1. Continuous Improvement & Six Sigma Green Belt Certification
Virtual Session 3 (Keisen v m)

2. Review Standard Deviation Statistical 6 Sigma
DPU, DPO, DPMO, Defective%, PPM
Sigma Level
Descriptive Statistics
Quincuncx “artifact”

3. Brain Flex Standard Deviation and its meaning
What are your conclusions of the following graph where the mean value of the process is 28 minutes and the standard deviation is 0.3 minutes R:
Most of the times (99.7%), the process is executed between 27.1 and 28.9 minutes.
We can estimate that for the next 100 services, 95.4% of them are expected to execute between 27.4 and 28.6 minutes.
Quick 5 minute discussion.
Do the exercise and make participants do the interpretation of the results.

4. Brain Flex σ= 0.3 27.1 28.9 2007.05.21 v6 www.keisen.com Mean = 28.0
Std Deviation = 0.3
27.4
28.0
27.7
28.3
28.6
27.1
68.3%
28.9
95.4 %
Quick 5 minute discussion.
Do the exercise and make participants do the interpretation of the results.
Some examples:
If Standard Deviation is 0.3 minutes, 99.7% of the services (almost all) are delivered in an interval of 27.1 to 28.9 minutes.
95 out of the next 100 services will be delivered between 27.4 and 28.6 minutes.
Half of the services can be delivered before 20 minutes. Or Half of the services can be delivered after 28 minutes.
Most of the services tend to be delivered in 28 minutes, however as we cannot control al the variables of the process, we have variation and services are delivered faster or later tan this central tendency.
99.7%
Keisen Consultores S.A. de C.V.

5. Process Variation vs Specifications
Process Variation vs Specifications
Different problems = Different Solutions 1 2 3
BIAS
Deviation vs Center
DISPERSION
ABNORMALITY
(Assignable cause)
© Keisen Consultores 2009 (México).

6. How to reduce variation?
Video
How to reduce variation?
Video number 6
Duration

7. Video: Reducing variation & dealing with abnormalities
Have you seen a Gauss “bell-shaped” curve?
Use of Quincuncx to explain:
Bell-shaped curve of a normal process.
How to reduce variation? = Control of more variables.
How to mess up a process? = Non-evidence based actions and decisions.
How to understand an abnormality?
Abnormalities and the need to investigate

8. Video Video number 7 Duration Benefits of understanding variation
Multistep process measurement
Video number 7
Duration

9. Video: Importance of measuring variation
We usually know the average time of a 3 stage process can be estimated adding the average values of each stage.
However, how can we estimate the variation of the total time of this 3 stage process?
Use of Quincuncx and White board to explain:
3 different Stages or process steps with same mean but different standard deviation.
Total time mean estimation by “ADDITIVITY OF THE MEAN”.
How to estimate the Final Standard Deviation as the “ADDITIVITY OF THE STANDARD DEVIATION” will NOT give an accurate value=
Learning how to calculate the mean (average) and standard deviation of a single process is now a simple task.
However, how to calculate the central tendency and dispersion of a multistep process or the result of several processes?
The additivity of the average exists and so we just have to add up the averages of each process.
However, the additivity of the standard deviation does not exist, so total standard deviation IS NOT the summation of the standard deviations of each process or stage.
Solution: Use the Additivity of the Variance rule = Summation of the Variance of each process or stage to calculate TOTAL VARIANCE. And then calculate the Squared Root of the Total Variance to obtain TOTAL Process Standard Deviation.

10. Video: Additivity of mean and varianceP1
µ1=20
Average time 1 P2
Average time 2
µ2=30 P3
Average time 3
µ3=10
Total average time
µ= =60
Whole Process
(minutes)
The additivity of the mean applies (i.e. it is OK to do it). So if we have a 3 stage process and we can calculate the average time of each one of the sub processes (i.e. stages), we can simply calculate the TOTAL AVERAGE TIME of the whole process by adding the average times of the sub processes.
The additivity of the average = OK to use it.

