesis testing? What do the mean?
Ho - Null Hypothesis - no significant difference HA - Alternative Hypothesis - significant difference
31
When selecting Ho or Ha during hypothesis testing, the decision can be accepted or rejected based on the p-value. How can one understand the risk of making an incorrect selection?
Alpha error: rejection of null hypothesis although it is correct; max accepted risk is 5% Beta error: failure to reject the null hypothesis although it is wrong
32
What are the stratification factors for 'Data Stratification'?
Who? When? Where? What? How?
33
What is the purpose of hypothesis testing (data door)?
To tell if we are right or wrong about our hypotheses. To check if the differences in our data are statistically significant.
34
Describe the 2 types of 'First Leads for Hypotheses.'
Comparison Hypotheses - e.g. Kaitlyn shoots shorter than Matt. The... The... Hypotheses - e.g. The higher the tension, the further the distance.
35
How can we describe the Null Hypothesis?
Ho = Null Hypothesis Everything is equal (comparison hypotheses) Everything is free of influence (the... the... hypotheses) Everything is free of significance (regression model)
36
True/False: You can prove equality or freedom of the Null Hypothesis?
False. You cannot prove equality or freedom, so instead you prove the alternative, aka 'Reject the Null Hypothesis.'
37
How can we describe the Alternative Hypothesis?
Ha = Alternative Hypothesis It is not equal. It is not free of influence. It is not free of significance.
38
True/False: You can prove an alternative hypothesis not equal.
True. You can prove prove Ha. If Ha is valid, then Ho can be rejected.
39
In hypothesis testing, how would you describe the risk you are willing to take?
In statistics, accepted alpha-error is 5%, which is the max accepted risk to reject Ho wrongly.
40
What does it tell you if the p-value is smaller than the alpha-level?
Shows the actual risk of rejecting Ho wrongly.
41
Hypothesis Test Overview: What tests for the below? Target Value (y) = discrete Influencing Value (x) = discrete
1-proportion test 2-proportion test chi^2 test
42
Hypothesis Test Overview: What tests for the below? Target Value (y) = continuous Influencing Value (x) = discrete
1-sample t-test 2-sample t-test 1-way ANOVA 2-way ANOVA 2-variances test --> F-test or Levine's test Test for equal variances --> Bartlett's or Levine's test
43
Hypothesis Test Overview: What tests for the below? Target Value (y) = continuous Influencing Value (x) = continuous
Correlation Regression
44
Hypothesis Test Overview: What tests for the below? Target Value (y) = discrete Influencing Value (x) = continuous
Logistic Regression
45
What are the names of the 3 normality tests in Minitab?
Anderson-Darling Ryan-Joiner Kolmogorov-Smirnov
46
How do you deal with non-normality? (4 options)
1. Get more samples. 2. Data stratification. 3. Remove special causes & document 4. Take data like it is.
47
What are the prerequisites for the 1-way ANOVA hypothesis test?
Sigma^2(1) = Sigma^2(2) = Sigma^2(3)... which is the Ho for Test for Equal Variances... which means Bartlett’s test or Levine's test
48
In hypothesis tests, what is 'x'?
Influencing factor
49
In hypothesis tests, what is 'y'?
Output
50
True/False: In hypothesis testing, you can have both discrete & continuous factors?
True.
51
What is the model to check the Mu for the 1-way ANOVA test?
y = y-bar(total) + Tao(i) + Epsilon Tao = Individual influence/effect of the shooter Epsilon = 'Unexplained rest'
52
What are the 3 steps to evaluating the 1-way ANOVA test?
1. Visualize data: box plot + main effects chart 2. Check the Model: Ho: T(i) = 0 3. Determination coefficient (in more detail on another slide)
53
What is the determination coefficient?
R^2(adj) > 80%, which tells you how good your [influencing] factor affects the reality. At this point, you've found enough root causes to stop investigating.
54
What is the difference between a 1-way ANOVA & a 2-way ANOVA?
1-way ANOVA = 1 factor 2-way ANOVA = 2 factors
55
Describe a 2-way ANOVA.
