Bonacorsi Consulting Analyze Master Template (09!27!07)
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Lean Six Sigma Program Name Define
Measure Analyze Improve Control
LEAN SIX SIGMA
< Last Name, First Name > Wave No: <xx> Review Date: <xx/xx/xxxx>
Lean Six Sigma Program Name Define
Measure Analyze Improve Control
LEAN SIX SIGMA
Analyze Phase Road Map Define
Measure
Analyze
Analyze
Activities •
Identify Potential Root Causes
•
Reduce List of Potential Root Causes
•
Confirm Root Cause to Output Relationship
•
Estimate Impact of Root Causes on Key Outputs
•
Prioritize Root Causes
•
Complete Analyze Gate
Bonacorsi Consulting
Improve
Control
Tools
3
•
Process Constraint ID and Takt Time Analysis
•
Cause & Effect Analysis
•
FMEA
•
Hypothesis Tests/Conf. Intervals
•
Simple & Multiple Regression
•
ANOVA
•
Components of Variation
•
Conquering Product and Process Complexity
•
Queuing Theory
Analyze
Measure Overview Process Capability
CTQ: ? Unit (d) or Mean (c): ? Defect (d) or St. Dev. (c): ? DPMO (d): ?
I -M R C h a r t o f D e l i v e r y T i m e 40 U C L= 3 7 . 7 0 Individual Value
Graphical Analysis
35 _ X= 2 9 .1 3
30 25
LC L= 2 0 . 5 6
20 1
Sigma (Short Term): ? Sigma (Long Term):? MSA Results: show the percentage result of the GR&R, AR&R or other measurement systems analysis carried out in the project
Quick Win #1
Root
cause:
Quick Win #2
Root
cause:
cause:
Quick Win #3
Bonacorsi Consulting
82
109
136 O b s e r v a tio n
163
190
217
244
U C L= 1 0 . 5 3
7 .5 5 .0
__ M R = 3 .2 2
2 .5 0 .0
LC L= 0 1
Root Cause / Quick Win Root
55
1 0 .0 M oving Range
28
28
55
82
109
136 O b s e r v a tio n
163
190
217
244
Tools Used
Detailed process mapping Measurement Systems Analysis Value Stream Mapping Data Collection Planning Basic Statistics Process Capability Histograms
4
Time Series Plot Probability Plot Pareto Analysis Operational Definitions 5s Pull Control Charts
Analyze
Graphical Analysis Summary
Data is Continuous: ?? Data Points Collected Between XX/XX/XX and XX/XX/XX Normality Central Tendency Variation Normal
Average
Non-Normal
Median or Q1 or Q3
Investigation
Statistical / Graphical Analysis
What is the shape of the Project Y data? Is the Y data normally distributed? What is the normality test p-value?
Std. Dev (long term) Span (1/99) or Stability Factor (Q1/Q3) Observations/Conclusion
Histogram Normality Test
What is the central tendency of the Mean, Median, or Quartile Values Project Y data? What is the spread of the Project Y Standard Deviation, Span, or data? Stability Factor Is the data stable over time?
Run Chart
Describe any other observations about the Project Y data Include any other findings regarding the data here
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5
Analyze
TPC Analysis Technical-Political-Cultural (TPC) Analysis Sources Of Resistance Definition Causes Of Resistance Rating
Examples
Technical
Political
Cultural
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6
Analyze
Sources of Variation Defect
Overproduction
Transportation Was the Action workout performed #1?
Area 1
Area 1 Sub area 1
<Sub area 1>
Sub area 1
NVA Area 1
Processing
Area 1 Sub area 1
Motion
Inventory
< Insert your variation percentage as shown in pie chart >
Was the Action workout performed #2?
Area 1
Sub area 1
Sub area 1
32%
11%
Waiting
5% 5% 5%
42%
Man Machine Method Measurement Mother Nature Machine
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7
Analyze
Cause and Effect Matrix Importance Rating Score Blank = no correlation 1 = remote correlation 3 = moderate correlation 9 = strong correlation Item
16 18 15 14 17 8 7 11 12 3 4 5 6 1 13 2 9 19 19
Speed to Decision Cooperation, Seats Schedule Clarity, Feedback Meets Reach Available Options Accuracy Req Decision 10
7
6
7
10
9
Budget for Options 5
Correlation of Input to Output Process Step Process Inputs To what extent does variability in these process steps/inputs impact variability across these outputs ? Develop Options Customer schedule & including price & requirements schedule Proper decision makers Approve options Database, tribal Inventory Search of knowledge known Vacant Space Interview Customer to Form Understand Form options, costs, schedule Brief customer on options, funding vehicle options Supervisor Approval
9
9
1
9
9
9 3 9
1
Peer Review Approval
9 9 3 3 3 3 3
7
Importance Rating
Total Higher score =
9
9
3
459
9
9
3
1
9
392
9
9
3
261
9
9
9
9
Fill out Forms Accept Form Create Spreadsheet Dept Approval Approval Mgr 1 Approval Mgr 2 Identify Need Assign to Team Mark Up Floor Plan Notification Forms to requestor and Log in issue Prepare brief
Process Outputs
9
9
9
Funding Source Identified
3 3
3
3
3
Were all the process steps identified?