11. Video: Additivity of mean and variance
µ1=20
Var1=4
σ1=2 P1
µ2=30
Var2=25
σ2=5 P2
µ3=10
Var3=1
σ3=1 P3
Whole Process
(minutes)
µ= = 60
Var = σ12+σ22+σ32
Var=4+25+1=30
σ= 30 = 5.48
Variance = σ2
Standard Deviation = 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = σ2
However, if we want to understand the variation (dispersion, process width, or data spread) of the whole process, WE SHALL NOT add the standard deviations of each subprocess, as the Additivity of the Standard Deviation does not apply.
The Additivity of the Variance CAN BE APPLIED (it is OK to use it), so the only thing we need to calculate is the Variance of each sub process (remember the variance = Squared Standard Deviation). In EXCEL, you can calculate both, the Variance and the Standard Deviation of each sub process.
Once you have the Variance of each process, we add them up (var1+var2+var3) and we calculate the Total Process Variance. The standard deviation of the whole process can be then calculated by the Root Square of the Variance.

12. Video: Additivity of mean and variance
µ1=20
Var1=4
σ1=2 P1
µ2=30
Var2=25
σ2=5 P2
µ3=10
Var3=1
σ3=1 P3
σtotal= 5.48
Whole Process
(minutes)
µ= = 60
Var = σ12+σ22+σ32
Var=4+25+1=30
σ= 30 = 5.48
As we can calculate the whole process mean and its standard deviation, we can estimate the whole process central tendency and dispersion.
We can add & subtract to the mean value, 3 times the standard deviation and find the values of the natural and statistical control limits (i.e. process width at 99.7% of the times).
We can then say the average is 60 minutes, and most of the times the process is expected to be between min to min.
43.56 min
76.44 min
Variance = σ2
Standard Deviation = 𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒 = σ2
Total spread estimation

13. Brain Re-Flex Variances Additivity Property
We need to estimate total average time and variation of a 4 stage process.
P1: Mean1= 5 min StandDev1= 2 min.
P2: Mean2= 4 min. StandDev2= 2 min.
P3: Mean2= 6 min. StandDev2= 2 min.
P4: Mean2= 5 min. StandDev2= 2 min. R:
Means (averages) additivity property exists so total mean is estimated by adding each stage´s mean (20 min).
Also, Variances additivity property exists so in order to estimate the whole process standard deviation, we must add each stage´s variance (16 squared minutes).
DO NOT add independent standard deviations to calculate total processes SD.
Total Standard Deviation is 4 minutes.
What does this mean? How consistent is our process?
10 minutes discussion and exercise.
Tips:
1) Total Time of the process (average) is 20 minutes.
The Variance is the Squared Standard Deviation, so if Standard Deviation of each one of the processes is 2, 2, 2 and 2, then the Variance for each process is 4,4,4,4 and Total Process Variance is 16.
The Total Process Standard Deviation is then Squared Root of 16 = 4 minutes.
What does an average of 20 minutes and a Standard Deviation of 4 minutes represents for the process?
(estimate with the 6 sigma rule the width of the process, adding and subtracting 3 times de Standard Deviation (12 minutes) to the mean value (in this case, the 99.7% of the process goes from 8 to 32 minutes (6 sigma rule), and we can also say that 95% of our services go from 12 to 28 minutes (4 sigma rule), as well as that 68 of our next 100 services could be delivered between 16 and 24 minutes)

14. Probability Distribution
Means and Standard Deviations σ
Probability distribution is simply a theoretical frequency distribution characterized by Mean and Standard Deviation.
Normal Distribution is represented by µ and σ.
Review of how a process results (data) in normal conditions (ex. Same person, same process, same environment, same tools) can be measured and estimated with its central tendency and its dispersión (variation). So the mean value or average and the standard deviation or sigma, play an important role for us to understand the consistency (predictability) of our processes.
Figure:
Remember then that ONE standard deviation to the right and to the left of the mean represent 68.3% of our whole data or process results OR we can also estimate and say that 68 out of the next 100 services will be delivered between the values or statistical limits defined by adding and subtracting one standard deviation to the mean.
The same thing can be said about the other results shown in the graph.