It is a hypothesis test with 2 influencing factors. X factors have a direct influence on y (output). X factors have an indirect influence on each other. Requirements: balanced design Example: y = distance (continuous) x = Ball + Shooter (both discrete)
56
What is the requirement of a 2-way ANOVA?
A balanced design!
57
What are the 4 steps to evaluating the 2-way ANOVA test?
1. Visualize data: box plot + main effects chart + Interaction plot 2. Check the Model: Ho: T(i) = 0 Ho: B(j) = 0 Ho: (TB)ij = 0 3. Determination coefficient (in more detail on another slide) 4. Check Residuals (aka Epilson - unexplained rest)
58
What is the model of a 2-way ANOVA?
59
What are the 3 things you check for when checking 'Residuals' (aka Epsilon/unexplained rest) for 2-way ANOVA?
1. Normal distribution 2. Check for patterns over y 3. Check for trends over time
60
What are the 2 more specific tests for the 2-variances hypothesis test?
F-test - normal distribution Levine's test - non-normal distribution
61
What are the 2 more specific tests for the Test for Equal Variances?
Bartlett's test - normal distribution Levine's test - non-normal distribution
62
What are the prerequisites for proportion based hypothesis tests?
n >/ 100 & at least 5 in each category
63
What is the correlation hypothesis test checking for?
Checking the magnitude of the relation. Is there a significant connection between A & B, & how strong is the connection?
64
What is the symbol for Pearson's Correlation Coefficient?
r
65
What is the r value for a Perfect Correlation? Describe the graph(s).
-1 = r = 1 Diagonal linear line going up or down.
66
What is the r value when no correlation? Describe the graph(s).
r = 0 Scatter plot, dots all over Horizontal line across
67
What is the r value when there is no linear correlation? Describe the graph(s). How is the correlation ranked in this case?
r = 0 Various options, but a non-linear shape. Rank by absolute value... >/ 0.8 strong >/ 0.5 medium
68
What does FMEA stand for?
Failure Mode & Effect Analysis
69
What are the 2 types of FMEA analysis?
Design FMEA: check design for potential risks Design FMEA: check process for potential risks
70
For FMEA, the Analyze phase looks at what? Improve phase?
Analyze - 'As Is Process' Improve - 'To Be Process'
71
In FMEA, how are identified risks categorized?
Severity, Occurrence, Detection
72
In FMEA, how do you calculate the Risk Priority Number (RPN)? (new & old way)
RPN = S * O * D (old way) Action Priority (AP) - High, Medium, Low
73
What does Regression hypothesis testing tell you?
Describes the influence of X on Y statistically.
74
In regression testing, x is called the...
Predictor
75
In regression testing, y is called the...
Response
76
In the Analyze Phase, regression testing is looking at?
Cause & Effect - how big is the impact on X & Y?
77
In the Improve Phase, regression testing is looking at?
Prognosis - what should the X be like if I want Y to be... (target). Use the model to get the target value you want - interpolation.
78
What is the difference between Interpolation & Extrapolation?
Interpolation: estimate a value within two known values Extrapolation: estimate a value based on extending a known sequence of value
79
How many Predictors (factors) in a Simple Linear Regression?
1 predictor/factor
80
How many Predictors (factors) in a Multiple Linear Regression?
2+ predictors/factors
81
What is the prerequisite of a multiple linear regression?
Predictors must be independent of each other (no correlation).
82
What is the indicator that the predictors are independent of each other (no correlation)? How do you know if it's good or bad?
Variation Inflation Factor (VIF) VIF = 1 :) VIF >/ 5 :(
83
What do you do if your VIF value is too high?
Kick out factors to meet the prerequisites before running the test.
84
How do you Analyze regression data in Minitab (4 items)?
1. R^2: goal >/ 80% 2. Is the model significant? 3. Are the coefficients significant? 4. Analyze Residuals
85
How do you Analyze Residuals?
1. Check for normal distribution (Ho = Normal Distribution, p-value) 2. Check for Patterns over Y (cone) 3. Check for trends over time
86
Describe the OFAT Strategy. What are the pros & cons?
