Bonacorsi Consulting Measure 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
Measure Phase Road Map Measure
Define
Analyze
Activities
• • • • • • • •
• • Develop Operational Definitions • Develop Data Collection Plan • Validate Measurement System • • Collect Baseline Data • Determine Process Performance/Capability • Validate Business Opportunity • Value Stream Map for deeper understanding & focus • • Quick Wins (Control Plans) • Measure Gate Review •
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Improve
Control
Tools
• Identify Key Input, Process and Output Metrics •
Measure
3
High/Low level process Maps Value Stream Map Operational Definitions Data Collection Plan Statistical Sampling Measurement System Analysis (MSA), Gage R&R Constraint Identification Setup Reduction Generic Pull Kaizen TPM Control Charts Process Capability, Cp & Cpk
Project Charter
Measure
Problem/Goal Statement Problem: Describe problem in non-technical terms Statement should explain why project is important; why working on it is a priority Goal: Goals communicate “before” and “after” conditions Shift mean, variance, or both? Should impact cost, time, quality dimensions Express goals using SMART criteria Specific, Measurable, Attainable, Resource Requirements, Time Boundaries Explain leverage and strategic implications (if any)
Financial Impact
State financial impact of project Expenses Investments (inventory, capital, A/R) Revenues
Separate “hard” from “soft” dollars
State financial impact of leverage opportunities (future projects)
Team PES Name PS Name DC Name GB/BB Name MBB Name Core Team Team Member 1 Team Member 2 Team Member 3 Team Member 4 Extended Team Team Member 1 Team Member 2
Tollgate Review Schedule
Project Executive Sponsor (if different from PS) Project Sponsor/Process Owner Deployment Champion Green Belt/Black Belt Master Black Belt Role % Contrib. LSS Training SME XX YB TM XX GB SME XX PS SME XX YB
Tollgate
Scheduled
Revised
Complete
Define:
XX/XX/XX
-
XX/XX/XX
Measure:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Analyze:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Improve:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Control:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Review high-level schedule milestones here:
BFM IT
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XX XX
Not Trained Not Trained
4
Phase Completions Tollgate Reviews Trials
Measure
Attitude Charting & Key Constituency Map Key Constituents Map Customer 13%
Operations 25%
“Critical mass must be won-over”
HR 25%
Finance 37%
Attitude Charting
40% 35%
35%
35% 30% 25% 20% 15%
15%
15% 10% 5% 0% Innovators
Early Adopters
Late Adopters
Resistors
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5
Sample Size: Continuous Data
Measure
Use this calculation for CONTINUOUS data 1.96 (Confidence Interval: Za/2)
Desired Confidence Level (90%, 95%, 99%, etc)?
95
* Population Standard Deviation (# hours/days/etc)?
3
3 (Sigma)
** What Precision is Required (within # hours/days/etc)?
1
1 (Delta)
Minimum Sample Size (n):
34
n = ((Za/2 * Sigma)/Delta)^2
* Population Standard Deviation: Your best estim ate based on your know ledge of the data or previous sam ples of sim ilar data. Exam ple - based on sim ilar data from a previous project you assum e Std Dev for call length = 5 m inutes. ** Error: Exam ple - if you w ant to estim ate average call length w ithin +/- 1 m inute, you w ould specify an error of 1.
Finite Population Correction (if n/N >0.05 or 5% where N = Total Population Size): n= N=
34 400
n (finite) =
31
Calculated minimum sample size (above) Total population size n (finite) = n / (1 + n/N)
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6
Sample Size - Discrete Data
Measure
Use this calculation for "YES/NO," "TRUE/FALSE," "DEFECTIVE/NOT DEFECTIVE" (binomial or discrete) types of data. Use only when n * PB >= 5 Desired Confidence Level (90%, 95%, 99%, etc)?
95
* What Precision is Required (within 5, 10, 15%, etc)?