15. Calculation of z-value (z score)
Where:
x = Value of the data point of interest
µ = Mean of the population or data points
σ = Standard Deviation of the data points
Z = Number of standard deviations between x & the mean (µ). z-value is negative when the data point of interest is below the mean.
𝑧= 𝑥−𝜇 𝜎
z-value is the number of standard deviations between a measured value and the mean. For example, we have a process with a mean value of 28 minutes and a standard deviation of 0.3 minutes, and one service is finished in 29.2 minutes. The question is, How far is this service (29.2) from the mean value (28) measured in terms of a certain number of standard deviations. The answer is: 29.2 minutes is 4 standard deviations away from the mean value (4x0.3=1.2 minutes).
So, there is where the “z-value” formula comes from, we subtract the mean value from the measured value “x” and then divide this result by the standard deviation (sigma).
In other words, every process has its mean value (central tendency or average) and its standard deviation, and we can unify it or transform this values into a process where the mean value or “Central tendency” is “0” and the horizontal axis is the number of standard deviations of the measured value from the mean value.

16. The z-value?
Assume the mean of service process A is 28.0 min. with a standard deviation of 0.3 min. Calculate the z-value for process A service times:
27.7 minutes
28.3 minutes
28.6 minutes
29.2 minutes R:
Za= 27.7− = -1
zb= 28.3− = 1
Zc= 28.6− = 2
Zc= 29.2− = 4
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
+0.08%
+0.08% 4σ
0.15%
0.15%
28.9
Use the formula of Z Value to calculate the number of Standard Deviations between x & the mean (µ). A negative value is on the left hand side of the mean value.
This graph helps to illustrate the meaning of z-value in a practical example we have followed during the session.

17. The z-value & Probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Given the z-value for process A services times, estimate the probability of finishing:
Before 27.7 minutes (z= -1)
Before 28.3 minutes (z= 1)
After 28.6 minutes (z=2)
After 29.2 minutes (z=4)
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
Probability of finishing before 27.7 min or z= -1?
R: 15.87%
+0.08%
+0.08% R:
15.87% of the services are finishing before 27.7 minutes.
84.13% of the services are delivered before 28.3 minutes.
As 97.72% of the services are finished by 28.6 minutes, 2.28% of the services might be delivered after 28.6 minutes.
30 services per million opportunities (0.003%) of the services might deliver after 29.2 mins. 4σ
+0.003%
0.15%
0.15%
29.2
For each one of the cases (a to d) we illustrate the “meaning” of the probability of something to happen.
Later we will explain how to calculate the probability. By now, we need to explain the participants the concept of probability.
We can put an example of tossing the coin, something most of the people understand it has the probability of 50-50%. So now, is how to estimate a probability when you know the mean or average, the standard deviation and the value for which you want the distribution (or calculate the probability).

18. Using Normal Distribution Tables
If z-value=-1.00 search the first column for the number -1.0 (one decimal) then each one of the following columns define the second and third decimal of the z-value.
In our case, z=-1 so the probability of services finishing before z=-1 is equal to 15.87%
We will later show how to calculate this probability with excel, however it is common to show this tables to the participants as they might want to calculate this probability based on z-value in an easy way.
The important part is to understand the concept of z-value and the concept of probability:
For the next 100 services or results, how many of them will be delivered before or under a certain value (z-value) given a known average and standard deviation of the process (population).
In following exercises, we will also, ask to estimate the probability of an event to happen between 2 certain values (ex. Probability of finishin after 27.7 minutes and before 29 minutes).

19. The z-value & Probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Given the z-value for process A services times, estimate the probability of finishing:
Before 27.7 minutes (z= -1)
Before 28.3 minutes (z= 1)
After 28.6 minutes (z=2)
After 29.2 minutes (z=4) R:
15.87% of the services are finishing before 27.7 minutes.
84.13% of the services are delivered before 28.3 minutes.
As 97.72% of the services are finished by 28.6 minutes, 2.28% of the services might be delivered after 28.6 minutes.
30 services per million opportunities (0.003%) of the services might deliver after 29.2 mins.
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
b) Before 28.3 min or z= 1?
R: 84.13%
+0.08%
+0.08% 4σ
+0.003%
0.15%
0.15%
29.2