217 216
1
Was the rating of importance done by Kano analysis?
118
111
3 3
3 3
108 51 30 30 30 30 21 21 0 0
What is the % of quick hits causing the problem?
0 0
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8
Analyze
Run Charts When the points are connected with a line, a run ends when the line crosses the median
A run, in this case, is one or more consecutive points on the same side of the median
Median = 38
Longest Run about the median
1 Run about the Median, can you count the other 8? There is a no nonrandom influence acting upon this process. There is no trend
There is a no nonrandom influence acting upon this process. There is no Oscillation
There is a nonrandom influence acting upon this process that is creating clustering
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9
Pareto Plot
Analyze
100 150
60
100
40 50 20 0
0 ut h
Defect
So
Count P ercent Cum %
100 58.5 58.5
r No
th
s Ea
50 29.2 87.7
15 8.8 96.5
t
he Ot
rs
6 3.5 100.0
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10
Percent
Count
80
Analyze
Scatter Plot
Average Expenses decrease as Sales Increase
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11
Analyze
Linear Regression
95% confident that 94.1% of the variation in “Wait Time” is from the “Qty of Deliveries” F i tt e d L i n e P l o t W a it T im e = 3 2 .0 5 + 0 .5 8 2 5 D e live r ie s 55
S R-Sq R - S q ( a d j)
Wait Time
50
45
40
35 10
15
20 25 D e liv e r ie s
30
35
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12
1 .1 1 8 8 5 9 4 .1 % 9 3 .9 %
One-Sample T-Test and Dot Plot Dotplot of Improve Data
This Dot Plot graphically displays 95% confidence intervals that the data will fall between 23.45 and 32.75 for response time (see the red brackets and red line). It also indicates that the Mean (Red X) is at 28.1. The blue Ho marks the Target Mean.
(with Ho and 95% t-confidence interval for the mean)
[
_ X Ho
]
We Are 95% Confident The Improve Mean Is Not Statistically Different 0
10
20
30
40
50
Improve Data
The test statistic, T, for Ho: mean = 30 is calculated as –0.84. The P-Value of this test, or the probability of obtaining more extreme value of the test statistic by chance if the null hypothesis was true, is 0.410 (> 0.05). This is called the attained significant level, or P-Value. Therefore, Accept Ho, which means we conclude that the Improve data set mean (28.1) is NOT different than the Target mean (30).
Analyze
Hypothesis Test: Is the Improve data set mean different from the Target Mean mean of 30 minutes?
One-Sample T: Improve Data Test of mu = 30 vs mu not = 30 Variable
N
Mean
StDev
SE Mean
Improve Data
30
28.10
12.45
2.27
Variable
95.0% CI
Improve Data
(23.45,
32.75)
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13
T -0.84
P 0.410
Test for Equal Variance
Analyze
Test for Equal Variances for Total Time 95% Confidence Intervals for Sigmas
Test for Equal Variance Confirms Payroll Input Type Cycle Time is Significant
Factor Levels Antenna
Payroll 0
50
100
150
F-Test Test Statistic: 0.108
Levene's Test Test Statistic: 21.054
P-Value
P-Value
: 0.001
: 0.000
The spread of the data is statistical greater for completing the payroll form than the Antenna time tracking.
Boxplots of Raw Data
Antenna
Payroll
0
10
20
30
40
50
60
70
T otal T ime
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14
Analyze
One Way ANOVA Boxplot: Part/ No Part Impact on Ticket Cycle Time
150
100
It is also caused by the need for 50 technicians to make a second visit to the end user to complete the part replacement. Next step will be 0 for the team to confirm these Part/No Part suspected root causes. Analysis of Variance for Net Hour Source DF SS MS Part/No 1 7421 7421 Error 69 59194 858 Total 70 66615 Level No Part Part
N 27 44
Pooled StDev =
Mean 21.99 43.05 29.29
StDev 19.95 33.70
F 8.65
P 0.004
Individual 95% CI's For Mean --+---------+---------+---------+---(--------*---------) (------*------) --+---------+---------+---------+---12 24 36 48
Part
No Part
After further investigation, possible reasons proposed by the team are supplier backorders, lack of technician certifications and the distance from the supplier to the client site.
Net Hours Call Open
Because the p-value <= 0.05, we can be confident that calls requiring parts do have an impact on the ticket cycle time.