1
** Defective Data (50%, 40%, 30%, 20%, 15%, etc)?
0.2
Minimum Sample Size (n):
76
1.96 (Confidence Interval: Za/2) 0.01 (Delta) 0.002 (P B)
n = (Za/2/Delta)^2 * P B (1-P B )
* Error: Exam ple - if you w ant to es tim ate the proportion defective w ithin +/-2%, you w ould s pe cify an e rror of 2. ** Defective Data: Your es tim ated pe rce ntage of the overall population that w ould be cons ide re d "de fe ctive " data. Exam ple - bas e d on the spec, you e xpect a de fe ct rate of 12%. If you don't know and w ant to be cons ervative, use 50%
Finite Population Correction (if n/N >0.05 or 5% where N = Total Population Size): n= N= n (finite) = defects expected:
76 925 70 0
Calculated minimum sample size (above) Total population size n (finite) = n / (1 + n/N)
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7
Measure
Data Collection Plan Performance Measure
Operational Definition
Data Source and Location
How Will Data Be Collected
Who Will Collect Data
When Will Data Be Collected
Sample Size
Stratification Factors
VOC MSA Process VSM Financials Others
For each performance measure (Y), update a data collection plan
Include MSA measure plan (Gantt chart, MS project plan is Optional)
Add Financial measure plan if separate from performance Y
Add any Time Study or other data collection plans for Value Stream Map
Sample Size Calculation
Use additional slides if needed
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8
How will data be used?
Measure
Operational Definitions
Y – Continuous data (Process start/stop and cycle time boundaries (such as the unit of measure (ex minutes), the unit (the thing you are measuring), will you include weekends, holidays, non-business hours?)
Y – Discrete data (Define Success/Defect or other attribute values you will measure
X – The subgroups values or X-factor groupings you will use on your project data collection
Other unique terms that apply to your project that require clear operational definitions
Use additional slides as needed to complete your operational definitions Enter Key Slide Take Away (Key Point) Here
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9
Measure
Measurement Systems Analysis (MSA) Gage R&R (ANOVA) for Response
Gage R&R Source Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation
%Contribution VarComp (of VarComp) 0.0015896 3.70 0.0005567 1.29 0.0010330 2.40 0.0003418 0.79 0.0006912 1.61 0.0414247 96.30 0.0430143 100.00
Source Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation
Study Var %Study Var StdDev (SD) (6 * SD) (%SV) 0.039870 0.23922 19.22 0.023594 0.14156 11.38 0.032140 0.19284 15.50 0.018488 0.11093 8.91 0.026290 0.15774 12.68 0.203531 1.22118 98.13 0.207399 1.24439 100.00
Components of Variation % Contribution
P e rc e nt
% Study Var
9.75 9.50
0
Gage R&R
Repeat
Reprod
1
Part-to-Part
2
3
Sa m ple R a nge
2
3
0.10
7
8
9
10
10.00 9.75
0.00
LCL=0
1
2 Operator
Xbar Chart by Operator 2
UCL=9.8422 __ X=9.7996 LCL=9.7569
9.75
3
Operator * Part Interaction
3
Operator
10.00 A v e ra ge
Sa m ple M e a n
6
9.50
10.00
1 2
9.75
3
9.50
9.50
1
2
3
4
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5
Response by Operator UCL=0.1073
_ R=0.0417
0.05
4
Part
R Chart by Operator 1
1
Measurement system is acceptable with the Total % Contribution <10%
10.00
50
Number of Distinct Categories = 7
Response by Part
100
10
5 6 Part
7
8
9 10
Measure
MSA Conclusions
The measurement systems are acceptable. The data is considered to have no potential for significant error. Need to be careful to appropriately use the data during the Analyze Phase. Type of Measurement Error
Description
Considerations to this Project
Discrimination (resolution)
The ability of the measurement system to divide into “data categories”
Work hrs can be measured to <.25 hrs. Tool usage measure to +- 2 min.
Bias
The difference between an observed average measurement result and a reference value
No bias - Work hours and radar startstop times consistent through population.
Stability
The change in bias over time
No bias of work hrs & radar usage data.
The extent variability is consistent
Not an issue. Labor and radar usage is historical and felt to be accurate enough for insight and analysis.
Reproducibility
Different appraisers produce consistent results
Remarks in usage data deemed not reproducible, therefore were not considered in determining which radars were used in each op
Variation
The difference between parts
n/a to this process.
Repeatability
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11
Measure
Probability Plot Probability Plot of Anderson-Darling Normality Normal - 95% CI 99.9 99 95
Percent
90 80 70 60 50 40 30 20
Mean StDev N AD P-Value
24.74 4.177 100 0.380 0.397
10 5 1 0.1
10
15
20
25 QTY
30
35
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12
40
Measure
Baseline Basic Statistics
The current process has a non-normal distribution with the P-Value < 0.05 but does have a normal bell-shape.
Summary for Delivery Time
Since the mean and median are the same in days (29) +/- 0.5 days, we will not transform data.