20. The z-value & Probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Given the z-value for process A services times, estimate the probability of finishing:
Before 27.7 minutes (z= -1)
Before 28.3 minutes (z= 1)
After 28.6 minutes (z=2)
After 29.2 minutes (z=4) R:
15.87% of the services are finishing before 27.7 minutes.
84.13% of the services are delivered before 28.3 minutes.
As 97.72% of the services are finished by 28.6 minutes, 2.28% of the services might be delivered after 28.6 minutes.
30 services per million opportunities (0.003%) of the services might deliver after 29.2 mins.
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
c) Over 28.6 min or z=2?
R: % = 2.28%
+0.08%
+0.08% 4σ
+0.003%
0.15%
0.15%
29.2
Note:
The probability of anything to happen is 100% or 1.
So, the probability of being OVER a z-value of 2, is just estimating the probability of finishing before z-value and then subtracting it from 1.

21. The z-value & Probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Given the z-value for process A services times, estimate the probability of finishing:
Before 27.7 minutes (z= -1)
Before 28.3 minutes (z= 1)
After 28.6 minutes (z=2)
After 29.2 minutes (z=4) R:
15.87% of the services are finishing before 27.7 minutes.
84.13% of the services are delivered before 28.3 minutes.
As 97.72% of the services are finished by 28.6 minutes, 2.28% of the services might be delivered after 28.6 minutes.
30 services per million opportunities (0.003%) of the services might deliver after 29.2 mins.
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
d) Over 29.2 min or z=4?
R: = 0.003%
+0.08%
+0.08% 4σ
+0.003%
0.15%
0.15%
29.2

22. How to calculate with MS Excel?
Function
Excel English
Excel Spanish
NORMAL DISTRIBUTION FOR SPECIFIED MEAN AND STD. DEVIATION
NORMDIST (x, mean, standard deviation, TRUE)
DIST.NORM (x, media, desviación estandar, VERDADERO)
Function that estimates the probability (to the left) given a mean and a standard deviation.
X: The value for which you want the distribution
Mean: Arithmetic mean or average of the distribution
Standard deviation: Standard deviation of the distribution (a positive number)
TRUE: A logical value for the cumulative distribution function.
If you want the probability density function, then use “false” (i.e. use false when you want to calculate the “y” value of the “x” value in a Normal Distribution Curve).
Homework has one exercise related to the use of Excel.

23. How to estimate probable service process output?
Brain Flex
How to estimate probable service process output?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Estimate the % of process A services finishing:
Before 27.4 minutes
Between 27.4 and 28.3 minutes
Over 28.9 minutes

24. What is the probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Estimate the % of process A services finishing:
Before 27.4 minutes
Between 27.4 and 28.3 minutes
Over 28.9 minutes R:
= 2.25% (in graph due to rounding). In table: 2.28%
= 81.8%
1-( ) = 0.15%
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
a) Before 27.4 min?
0.05%
0.05%
0.15%
0.15%
Note:
Due to rounding of the probability percentages (to one decimal) in the graph, when adding the remaining percentages (e.g. on the left side or before 27.4 min), the result shows 2.25% of the services will finish before the mentioned time.
When done with the tables or Excel function, the exact value comes to 2.28% of finishing before 27.4 minutes (the most exact value)

25. What is the probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Estimate the % of process A services finishing:
Before 27.7 minutes
Between 27.4 and 28.3 minutes
Over 28.9 minutes R:
= 15.85%
= 81.8%
1-( ) = 0.15%
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
b) Between 27.4 and 28.3 min?
0.05%
0.05%
0.15%
0.15%
Note:
If tables are used then must calculate the probability of finishing:
Before 28.3 minutes (84.13%)
Before 27.4 minutes (2.28%)
Then subtract =81.86% (differences due to rounding) in the class example.
The main objective is for people to understand that we can calculate the probability (concept), not to stress them with the use of tables.