Change only 1 factor at a time to find the correlation & best result. It's intuitive & the effect is clear, however, it requires a lot of setup and you cannot see the interactions between the factors.
87
In 2^k Design, what does the 2 stand for?
Factor Levels (min/max of each factor)
88
In 2^k Design, what does the k stand for?
Factors
89
What assumption does the 2^k Design make?
Must be a linear model
90
In the Process Model, what are the factors affecting the target value?
Influencing factors, Constant factors, Noise factors
91
How do you deal with variation from unknown noise?
Repeats & Replicates
92
Why do we want centerpoints?
Need to check the assumption of linearity & have more data to review.
93
When calculating the Pearson's Correlation Coefficient, what is the Ho?
Ho: A & B are 'free of each other' or 'free of correlation'
94
What is the formula for Multiple Linear Regression?
y = bo + b1x1... + bu xu + Epsilon
95
In the regression model, what is Bo?
The intersection with the y-axis.
96
In the regression model, what is B1?
The slope gradient of the line generated by the model
97
In the regression model, what is x?
The x-axis of that particular factor.
98
In the regression model, what is Epsilon?
The unexplained rest.
99
In the regression model, how do you analyze the coefficients?
Ho: Bo = 0 (line goes through the origin; Bo is free of significance) Ho: B1 = (line is parallel to the x-axis, no slope; B1 is free of significance)
100
When dealing with unknown noise, what are repeats?
Used to understand how to deal with short term unknown noise. "Make more parts/tests"
101
When dealing with unknown noise, what are replicates?
Used to understand how to deal with long term noise. "Do the whole plan again"
102
What do I do if I don't have data, a clue, or a lot of money to spend?
Design of Experiment!
103
What's the intuitive approach in DOE?
OFAT
104
In DOE, how do I know the effect of interactions between the factors?
Perform a Full Factorial Design
105
How to calculate the number of setups in a Full Factorial Design?
2^k... k is the number of factors
106
When you use DOE to understand the interactions between the factors, how can you tell if the interactions are significant?
To the right side of the significance line. You can also check the p-values.
107
In DOE, what do you do if the interactions are not significant?
Remove non-significant factors 1 at a time. Then the model will recalculate. Always start with the smallest one.
108
When removing non-significant interactions in DOE, be careful not to what?
Remove a factor where there is an interaction (letter combo).
109
In a main effects plot, how can you tell if there is an effect from from the factor?
Diagonal line = effect Horizontal line = no effect
110
In an interaction plot, how can you tell if there is an interaction?
Interaction if crossing lines.
111
What are the 5 things to check when reviewing analytical results in DOE?
1. Check VIF 2. Significance of main factors & interactions (p-value) 3. Determination Coefficient (R^2 adj.) >80% 4. Check the significance of the model (p-value) 5. Check the model's suitability - Residuals
112
True or False: Centerpoints are included in the Residual Analysis?
False
113
What is the difference between Full Factorial Design and Fractional Factorial Design?
Full Factorial Design looks at all possible combinations. Fractional Factorial Design looks at less combinations to save money.
114
How do you determine the number of samples/tests for a Fractional Factorial Design? What does each item mean?
2^k-q k = # factors q = reduction level 2 = # factorial levels (min/max)
115
What is the risk of using Fractional Factorial Design?
Confounding effects! Must decide if we can accept the risk of these effects.
116
With a resolution Type III, what is confounded?
1 main factors confounded, 2 factor interactions
117
What can you find in your Lean toolbox?
5s Push/Pull Polka Yoke Kanban Little's Law PDCA SMED TIM WOODS JIT Kaizen
118
Why was Lean developed?
MUDA (TIM WOOD) MURI (overuse/underuse of resources - S from TIM WOODS) MUSA (unbalanced)
119
How can I find out PLT? Must have what to use this process?
Little's Law = PLT = WIP/ER Must have stable WIP.
120
Why does confounding happen?
Confounding arises in fractional designs only and is caused by the signage of one column being the same as another. This means when calculating the effect of these factors/interactions, we will get the same value for the size of the effect.

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