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15
Analyze
Mood’s Median
Chi-Square = 19.14 DF = 2 P = 0.000 Month N<= N> Median 10 88 132 8.43 11 123 121 6.57 12 111 68 4.77 Overall Median 6.63
Q3-Q1 12.15 10.52 7.10
Dotplots Defects by Impact Subgroup Dot Plot:of Part/ No Part on Ticket Cycle Time
30
20
10
0
Subgroup
Mood’s Median Test Individual 95.0% Confidence Interval’s ------+---------+---------+---------+ (--------+-------) (------+------) (-----+------) ------+---------+---------+---------+ 4.8 6.4 8.0 9.6
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16
Before
It is also caused by the need for technicians to make a second visit to the end user to complete the part replacement. Next step will be for the team to confirm these suspected root causes.
40
After
After further investigation, possible reasons proposed by the team are supplier backorders, lack of technician certifications and the distance from the supplier to the client site.
Defects
Because the p-value <= 0.05, we can be confident that calls requiring parts do have an impact on the ticket cycle time.
Statistical Testing for Discrete Data Chi-Square Test
Because this p value is less than 5% (0.05), we can be confident that department does have an impact on the proportion of correct tickets processed
Expected counts (calculated by Minitab) are printed below observed counts Errors 33 40.46
Correct 60 52.54
Total 93
2
27 40.90
67 53.10
94
3
132 107.03
114 138.97
246
4
32 71.35
132 92.65
164
5
168 132.26
136 171.74
304
Total
392
509
901
1
Chi-Sq =
1.376 4.722 5.827 21.703 9.657 DF = 4, P-Value
This is due to the fact that Department 4 has fewer people who need to add information to each ticket, which reduces the chances that an error will be made. Also, in Department 4 the customer name, address and ticket number are added directly from the contracts system without human intervention, reducing the possibility that a typo or other error type could occur that will need to be corrected later (taking more time)
+ 1.060 + + 3.637 + + 4.487 + + 16.714 + + 7.437 = 76.620 = 0.000
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Analyze
17
Process Bottleneck Identification & Workload Balancing
Analyze
Takt Rate Analysis compares the task time of each process (or process step) to: Each other to determine the time trap Customer demand to determine if the time trap is the constraint
Task Time (seconds)
Value Add Analysis - Current State 80 70 60 50 40 30 20 10 0
Takt Time = Net Process Time Available Number of Units to Process
-Takt Tim e = 55
--
---
1
2
3
4
5 6 Task #
7
8
9
10
CVA Time
BVA Time
NVA Time
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18
Hypothesis Test Summary Hypothesis Test (ANOVA, 1 or 2 sample t - test, Chi Squared, Regression, Test of Equal Variance, etc)
Example: ANOVA
Example: ANOVA
Example: Chi Squared
Example: Pareto
Factor (x) Tested
p Value
Analyze
Observations/Conclusion
Location
Significant factor - 1 hour driving time from DC 0.030 to Baltimore office causes ticket cycle time to generally be longer for the Baltimore site
Part vs. No Part
Significant factor - on average, calls requiring 0.004 parts have double the cycle time (22 vs 43 hours)
Department
Significant factor - Department 4 has digitized 0.000 addition of customer info to ticket and less human intervention, resulting in fewer errors
Region
n/a
South region accounted for 59% of the defects due to their manual process and distance from the parts warehouse
Describe any other observations about the root cause (x) data
Was the hypothesis roadmap followed?
What was the power and Sample Size?
What are α and β risks?
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19
Analyze
Control / Impact Analysis Does the X have a high, medium, or low impact on the project Y?
Does the X in he teams control, influence, or out of their control?
High Impact In Our Control
In Our Influence
Out of Our Control
Medium Impact
Low Impact
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
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20
In/Out of Frame
Analyze
Creating a visual depiction of what elements of the project are in the scope (frame) and out of the scope Vital X #8
Influence
Vital X #2
Vital X #7
No Control
Vital X #1
Vital X #3
Vital X #5
Vital X #4
Control Vital X #6
Vital X #9
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21
Systems & Structures Assessment
How should we use or modify to support "____" vision and objectives
System or Structure Staffing
1 2
Training & Development
1 2
Measurements & Reward
1 2
Communication
1 2
Organization Design
1 2
Information Systems
1 2
Resource Allocation
1 2
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Analyze
22
Analyze
Quick Wins
5s
4-Step Setup Reduction
Inventory Reduction
MSA Improvements
Price reductions
Reduced DOWNTIME (Non-value added steps or work)
Pull System
Kaizen events
Other Enter Key Slide Take Away (Key Point) Here
Bonacorsi Consulting
23
Analyze
Business Impact
State financial impact of project
Separate “hard or Type 1” from “soft Type 2 or 3” dollars
State financial impact of future project leverage opportunities Annual Estimate
Replicated Estimate
Revenue Enhancement
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Expenses Reduction
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Loss Reduction
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Cost Avoidance
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Total Savings
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
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24
Analyze
Business Impact Details
Type 1: Describe the chain of causality that shows how you determined the Type 1 savings. (tell the story with cause–effect relationships, on how the proposed change should create the desired financial result (savings) in your project )
Show the financial calculation savings and assumptions used. Assumption #1 (i.e. source of data, clear Operational Definitions?) Assumption #2 (i.e. hourly rate + incremental benefit cost + travel)
Type 2: Describe the chain of causality that shows how you determined the Type 2 savings. (tell the story with cause–effect relationships, on how the proposed change should create the desired financial result (savings) in your project )
Show the financial calculation savings and assumptions used. Assumption #1 (i.e. Labor rate used, period of time, etc…) Assumption #2 (i.e. contractor hrs or FTE, source of data, etc…)
Describe the Type 3 Business Impact(s) areas and how these were measured Assumption #1 (i.e. project is driven by the Business strategy?) Assumption #2 (i.e. Customer service rating, employee moral, etc…)
Other Questions Stakeholders agree on the project’s impact and how it will be measured in financial terms? What steps were taken to ensure the integrity & accuracy of the data? Has the project tracking worksheet been updated?
