Video on Analyze Phase
Lean Six Sigma  Analyze Phase
Problem Identification / IO Identification
Identified Stakeholders
Gather their Voice
Convert to CTQ
Create Project Charter
 Business Case
 Problem Statement / Opportunity Statement
 Goal Statement
 Scope Statement
 In Scope
 Out Scope
 Identify Team
 Define Roles and responsibilities
 RASIC/RACI/ ARMI
 Define Roles and responsibilities
 Summary Milestone
 Detailed Flow Chart
 COPIS
 Benefits Documentation
 Risk Documentation
Measure
 Identify CTQ Metric
 Build DCP
 Collect Data
 Validated Data using MSA
 Asis Process Capability
 Numeric
 1 Sided Spec
 Z (remember to transform data if data is non normal)
 2 Sided Spec
 Z & Cp CPk (remember to transform data if data is non normal)
 1 Sided Spec
 NonNumeric
 DPMO
 Numeric
Analyze
 Identify the potential factors (Xs)
 Brainstorming
 COPIS
 I stands for Input, Input shall be a key determinant of the outcome.
 Input & Resources must be evaluated as potential contributors
 Detailed Flowcharts
 Mathematical formulae and Derivatives
 Recruitment TAT = MPR + MPR Approval + Resume Shortlisting time + Interview TAT + Final Interview TAT + Offer Issuance TAT + Acceptance TAT….
 Study of Existing Data Strata
 Data wont exist in silo – it shall always have some other attributes that are captured with it.
 FMEA – Failure Mode and effect Analysis
 You try to find root case for the potential failure mode of the process
Analyze1
 Identify all Potential Xs
# Brainstorming – Reason of defects asks from all stakeholders
# Data Strata –
# Mathematical derivatives and formula
# IInput of SIPOC/COPIS
#Process steps (detail flow chart)
# FMEA
 Prepare DCP of Xs.
 Collect Data of all Xs.
 Understand behaviour of Y.
 Check statistical relationship between Y&X.
 Summary of analysis.
Next list down all the factors in excel and show all the factors in FISH bone. We will validate the factors whether one should be impacted or not through hypothesis testing/statistical relationship between Y & X.
 Build DCP for Factors
Factor Name 
CTQ (Measurable Metric) 
Data Type 
Test to be used 
DVT 
Next Step 
Hold time 





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Process Knowledge 

















 Collect data for factors
 Perform hypothesis tests for statistical evidence of impact etc.
Hypothesis Testing (Assumption Testing): Method to check impact of X on Y is known as hypothesis testing.
Study the sample and pass the judgement
 Null Hypothesis
 Data is normal distributed
 Data is random( No significant evidence of the 4 patterns)
 Data is independent ( Y is not significantlyimpacted by X)
 Alternate Hypothesis
 Data is nonnormal
 Data is non random
 Data is dependent ( Y is impacted by X)
Data has 3 basic characteristics
 Data is normal distributed
 If data were to adhere to the basic rules
 Mean +_1 Std Dev = 68%
 Mean +2 Std Dev = 95%
 Mean + 3 Std Dev = 99.74%
 If any of the three above rules are not met – we say that the data has evidence of special cause variation. (and process is referred to as out of control)
 If data were to adhere to the basic rules
 Data is random (Run Chart)
 Path in Minitab – Stat > Quality tools > Run chart
 Absence of the 4 patterns in data is termed as randomness (Stability)
 Trend – Data is moving in one direction (either upwards or downwards).
 Cluster – Data which is close to each other.
 Some control is being exhibited to keep the data close to each other.
 Oscillation – To &Fro movement in data
 Ideally represents a upstream – downstream kind of process.
 Mixtures – Absence of data near the central line
 Represents 2 or more population in the same data.
 Absence of the 4 patterns in data is termed as randomness (Stability)
 Data is independent
 All variables are independent of each other
P Value:
Decision criteria to choose between Ho & Ha is a value of probability referred to as P Value
Also referred to as value of significance – is a probability value (i.e. P value cannot be greater than 1 or 100% and cannot be smaller than 0 or 0%) and it represents contribution of chance in any relationship.
1 – Pvalue = Contribution of the factor
Cat causes accident.
Other factors (Chance) that could have caused accident – Rash driving / Drunk / Lubricant on the road / Someone else mistake
Salary causes attrition
Other factors that may have contributed is referred to as chance.
Cat experiment – P value  .12 (Chance)
Contribution of cat = 1.12 = .88 or 88%
Salary impacting attrition
P Value = .01
Chance or other factors = 0.01
Salary = 1.01 = .99
Factor 
P Value 
Chance 
Factor 
Std = 95% 
Bad Boss 
.27 
.27 
1  .27 = .73 or 73% 
Not impacting 
Salary 
.01 
.01 
.99 or 99% 
Impacting 
Growth Opp 
0.03 
.03 
.97 or 97% 
Impacting 
Distance from office 
0.0 
0 
100 
Impacting 
P Value < 0.05 = Ha will be accepted
P value = > 0.05 = Ho will be accepted.
LoHa
Confidence Level
The degree of surety about any inference.
Sample – Population
Population = 120 Cr
Sample = 12 Crore
1.2 Cr
12 Lakh
1.2 L
12000
Data Collection will have huge cost impact, may need too much time
Sample Size impact CL
Higher the sample size higher the Confidence Level
CL = 1 Alpha
95% CL – 5%
Minitab = 95% CL ( 5% Alpha error happening)
Alpha Error
Beta Error





Pranay is innocent 
Free the guilty Pranay 
Beta Error 
Alpha 
Innocent Pranay is punished 
Pranay is guilty 

When Ho is true but you accept Ha = Alpha error
When Ha is true and you accepted H0 – Beta error.





Salary does not impact 
While salary impacts you have concluded it does not. 
Beta Error 
Alpha 
While salary does nt impact – you concluded that it did 
Salary impacts 

Choice of Tests is dependent on data type of Y & X
When Y is Continuous
When YC Xc
 Correlation
 Ho – Variables are independent
 Ha – Variables are related
 Step 1 : P Value received from the test
 Decide Ho or Ha
 St
 Step 1 : P Value received from the test
DVT – Scatter Plot
YcXd – Regression Test
Step 1: P Value
 Ho : X does not significantly impact Y
 Ha : X significantly impacts Y
Step 2 : R Sq. – Coefficient of Determination – and it explains the % impact of X on Y
R Sq. = SSF/SST as %, industry standard for R Sq. = 62%, i.e. if R Sq. is greater than 62%, we consider the magnitude of impact as high.
Step 3 : If P is Ha, R Sqr is high – then check the regression equation ( also called transfer function)
Regression Analysis: Total Breakage versus Production(Kg)
The regression equation is
Total Breakage = 6.05 + 0.0198 Production(Kg)
Predictor Coef SE Coef T P
Constant 6.050 1.255 4.82 0.000
Production(Kg) 0.019802 0.004574 4.33 0.000
S = 2.47203 RSq = 8.3% RSq(adj) = 7.9%
Analysis of Variance
Source DF SS MS F P
Regression 1 114.53 114.53 18.74 0.000
Residual Error 206 1258.86 6.11
Total 207 1373.38
DVT – Scatter Plot
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