26. What is the probability?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Estimate the % of process A services finishing:
Before 27.7 minutes
Between 27.4 and 28.3 minutes
Over 28.9 minutes R:
= 15.85%
= 81.8%
1-( ) = 0.15%
µ=28.0 minutes and σ=0.3 minutes
27.1
28.9
27.4
27.7
28.3
28.6
28.0 σ
c) Over 28.9 min?
0.05%
0.05%
0.15%
0.15%
Note:
Due to rounding of probability percentages (to one decimal) in the graph, when adding the remaining percentages, the result shows 0.15% of the services will be over 28.9 min.
When done with the tables or Excel function, the exact value comes to 0.135% of finishing after 28.9 minutes

27. Focus of CI & Six Sigma: Y=f(X)
Measuring and improving “Y”
Current Situation?
THE RESULTS
How well did it go? “Y”
Focus on the CAUSE (X),
Not the Result (Y).
How are the processes?
THE FACTORS THAT GENERATE THE RESULTS
How well were resources allocated?
“X”
Review and emphasize the importance to understand not only the effect of a problem, but to understand the factors and processes that generate this problem, in order to find causes.

28. Pareto Graph Type of Claim Frequency Cumulative frequency %
Pareto Graph
Type of Claim
Frequency
Cumulative frequency %
Cumulative % A: 44
36.7 % B: 28 72
23.3 %
60.0 % C: 25 97
20.8 %
80.8 % D: 10
107
8.3 %
89.1 % E: 5
112
4.2 %
93.3 %
Others 8
120
6.7%
100.0 %
TOTAL
© Keisen Consultores 2009 (México).

29. PARETO GRAPH www.keisen.com 2009.03.25 Pareto Graph Objectives:44 72 97
107
112
120
100.0%
Pareto Graph Objectives:
Stratify the whole into its parts for better understanding of the situation.
Be able to Prioritize important in order to Focus on key issues.
Interpretation:
Claim type A represents 36.7% of all claims.
Claims A, B &C represent 80.8% of all claims.
From the frequency standpoint, our priority is to reduce claims type A,B&C.
Is a combination of 2 graphs:
Bar graph (sorted data) of stratified factors (divide the whole into its parts)
Line graph, showing the cumulative values.
It also has 2 vertical axis:
Primary axis (left side): The scale used by the original data of the process or problem being measured. The maximum value of the primary axis shall cover the whole problem data (e.g. summation of the whole parts).
Secondary axis (right side) is an equivalent percentage scale of the individual parts and whole data (in our example, 120 defective services = 100% of the total defectives).
Pareto Graph has 2 main objectives.
Stratify the whole into its parts for better understanding of the situation.
Be able to Prioritize important in order to Focus on key issues.
© Keisen Consultores 2009 (México).

30. PARETO GRAPH (Stratified by A)
PARETO GRAPH (Stratified by A)
Pareto Graph Objectives:
Stratify the whole into its parts for better understanding of the situation.
Be able to Prioritize important in order to Focus on key issues.
Interpretation:
Claim type A represents 36.7% of all claims.
Claims A, B &C represent 80.8% of all claims.
From the frequency standpoint, our priority is to reduce claims type A,B&C. 20 30 37 41 44
100.0%
A Pareto graph shall be also used to stratify the selected key problem or issue into its parts again, when data is available, so to “drill down” and go from a general problem into the specific key problem.
After selecting problem “A”, we now stratify into its components or subproblems A1, A2, A3,….other.
So we now know that 45.5% of the “A” problem is represented by “A1”.
© Keisen Consultores 2009 (México).

31. PARETO GRAPH (Stratified by A)
PARETO GRAPH (Stratified by A)
Pareto Graph Objectives:
Stratify the whole into its parts for better understanding of the situation.
Be able to Prioritize important in order to Focus on key issues.
Interpretation:
Claim type A represents 36.7% of all claims.
Claims A, B &C represent 80.8% of all claims.
From the frequency standpoint, our priority is to reduce claims type A,B&C. 10 15 18 19 20
100.0%
As much as data is available, or the individual as well as team are willing to collect new data, we can keep on stratifying a problem (“drilling down”).
It is common to find root causes, through this data intensive approach, and having no need to use other tool to define a causal relationship.
However, this Pareto graph tool is an important tool for problem definition and prioritization of the key topic to improve.
© Keisen Consultores 2009 (México).