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25
Analyze
Current Status Lean Six Sigma Project Status and Planning
Key actions completed
Issues
Lessons learned
Communications, team building, organizational activities
Deliverables/Tasks Completed last week
Upcoming Deliverables/Tasks - 2 weeks out
Due
Revised Due
Comments
For deliverables due thru: Actions Scheduled for next 2 Weeks Deliverable/Action Who Due Revised Due Comments/Resolution Need Help
Current Issues and Risks Who Due Revised Due Recommended Action Need Help
Issue/Risk
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W/E:
Comments
26
Analyze
Next Steps
Key actions
Planned Lean Six Sigma Tool use
Questions to answer
Barrier/risk mitigation activities
Lean Six Sigma Project Issue Log No.
Description/Recommendation
Status Open/Closed/Hold
Due Date
Last Revised: Revised Due Date
Resp
1 2 3 4 5 6 7 8 9 10
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27
Comments / Resolution
Analyze
Stop
Tollgate Checklist
Tollgate Review
Has the team identified the key factors (critical X’s) that have the biggest impact on process performance? Have they validated the root causes?
Analyze
Has the team examined the process and identified potential bottlenecks, disconnects and redundancies that could contribute to the problem statement?
Has the team analyzed data about the process and its performance to help stratify the problem, understand reasons for variation in the process, and generate hypothesis as to the root causes of the current process performance?
Has an evaluation been done to determine whether the problem can be solved without a fundamental ‘white paper’ recreation of the process? Has the decision been confirmed with the Project Sponsor?
Has the team investigated and validated the root cause hypotheses generated earlier, to gain confidence that the “vital few” root causes have been uncovered?
Does the team understand why the problem (the Quality, Cycle Time or Cost Efficiency issue identified in the Problem Statement) is being seen?
Has the team been able to identify any additional ‘Quick Wins’?
Have ‘learning's to-date required modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders?
Have any new risks to project success been identified, added to the Risk Mitigation Plan, and a mitigation strategy put in place?
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28
Sign Off
Analyze
• I concur that the Analyze phase was successfully completed on MM/DD/YYYY • I concur the project is ready to proceed to next phase: Improve
Enter Name Here
Enter Name Here
Green Belt/Black Belt
Enter Name Here
Deployment Champion
Enter Name Here
Sponsor / Process Owner
Enter Name Here
Financial Representative
Master Black Belt
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29
Bonacorsi Consulting This Training Manual and all materials, procedures and systems herein contained or depicted (the "Manual"), are the sole and exclusive property of Bonacorsi Consulting, L.L.C. The contents hereof contain proprietary trade secrets that are the private and confidential property of Bonacorsi Consulting. Unauthorized use, disclosure, or reproduction of any kind of any material contained in this Manual is expressly prohibited. The contents hereof are to be returned immediately upon termination of any relationship or agreement giving user authorization to possess or use such information or materials. Any unauthorized or illegal use shall subject the user to all remedies, both legal and equitable, available to Bonacorsi Consulting. This Manual may be altered, amended or supplemented by Bonacorsi Consulting from time to time. In the event of any inconsistency or conflict between a provision in this Manual and any federal, provincial, state or local statute, regulation, order or other law, such law will supersede the conflicting or inconsistent provision(s) of this Manual in all properties subject to that law. © 2006 by Bonacorsi Consulting, L.L.C. All Rights Reserved.