24
26
28
30
32
A nderson-Darling Normality Test
34
A -Squared P-V alue <
1.95 0.005
Mean StDev V ariance Skew ness Kurtosis N
29.128 2.677 7.169 0.201075 -0.471714 266
Minimum 1st Q uartile Median 3rd Q uartile Maximum
24.000 27.000 29.000 31.000 35.000
95% C onfidence Interv al for Mean
The range is 35 and the standard deviation is 2.7 days
28.805
95% C onfidence Interv al for Median 29.000
29.000
95% C onfidence Interv al for StDev 9 5 % Confidence Intervals
2.468
Mean Median 28.8
28.9
29.0
29.1
29.2
29.3
29.4
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29.451
13
2.927
Measure
Control Chart
255 data points collected with zero subgroups, thus the I&MR control chart selected
I-MR Chart of Delivery Time 40 UCL=37.70 Individual V alue
The current baseline delivery time is stable over time with both the Moving Range (MR) (3.22 days) and Individual Average (29.13 days) experiencing common cause variation
35 _ X=29.13
30 25
LCL=20.56
20 1
28
55
82
109
136 Observation
163
190
244
UCL=10.53
7.5 5.0
__ MR=3.22
2.5
LCL=0
0.0 1
28
55
82
109
136 Observation
163
190
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217
10.0 M oving Range
14
217
244
Baseline Process Capability
Measure
P r ocess Capability of Deliv ery Time
266 data points collected between 11/1/04 thru 11/30/04
LS L
T arg et
USL
W ith in O v erall
Mean 29 days, St. Dev. 2.9 days, CP is 1.16 indicating process needs centering to the LSL of 10 and USL of 30 days. Cpk is .1 indicating that the process is exceeding the USL.
P ote ntia l (W ithin) C a pa bility Cp 1.16 C PL 2.22 C PU 0.10 C pk 0.10 C C pk 1.16 O v e ra ll C a pa bility Pp PPL PPU P pk C pm
With an overall PPM of 371,895 defects per million opportunity, the current process has a Sigma Quality Level of 1.8 or a 62% yield
P roce ss D a ta LS L 10 T a rge t 20 USL 30 S a m ple M e a n 29.1203 S a m ple N 266 S tD e v (W ithin) 2.87033 S tD e v (O v e ra ll) 2.69154
12
16
20
24
O bse rv e d P e rform a nce P P M < LS L 0.00 P P M > U S L 281954.89 P P M T ota l 281954.89
E xp. PPM PPM PPM
28
32
W ithin P e rform a nce < LS L 0.00 > U S L 379619.67 T ota l 379619.67
36 E xp. O v e ra ll P e rform a nce P P M < LS L 0.00 P P M > U S L 371895.18 P P M T ota l 371895.18
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1.24 2.37 0.11 0.11 0.35
15
Measure
Sigma Calculator: Continuous Data 1. Label The Normal Curve With The Following:
n n
Example:
n n
s
LSL = 8
X value X+s value USL & Shade Area To The Right LSL & Shade Area To The Left s
USL = 22
Area 2
Xbar 15 S 2 USL 18 LSL 5 Sigma = 3 Sigma
Area 1 10 18 x x+s
1.5
Example: The average processing time = 15 days (Xbar = 15)
=
0.933193
The standard deviation was 2 days (s = 2)
=
0.066807
=
-5
=
2.87E-07
Area 2 =
2.87E-07
2. Determine Area 1: Find Z1
Z1 =
USL – x s
=
(
)
–
(
(
)
=
)
702009 O ##
Look up Z1 in Normal Table
Norm Dist (Z1) = Normal Table Look Up for Z1
Area 1 = 1 – Look Up
Area 1 = 1 – Norm Dist (Z1) = 1 –
(
)
3. Skip this step if there is no LSL 2. Determine Area 2: LSL – x s
(
)
–
(
)
Find Z2
Z2 =
Look up Z2 in Normal Table
Norm Dist (Z2) = Normal Table Look Up for Z2
=
(
)
Area 2 = Look Up
4. Determine Total Area:
Total Area = Area 1 + Area 2 =
5. Yield = 1 – Total Area
Yield = 1 – Total Area = 1 –
(
(
)
+
(
)=
)
= x 100%
6. Process Sigma Comes From Table Look Up Of Yield
Enter Values in Yellow Mean (Average) Standard Deviation Delete if no USL (Upper Specification Limit) Delete if no LSL (Lower Specification Limit) Sigma Quality Level
SigmaST = Look Up Value in Sigma Table
A unit processed longer than 18 days was too late to the customer (USL = 18) A unit processed faster than 5 days was too early to the customer (LSL = 5)
Sigma Quality Level = 3
0.066807
=
0.933193
=
93.3193%
=
3.00
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16
Measure
Sigma Calculator: Discrete Data
General Worksheet For Calculating Process Sigma
1 Number Of Units Processed 2 Total Number Of Defects Made (Include Defects Made And Later Fixed) 3 Number Of Defect Opportunities Per Unit 4 Solve For Defects Per Million Opportunities (DPMO) 5 Look Up Process Sigma In Abridged Sigma Conversion Table
Enter Values Below in Yellow N= 200 D= 35 O= DPMO = #DIV/0! Sigma = #DIV/0!