32. Brain Flex
Analyze the following Pareto graph and write your most important issue to improve.

33. Why Why Analysis Why the user complains about the service received?
A Simple and powerful Root Cause Analysis technique
Why the user complains about the service received?
Because of the errors in the bill account.
Why are they complaining about errors in the bill account?
Promised discount in an ad is not being applied
Why are promised discounts not being applied?
Online web based discount is applied, however “face to face” operations in Region 3 are charging without applying the discount.
Why are Region 3 sales executives did not apply the discount?
Not aware of the existence of such a promotion and as their do a manual process without web services, our “automated sales system” was not able to prevent these cases.
Why did Region 3 sales executives did not know about this particular promotion?
50% of Region 3 agents are commission agents (contractors) and were not considered in the mailing list and two way communication protocols.
“Errors in the bill account occur because commission agents (contractors) only have currently serving distant populations are not in the mailing list”
It is important to explain that all of us when answering the question “Why?”, commonly answer 2 different types of sentences.
Reasons
Causes
And the Why Why analysis requires that we understand these 2 different types of answers in order to promote the “Causes” responses to find root causes.
Avoid “One Word” answers as causes in a brainstorming session. Try to write full sentences.
Avoid writing explanations: “look for causes, do not explain the problem”.
Avoid writing justifications: “look for the origin of the problem, not justify your actions”.
Avoid judging the current situation or causes: “Bad procedure”, “Inadequate operation”
Avoid writing causes as a “lack of your solution”: “Lack of training”, “not enough time”

34. Why Why Analysis (wrong way)
Promote evidence based approach vs assumptions
Why the user complains about the service received?
Because of the errors in the bill account.
Why are they complaining about errors in the bill account?
Because customers never want to pay and lie seeking for any opportunity to extend their payment period.
Why do customers do not want to pay?
Customers do not have money. They just like to buy things and then return them hoping you will never remember them.
Why do customers do not have money?
Because they like to go on vacations.
Why do customers like to go on vacations?
In order to relax and prevent stress.
“So the user complains about the bill account errors, because users like to relax and prevent stress”.
A common error in cause analysis is when we “keep on” answering reasons and judging current situations.
Try not to build this “non logical” causal analysis.

35. Practice Why Why Analysis
Activity
Practice Why Why Analysis
Based on a particular example of a process or service problem, use the why why analysis.
See the following table.

36. In search of causes (origin)
Why_________________________________________________
How can I validate the cause?
10 minute activity.
Individually or in groups (if required, trainers shall organize the group into teams).
Select a problem (measurable and specific).
Try to apply the why why analysis (each row is a why why step).
After each answer, define the actions to be done in order to validate the existence and relationship of the cause with the effect (it is a “checkpoint” to assure we are analyzing in the right way).

37. Cause & Effect Diagram
The Cause & Effect diagram was first used by Dr. Kaoru Ishikawa of the University of Tokyo to organize a discussion with Kawasaki Steel Co. managers in This diagram is used to identify all of the contributing possible causes (factors) likely to be causing an effect (problem or deviation).
Outside Japan, is also known as Ishikawa Diagram and/or Fishbone Diagram.
This tool offers several benefits:
Visual tool
Straightforward and easy to learn
Promotes participation of the whole team
Organizes discussion & maintains focus
Promotes systems thinking
Supports further analysis and corrective actions
Cause and Effect Diagram, is also known as “Fishbone Diagram” or Ishikawa Diagram.
Very useful to identify all of the contributing possible causes (causal factor) likely to be generating an effect (problem or deviation).
Recommendations:
Very useful when addressing UNKWNOWN CAUSES and COMPLEX CAUSES for example, multifactorial problems.
At first, the Why Why analysis may be good enough to find root causes, so the Ishikawa Diagram in not recommended to be used in causal analysis for all problems or undesired situations.

38. Ishikawa Diagram Deployment
Ishikawa Diagram Deployment
A good Cause and Effect Diagram looks like this one and can have the following characteristics.
Minimum of 40 bones in total (so please generate a proactive brainstorming)
Reach fourth level in each case (minimum).
As bones are organized, keep generating more and more bones.
© Keisen Consultores (México).