Steven Bonacorsi is a Senior Master Black Belt instructor and coach. Steven Bonacorsi has trained hundreds of Master Black Belts, Black Belts, Green Belts, and Project Sponsors and Executive Leaders in Lean Six Sigma DMAIC and Design for Lean Six Sigma process improvement methodologies. Bonacorsi Consulting
30
Measure Analyze Improve Control
LEAN SIX SIGMA
Lean Six Sigma Program Name Define
Measure Analyze Improve Control
LEAN SIX SIGMA
Analyze Phase Road Map Define
Measure
Analyze
Analyze
Activities •
Identify Potential Root Causes
•
Reduce List of Potential Root Causes
•
Confirm Root Cause to Output Relationship
•
Estimate Impact of Root Causes on Key Outputs
•
Prioritize Root Causes
•
Complete Analyze Gate
Bonacorsi Consulting
Improve
Control
Tools
3
•
Process Constraint ID and Takt Time Analysis
•
Cause & Effect Analysis
•
FMEA
•
Hypothesis Tests/Conf. Intervals
•
Simple & Multiple Regression
•
ANOVA
•
Components of Variation
•
Conquering Product and Process Complexity
•
Queuing Theory
Analyze
Measure Overview Process Capability
CTQ: ? Unit (d) or Mean (c): ? Defect (d) or St. Dev. (c): ? DPMO (d): ?
I -M R C h a r t o f D e l i v e r y T i m e 40 U C L= 3 7 . 7 0 Individual Value
Graphical Analysis
35 _ X= 2 9 .1 3
30 25
LC L= 2 0 . 5 6
20 1
Sigma (Short Term): ? Sigma (Long Term):? MSA Results: show the percentage result of the GR&R, AR&R or other measurement systems analysis carried out in the project
Quick Win #1
Root
cause:
Quick Win #2
Root
cause:
cause:
Quick Win #3
Bonacorsi Consulting
82
109
136 O b s e r v a tio n
163
190
217
244
U C L= 1 0 . 5 3
7 .5 5 .0
__ M R = 3 .2 2
2 .5 0 .0
LC L= 0 1
Root Cause / Quick Win Root
55
1 0 .0 M oving Range
28
28
55
82
109
136 O b s e r v a tio n
163
190
217
244
Tools Used
Detailed process mapping Measurement Systems Analysis Value Stream Mapping Data Collection Planning Basic Statistics Process Capability Histograms
4
Time Series Plot Probability Plot Pareto Analysis Operational Definitions 5s Pull Control Charts
Analyze
Graphical Analysis Summary
Data is Continuous: ?? Data Points Collected Between XX/XX/XX and XX/XX/XX Normality Central Tendency Variation Normal
Average
Non-Normal
Median or Q1 or Q3
Investigation
Statistical / Graphical Analysis
What is the shape of the Project Y data? Is the Y data normally distributed? What is the normality test p-value?
Std. Dev (long term) Span (1/99) or Stability Factor (Q1/Q3) Observations/Conclusion
Histogram Normality Test
What is the central tendency of the Mean, Median, or Quartile Values Project Y data? What is the spread of the Project Y Standard Deviation, Span, or data? Stability Factor Is the data stable over time?
Run Chart
Describe any other observations about the Project Y data Include any other findings regarding the data here
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5
Analyze
TPC Analysis Technical-Political-Cultural (TPC) Analysis Sources Of Resistance Definition Causes Of Resistance Rating
Examples
Technical
Political
Cultural
Enter Key Slide Take Away (Key Point) Here Bonacorsi Consulting
6
Analyze
Sources of Variation Defect
Overproduction
Transportation Was the Action workout performed #1?
Area 1
Area 1 Sub area 1
<Sub area 1>
Sub area 1
NVA Area 1
Processing
Area 1 Sub area 1
Motion
Inventory
< Insert your variation percentage as shown in pie chart >
Was the Action workout performed #2?
Area 1
Sub area 1
Sub area 1
32%
11%
Waiting
5% 5% 5%
42%
Man Machine Method Measurement Mother Nature Machine
Enter Key Slide Take Away (Key Point) Here Bonacorsi Consulting
7
Analyze
Cause and Effect Matrix Importance Rating Score Blank = no correlation 1 = remote correlation 3 = moderate correlation 9 = strong correlation Item
16 18 15 14 17 8 7 11 12 3 4 5 6 1 13 2 9 19 19
Speed to Decision Cooperation, Seats Schedule Clarity, Feedback Meets Reach Available Options Accuracy Req Decision 10
7
6
7
10
9
Budget for Options 5
Correlation of Input to Output Process Step Process Inputs To what extent does variability in these process steps/inputs impact variability across these outputs ? Develop Options Customer schedule & including price & requirements schedule Proper decision makers Approve options Database, tribal Inventory Search of knowledge known Vacant Space Interview Customer to Form Understand Form options, costs, schedule Brief customer on options, funding vehicle options Supervisor Approval
9
9
1
9
9
9 3 9
1
Peer Review Approval
9 9 3 3 3 3 3
7
Importance Rating
Total Higher score =
9
9
3
459
9
9
3
1
9
392
9
9
3
261
9
9
9
9
Fill out Forms Accept Form Create Spreadsheet Dept Approval Approval Mgr 1 Approval Mgr 2 Identify Need Assign to Team Mark Up Floor Plan Notification Forms to requestor and Log in issue Prepare brief
Process Outputs
9
9
9
Funding Source Identified
3 3
3
3
3
Were all the process steps identified?