Example: 200 pairs of boots were supplied (N = 200) 35 shoelaces were found broken (D = 35) Each shoe had 1 lace and there were 2 shoes per pair (O = 2)
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17
Measure
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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18
Measure
5s Sort
?
?
Set Order
?
?
Shine
5s FORM IDENTIFICATION ITEM NAME
TAG NUMBER
CLASSIFICATION
RAW MATERIAL
TOOLS
FURNITURE
WIP
SUPPLIES
OFFICE MATERIAL
EQUIPMENT
BOOKS/MAGAZINES
FINISHED GOOD
QUANTITY
UNNECESSARY
LEFTOVER MATERIAL
?
DEFECTIVE
UNKNOWN
NON-URGENT
OTHER (EXPLAIN) DISPOSITION REQUIRED
?
DISCARD
TRANSFER
?
LONG-TERM STORAGE
OTHER (EXPLAIN)
IN-CELL STORAGE
REDUCE
ACTION TAKEN
?
ACTION DESCRIPTION
?
NEW LOCATION
APPROVED BY DATE NEW CELL / AREA
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OTHER (EXPLAIN)
REASON
?
Sustain
CELL / AREA
Standardize
TAGGED BY
TAG DATE
19
Measure
PDCA
Plan: ? ? Do:
?
? ?
?
Check: ? ?
?
Act: ? ?
? Enter Key Slide Take Away (Key Point) Here
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20
Plan
Do
Check
Act
Measure
Benchmark Analysis CTQ
Process Capability (X/Y)
Benchmark
Gap / Opportunity
Source
Assumptions
Based on the information above, what is the performance objective*? • • •
Reduce defects by % Reduce long-term DPMO from Improve short-term Z from
to to
. .
*If you do not benchmark, performance standards are based on: • • •
For a process with ≤ 3 sigma level, decrease % defects by 10x. For a process with > 3 sigma level, decrease % defects by 2x. Other….please explain (corporate mandate, compliance/legal, VOC data, etc)
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21
Risks
ntim e
4.0
Bonacorsi Consulting
Pr
ic
e r de R el rL at ea io ns d Ti hi m p N e M ew an Pr ag od em uc en tD t ev el op m Br en an Pr t od d Im uc ag tO e ff e rin Pr g ox Br im ea ity dt h to C us to m er
al O
CTQ Importance
Sp ec i
%
D el iv C er om y pl et e W O rd ar er ra nt y R et ur In ns ve nt or y Tu C rn or s re ct In vo ic e
O
Key Buying Factor Analysis Company
22
Measure
Comp 1 Comp 2
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Comp 3
10.0
9.0
8.0
7.0
6.0
5.0
Measure
Sources of Waste Defect
Overproduction
Transportation
Sources of Waste Area 1
Area 1 Sub area 1
<Sub area 1>
Sub area 1
NVA Area 1
Processing
?
z
? z?
Area 1 Area 1
Sub area 1
Sub area 1
Motion
Inventory
z
?
z
?
Sub area 1
5%
< Insert your waste percentage as shown in pie chart >
z
Waiting
5%
5% 5%
30%
10%
40%
Defect Overproduction Transportation Waiting Inventory Motion Processing
z? z?
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23
Sample Value Stream Mapping Symbols Machining Quotes
Measure
C/T = 36 Sec
I
Set Up Time 7 Min
Queue/ Inventory
Uptime 86% Data Box Process Box
Flow (Physical) Flow (Information)
Electronic Information
1 Electronic Data System
Personnel
Truck Shipment
Physical Transport
Supplier/ Customer
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Physical Pull “Go See” Monitoring
Sign Off Point
Push Systems
Project Burst
Supermarket Replenishment 24
Kanban Station
FIFO FIFO Lane
Paper Kanban
Measure
Value Stream Map – “Current State” Service lead time = 384 min
Order Mgmt Supervisor
Weekly Update
Customer call time = 24 min
SUPPLIERS
CUSTOMER
Phone Call
Automate Monitoring
Large Business
Trigger: Completion Criteria: Cycle Time: Takt Time: Number of People: Number of Approvals: Items in Inbox: % Rework: # of Iterations (cycles): # of Databases: Top 3 Rework Issues: 1. 2. 3.