39. Cause and Effect Diagram tips
Video
Cause and Effect Diagram tips
Video number 8
Duration:

40. Video: Ishikawa Diagram
How to make an Ishikawa Diagram from scratch.
Brainstorming with sticky notes.
Categorization (grouping).
Categories´ naming.
Organization of causes (primary, secondary, ….)

41. Video: Ishikawa Diagram
Video: Ishikawa Diagram
Problem
Statement
Category
© Keisen Consultores 2009 (México).

42.
Video: The Traditional 4M + New Categories Man Machine Materials Method + Management Environment
MANAGEMENT
MACHINE / EQUIPMENT
MAN / PEOPLE
PROBLEM STATEMENT
ENVIRONMENT
METHOD / PROCEDURE
MATERIALS / INPUT
© Keisen Consultores 2009 (México).

43. Video: Ishikawa: Original Approach.
Video: Ishikawa: Original Approach.
PROBLEM
STATEMENT
Process 1
Process 3
Process 2
Process “N”
Process 4
Process 5
First Ishikawa diagram had this structure, because a problem usually is generated in a process and or, generated as a consequence of different processes.
This is part of what is named “Upstream Management” by quality assurance and operations management experts. Key approach indicates that a problem shall be prevented by eliminating root causes in previous processes, usually located in early stages of the product design and development, marketing and sales departments, or simply the previous process before our process.
For example, a customer claim can be corrected or contained by our operators and staff at the workplace, however, preventive actions to eliminate reocurrence of the same problem or claim, could be generated during the sales process, or due to a bug in the information system protocols, and shall be comprehensively analyzed by process.
© Keisen Consultores 2009 (México).

44. Key Recommendations for an Effective Ishikawa Diagram
Key Recommendations for an Effective Ishikawa Diagram
Correct definition of the problem and its statement.
Invite key stakeholders who live, know and understand the process where the Improvement will be implemented.
Brainstorm all possible causes of the problem and write them in sticky notes (one cause, one note).
Avoid “One Word” sticky notes. Try to write full sentences.
Avoid writing explanations: “look for causes, do not explain the problem”.
Avoid writing justifications: “look for the origin of the problem, not justify your actions”.
Avoid judging the current situation or causes: “Bad procedure”, “Inadequate operation”
Avoid writing causes as a “lack of your solution”: “Lack of training”, “not enough time”
Check that all participants understand all written causes in the same way.
You can decide to use the 4M rule or create your own categories for your diagram.
© Keisen Consultores 2009 (México).

45. Pie Chart
Generally useful to show percentage or proportional data and when trying to compare parts of a whole.
It is not an easy graph to draw manually, however it helps us to STRATIFY the whole into its parts, and to focus on key issues.
Pareto Graphs, are a more robust tool for stratification and prioritization, as they manage more categories and give additional information than the Pie Chart (e.g. data for each category, cumulative data and possibility of stratifying into different Pareto graphs using a linear scale (Y axis) which makes it easy to compare different different perspectives with different scales and units (e.g. can easily present a Pareto graph of problem frequency, economic loss due to the problems, loss of time to repair and rework due to errors)

46. Bar graphs Bar or column 100% Stacked column 100%
Estas son series de tiempo solamente

47. Radar Chart (Spider chart)
Radar Chart (Spider chart)
Skill 1 10
Test result Z
100
Skill 2 10 5 5 50 5
100
Test result Y 50 10
Skill 3 5 C C-
Skill 4 10 A
Performance
evaluation A+
Graphical method to displaying multivariate data in a 2 dimentional char of 3 or more radial axis representing quantitative and qualitative variables.
The center of the chart (starting point of the axis) represents the lowest score, and the ending point represents the máximum score of each variable.
Very useful to view one process or person vs different variables when a balance between all the variables is desired.
Personality test 1
© Keisen Consultores 2009 (México).