217 216
1
Was the rating of importance done by Kano analysis?
118
111
3 3
3 3
108 51 30 30 30 30 21 21 0 0
What is the % of quick hits causing the problem?
0 0
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8
Analyze
Run Charts When the points are connected with a line, a run ends when the line crosses the median
A run, in this case, is one or more consecutive points on the same side of the median
Median = 38
Longest Run about the median
1 Run about the Median, can you count the other 8? There is a no nonrandom influence acting upon this process. There is no trend
There is a no nonrandom influence acting upon this process. There is no Oscillation
There is a nonrandom influence acting upon this process that is creating clustering
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9
Pareto Plot
Analyze
100 150
60
100
40 50 20 0
0 ut h
Defect
So
Count P ercent Cum %
100 58.5 58.5
r No
th
s Ea
50 29.2 87.7
15 8.8 96.5
t
he Ot
rs
6 3.5 100.0
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10
Percent
Count
80
Analyze
Scatter Plot
Average Expenses decrease as Sales Increase
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Analyze
Linear Regression
95% confident that 94.1% of the variation in “Wait Time” is from the “Qty of Deliveries” F i tt e d L i n e P l o t W a it T im e = 3 2 .0 5 + 0 .5 8 2 5 D e live r ie s 55
S R-Sq R - S q ( a d j)
Wait Time
50
45
40
35 10
15
20 25 D e liv e r ie s
30
35
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1 .1 1 8 8 5 9 4 .1 % 9 3 .9 %
One-Sample T-Test and Dot Plot Dotplot of Improve Data
This Dot Plot graphically displays 95% confidence intervals that the data will fall between 23.45 and 32.75 for response time (see the red brackets and red line). It also indicates that the Mean (Red X) is at 28.1. The blue Ho marks the Target Mean.
(with Ho and 95% t-confidence interval for the mean)
[
_ X Ho
]
We Are 95% Confident The Improve Mean Is Not Statistically Different 0
10
20
30
40
50
Improve Data
The test statistic, T, for Ho: mean = 30 is calculated as –0.84. The P-Value of this test, or the probability of obtaining more extreme value of the test statistic by chance if the null hypothesis was true, is 0.410 (> 0.05). This is called the attained significant level, or P-Value. Therefore, Accept Ho, which means we conclude that the Improve data set mean (28.1) is NOT different than the Target mean (30).
Analyze
Hypothesis Test: Is the Improve data set mean different from the Target Mean mean of 30 minutes?
One-Sample T: Improve Data Test of mu = 30 vs mu not = 30 Variable
N
Mean
StDev
SE Mean
Improve Data
30
28.10
12.45
2.27
Variable
95.0% CI
Improve Data
(23.45,
32.75)
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T -0.84
P 0.410
Test for Equal Variance
Analyze
Test for Equal Variances for Total Time 95% Confidence Intervals for Sigmas
Test for Equal Variance Confirms Payroll Input Type Cycle Time is Significant
Factor Levels Antenna
Payroll 0
50
100
150
F-Test Test Statistic: 0.108
Levene's Test Test Statistic: 21.054
P-Value
P-Value
: 0.001
: 0.000
The spread of the data is statistical greater for completing the payroll form than the Antenna time tracking.
Boxplots of Raw Data
Antenna
Payroll
0
10
20
30
40
50
60
70
T otal T ime
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Analyze
One Way ANOVA Boxplot: Part/ No Part Impact on Ticket Cycle Time
150
100
It is also caused by the need for 50 technicians to make a second visit to the end user to complete the part replacement. Next step will be 0 for the team to confirm these Part/No Part suspected root causes. Analysis of Variance for Net Hour Source DF SS MS Part/No 1 7421 7421 Error 69 59194 858 Total 70 66615 Level No Part Part
N 27 44
Pooled StDev =
Mean 21.99 43.05 29.29
StDev 19.95 33.70
F 8.65
P 0.004
Individual 95% CI's For Mean --+---------+---------+---------+---(--------*---------) (------*------) --+---------+---------+---------+---12 24 36 48
Part
No Part
After further investigation, possible reasons proposed by the team are supplier backorders, lack of technician certifications and the distance from the supplier to the client site.
Net Hours Call Open
Because the p-value <= 0.05, we can be confident that calls requiring parts do have an impact on the ticket cycle time.
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Analyze
Mood’s Median
Chi-Square = 19.14 DF = 2 P = 0.000 Month N<= N> Median 10 88 132 8.43 11 123 121 6.57 12 111 68 4.77 Overall Median 6.63
Q3-Q1 12.15 10.52 7.10
Dotplots Defects by Impact Subgroup Dot Plot:of Part/ No Part on Ticket Cycle Time
30
20
10
0
Subgroup
Mood’s Median Test Individual 95.0% Confidence Interval’s ------+---------+---------+---------+ (--------+-------) (------+------) (-----+------) ------+---------+---------+---------+ 4.8 6.4 8.0 9.6
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Before
It is also caused by the need for technicians to make a second visit to the end user to complete the part replacement. Next step will be for the team to confirm these suspected root causes.
40
After
After further investigation, possible reasons proposed by the team are supplier backorders, lack of technician certifications and the distance from the supplier to the client site.
Defects
Because the p-value <= 0.05, we can be confident that calls requiring parts do have an impact on the ticket cycle time.
Statistical Testing for Discrete Data Chi-Square Test
Because this p value is less than 5% (0.05), we can be confident that department does have an impact on the proportion of correct tickets processed
Expected counts (calculated by Minitab) are printed below observed counts Errors 33 40.46
Correct 60 52.54
Total 93
2
27 40.90
67 53.10
94
3
132 107.03
114 138.97
246
4
32 71.35
132 92.65
164
5
168 132.26
136 171.74
304
Total
392
509
901
1
Chi-Sq =
1.376 4.722 5.827 21.703 9.657 DF = 4, P-Value
This is due to the fact that Department 4 has fewer people who need to add information to each ticket, which reduces the chances that an error will be made. Also, in Department 4 the customer name, address and ticket number are added directly from the contracts system without human intervention, reducing the possibility that a typo or other error type could occur that will need to be corrected later (taking more time)
+ 1.060 + + 3.637 + + 4.487 + + 16.714 + + 7.437 = 76.620 = 0.000
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Analyze
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Process Bottleneck Identification & Workload Balancing
Analyze
Takt Rate Analysis compares the task time of each process (or process step) to: Each other to determine the time trap Customer demand to determine if the time trap is the constraint
Task Time (seconds)
Value Add Analysis - Current State 80 70 60 50 40 30 20 10 0
Takt Time = Net Process Time Available Number of Units to Process
-Takt Tim e = 55
--
---
1
2
3
4
5 6 Task #
7
8
9
10
CVA Time
BVA Time
NVA Time
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Hypothesis Test Summary Hypothesis Test (ANOVA, 1 or 2 sample t - test, Chi Squared, Regression, Test of Equal Variance, etc)
Example: ANOVA
Example: ANOVA
Example: Chi Squared
Example: Pareto
Factor (x) Tested
p Value
Analyze
Observations/Conclusion
Location
Significant factor - 1 hour driving time from DC 0.030 to Baltimore office causes ticket cycle time to generally be longer for the Baltimore site
Part vs. No Part
Significant factor - on average, calls requiring 0.004 parts have double the cycle time (22 vs 43 hours)
Department
Significant factor - Department 4 has digitized 0.000 addition of customer info to ticket and less human intervention, resulting in fewer errors
Region
n/a
South region accounted for 59% of the defects due to their manual process and distance from the parts warehouse
Describe any other observations about the root cause (x) data
Was the hypothesis roadmap followed?
What was the power and Sample Size?
What are α and β risks?
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Analyze
Control / Impact Analysis Does the X have a high, medium, or low impact on the project Y?
Does the X in he teams control, influence, or out of their control?
High Impact In Our Control
In Our Influence
Out of Our Control
Medium Impact
Low Impact
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
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In/Out of Frame
Analyze
Creating a visual depiction of what elements of the project are in the scope (frame) and out of the scope Vital X #8
Influence
Vital X #2
Vital X #7
No Control
Vital X #1
Vital X #3
Vital X #5
Vital X #4
Control Vital X #6
Vital X #9
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Systems & Structures Assessment
How should we use or modify to support "____" vision and objectives
System or Structure Staffing
1 2
Training & Development
1 2
Measurements & Reward
1 2
Communication
1 2
Organization Design
1 2
Information Systems
1 2
Resource Allocation
1 2
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Analyze
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Analyze
Quick Wins
5s
4-Step Setup Reduction
Inventory Reduction
MSA Improvements
Price reductions
Reduced DOWNTIME (Non-value added steps or work)
Pull System
Kaizen events
Other Enter Key Slide Take Away (Key Point) Here
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Analyze
Business Impact
State financial impact of project
Separate “hard or Type 1” from “soft Type 2 or 3” dollars
State financial impact of future project leverage opportunities Annual Estimate
Replicated Estimate
Revenue Enhancement
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Expenses Reduction
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Loss Reduction
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Cost Avoidance
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
Total Savings
• Type 1: ? • Type 2: ? • Type 3: ?
• Type 1: ? • Type 2: ? • Type 3: ?