48. Boxplot (Box & Whisker)
Graphical representation of a data distribution (center, width and outliers) based on:
Minimum or smallest value of data set
First or Lower Quartile (Q1)
Second Quartile or Median (Q2)
Third or Upper Quartile (Q3)
Maximum or largest value of data set
“Outliers” (value that lies at an abnormal distance from other values or statistically outside current data set distribution).
Note: For symetric distributions, statistical limits of the distribution (the whiskers) are calculated as follows:
Q IQR or largest value
Q1 – 1.5 IQR or smallest value
Where IQR = Q3 – Q1 (the Box)
Boxplot:
This is the most common approach to draw a Box&Whisker Plot (as in other tools, variations on how to estimate the “whisker”, and the IQR have been developed).
It is a simple and powerful graph to easily estimate the central tendency and width of the process (variation) using the medians (i.e. no need of standard deviation or formulas).
As it looks like a bar (not a bell shape graph), it is a very useful tool to monitor a process vs time (as a run chart of time series of a process distribution, and not only averages)
Also, useful to compare as in a bar graph different similar processes when stratified by person, shift, site and other perspectives.

49. Boxplot
An example
Provided is an example of a call center process where Average Handle Time (AHT) of the calls is compared between the Supervisors of the process.
What does the Boxplot tell you?
Note:
In the resources file, a template for Excel BoxPlot is available for homework.

50. Scatter Plot Graph
Analyses possible relationship between two variables:
Dependent (Y)
Independent (x)
IMPORTANT:
Linear correlation between two variables does not necessarily imply a causal relationship

51. Demonstration: Big Feet mean higher intelligence.
Brain Flex:
Demonstration: Big Feet mean higher intelligence.
16/04/2018
Dr. Imaizumi, Masumasa, introduces this example in a class to demonstrate a Japanese custom of praising babies for the size of their feet.
Dr. Ricardo Hirata, disciple of Dr. Imaizumi applies to graduate school through this innovative model of admission.
Due to the size of his feet, he entered graduate school without presenting any exam.
Please discuss.
Math exam grade
Size of feet
The experiment did happen, and the different participants answered an exam, and also their feet were measured. Each participant had a plot as follows: Size of the foot, Result in the math exam.
The Dispersion graph is as shown in the slide. Shows a directly proportional relationship. “The larger the foot, the better evaluation you got in the exam”.
The size of your feet has a relationship with the results of an evaluation (exam results).
What do you think?
This experiment was done with children from a Primary School (6 to 12 years old), and they had to answer a 6th grade math exam.
Children´s feet grow as they advance through primary school
First graders get bad scores in a 6th grade math test. 6th graders get better scores in their same level math test.
So one conclusion is that RELATIONSHIP DOES NOT necessarily means CAUSALITY.
The TRUE Causal relationship, could be: “The more school years a child can study, the better performance he will achieve”. The size of your foot, has nothing to do with your intelligence.
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52. Brain Flex Scatter Plot
What can be inferred from data presented below?
Quick 5 minutes discussion

53. How to estimate probable service process output using Z Score?
Homework
How to estimate probable service process output using Z Score?
Assume the mean of service process A is 28.0 minutes with a standard deviation of 0.3 minutes. Estimate the % of process A services finishing:
Before 27.7 minutes
Between 27.4 and 28.3 minutes
Over 28.9 minutes
Using Excel functions and compare to previous results of a Brain Flex.

54. Homework Build a Pareto Graph
Collect data of one of your daily processes that you would like to understand and maybe improve.
Stratify your data and build a Pareto Graph.
Write all your conclusions from your interpretation of the graphs.
Select your priority: Your most important issue to work on (you can use this issue or topic for building a Ishikawa Diagram).

55. Build a Box&Whisker Graph
Homework
Build a Box&Whisker Graph
Collect data of one of your daily processes you would like to understand and maybe improve.
Build a Box&Whisker Plot Graph of one process. Try to make more than one Boxplot, for example
Different Boxplot of the process vs time (one per month or week)
Same process, same month and different Supervisors.
Write all your conclusions from your interpretation of the graph.

56. Ishikawa / Cause and Effect Diagram
Homework
Ishikawa / Cause and Effect Diagram
Define a small team of colleagues (supervisors only or your natural team you work with daily):
Define a problem (define its magnitude).
Build a Cause and Effect Diagram.
Find potential causes of the problem.

57. Thank you