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Analyze
Business Impact Details
Type 1: Describe the chain of causality that shows how you determined the Type 1 savings. (tell the story with cause–effect relationships, on how the proposed change should create the desired financial result (savings) in your project )
Show the financial calculation savings and assumptions used. Assumption #1 (i.e. source of data, clear Operational Definitions?) Assumption #2 (i.e. hourly rate + incremental benefit cost + travel)
Type 2: Describe the chain of causality that shows how you determined the Type 2 savings. (tell the story with cause–effect relationships, on how the proposed change should create the desired financial result (savings) in your project )
Show the financial calculation savings and assumptions used. Assumption #1 (i.e. Labor rate used, period of time, etc…) Assumption #2 (i.e. contractor hrs or FTE, source of data, etc…)
Describe the Type 3 Business Impact(s) areas and how these were measured Assumption #1 (i.e. project is driven by the Business strategy?) Assumption #2 (i.e. Customer service rating, employee moral, etc…)
Other Questions Stakeholders agree on the project’s impact and how it will be measured in financial terms? What steps were taken to ensure the integrity & accuracy of the data? Has the project tracking worksheet been updated?
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Analyze
Current Status Lean Six Sigma Project Status and Planning
Key actions completed
Issues
Lessons learned
Communications, team building, organizational activities
Deliverables/Tasks Completed last week
Upcoming Deliverables/Tasks - 2 weeks out
Due
Revised Due
Comments
For deliverables due thru: Actions Scheduled for next 2 Weeks Deliverable/Action Who Due Revised Due Comments/Resolution Need Help
Current Issues and Risks Who Due Revised Due Recommended Action Need Help
Issue/Risk
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W/E:
Comments
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Analyze
Next Steps
Key actions
Planned Lean Six Sigma Tool use
Questions to answer
Barrier/risk mitigation activities
Lean Six Sigma Project Issue Log No.
Description/Recommendation
Status Open/Closed/Hold
Due Date
Last Revised: Revised Due Date
Resp
1 2 3 4 5 6 7 8 9 10
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Comments / Resolution
Analyze
Stop
Tollgate Checklist
Tollgate Review
Has the team identified the key factors (critical X’s) that have the biggest impact on process performance? Have they validated the root causes?
Analyze
Has the team examined the process and identified potential bottlenecks, disconnects and redundancies that could contribute to the problem statement?
Has the team analyzed data about the process and its performance to help stratify the problem, understand reasons for variation in the process, and generate hypothesis as to the root causes of the current process performance?
Has an evaluation been done to determine whether the problem can be solved without a fundamental ‘white paper’ recreation of the process? Has the decision been confirmed with the Project Sponsor?
Has the team investigated and validated the root cause hypotheses generated earlier, to gain confidence that the “vital few” root causes have been uncovered?
Does the team understand why the problem (the Quality, Cycle Time or Cost Efficiency issue identified in the Problem Statement) is being seen?
Has the team been able to identify any additional ‘Quick Wins’?
Have ‘learning's to-date required modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders?
Have any new risks to project success been identified, added to the Risk Mitigation Plan, and a mitigation strategy put in place?
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Sign Off
Analyze
• I concur that the Analyze phase was successfully completed on MM/DD/YYYY • I concur the project is ready to proceed to next phase: Improve
Enter Name Here
Enter Name Here
Green Belt/Black Belt
Enter Name Here
Deployment Champion
Enter Name Here
Sponsor / Process Owner
Enter Name Here
Financial Representative
Master Black Belt
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Bonacorsi Consulting This Training Manual and all materials, procedures and systems herein contained or depicted (the "Manual"), are the sole and exclusive property of Bonacorsi Consulting, L.L.C. The contents hereof contain proprietary trade secrets that are the private and confidential property of Bonacorsi Consulting. Unauthorized use, disclosure, or reproduction of any kind of any material contained in this Manual is expressly prohibited. The contents hereof are to be returned immediately upon termination of any relationship or agreement giving user authorization to possess or use such information or materials. Any unauthorized or illegal use shall subject the user to all remedies, both legal and equitable, available to Bonacorsi Consulting. This Manual may be altered, amended or supplemented by Bonacorsi Consulting from time to time. In the event of any inconsistency or conflict between a provision in this Manual and any federal, provincial, state or local statute, regulation, order or other law, such law will supersede the conflicting or inconsistent provision(s) of this Manual in all properties subject to that law. © 2006 by Bonacorsi Consulting, L.L.C. All Rights Reserved.
Steven Bonacorsi is a Senior Master Black Belt instructor and coach. Steven Bonacorsi has trained hundreds of Master Black Belts, Black Belts, Green Belts, and Project Sponsors and Executive Leaders in Lean Six Sigma DMAIC and Design for Lean Six Sigma process improvement methodologies. Bonacorsi Consulting
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