6 Customers
Small Business 5 Customers Simplify/ Mistake Proof
Home
3 Customers
Phone Call
Order Mgmt
4
Screen for Acct Mgr
P/T = 3 min 2-5
Forecast Improvement
Order Mgmt
Customer Info 4
Product Need
Lost calls=10% Volume=1200
day s
Simplify/ Combine
Improve Visibility Order Mgmt
Order Mgmt
Pricing
Shipping Info
4
4
P/T = 2 min
P/T = 6 Min
P/T = 6 Min
P/T = 2 Min
Error Rate=2% Volume=800
Error Rate=0% Volume=800
Error Rate=2% Volume=800
Error Rate=1% Volume=800
5 min 3 min
Order Mgmt
Manual Update
DIST 10
20 Orders
P/T = 120 Min Error Rate=1% Volume=1200
240 min 2 min
6 min
6 min
2 min
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Pick Pack & Ship
25
120 min
Measure
Value Stream Map (VSM) - “As Is” < ERP >
Questions
Supplier
Customer
What are the biggest Opportunity areas?
<Step1> 1
<Step3>
<Step3> 1
2
What is VA to NVA percentage?
What is the Takt Time?
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26
Measure
Value Stream Map (VSM) - “IDEAL” < ERP >
Questions What is the VA to NVA percentage?
Supplier
Customer
<Step1>
<Step3>
<Step3>
1
2
1
How was IDEAL future state VSM constructed?
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27
Measure
Spaghetti Diagram EAST Foyer
To Office Parking Lot
Lines indicate paper/information travel: - No set path
Order Entry 1
Order Entry 2
Order Entry 3
(paper and office supplies) Records Room
Printer, Fax
Order Taker 2
OM Lead
CC & Val 2
CC & Val 1
CC & Val 3
OM Supr Office
Order Taker 3
Restrooms
Cafeteria
Vault (finance) Engineering Offices Planning & Scheduling
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Reception
Room
(Order Management)
Indicates an in-box or outbox where work (forms/ information) waits to be worked on or transferred
Order Taker 1
Copier
- Lots of rework
Supply
28
AS-IS Process Mapping Symbols
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29
Measure
Oval shapes – Start/Stop of process Diamonds – Decision points Rectangles – process steps Half-Moon – Delay/Queue Time
Swim Lane Process Map Client Mgr
Client HR
Notify HR of employee exit date
Places information into HR database
Measure
Note: Steps in blue shapes are non-value added steps
Sends exit date to IT, telecom & facilities
Avg. Delay 2 days
Form require approval?
No
Re-verifies with mgr on employee’s exit status
Yes Client Contact
NT Admin
Secure approval(s)
Sends Email to Admin
Create ticket if request coming directly from client
Utilize e-mail vendor’s web tool to submit delete request to vendor
Avg. Delay 1 day
Avg. Delay 1 day
Admin
Email Vendor Bonacorsi Consulting
Delete account
Avg. Delay 2 days
Sends Email to Admin
Avg. Delay 2 days
Admin closes ticket and manager notified
Generates ticket & forwards to Admin
Avg. Delay 4 days
Mark request as completed on admin web site
30
Measure
Process Map Process Map (A 10 feet view) Inputs
C/N/Co/S
Process Steps
Outputs
Questions How was the data collected?
What is percentage of NVA to VA? Has the hidden factory been quantified?
FPY RTY
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31
Measure
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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32
Measure
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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33
Measure
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
34
Measure
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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35
Comments / Resolution
Measure
Stop
Tollgate Checklist
Tollgate Review
Does the team understand or has gathered the right data to help understand the process? Has the team baselined current process performance?
Measure
Has a more detailed Value Stream Map been completed to better understand the process and problem, and where in the process the root causes might reside?
Has the team conducted a value-added and cycle time analysis, identifying areas where time and resources are devoted to tasks not critical to the customer?
Has the team identified the specific input (x), process (x), and output (y) measures needing to be collected for both effectiveness and efficiency categories (i.e. Quality, Speed and Cost Efficiency measures)?
Has the team developed clear, unambiguous operational definitions for each measurement and tested them with others to ensure clarity and consistent interpretation?
Has a clear, reasonable choice been made between gathering new data or taking advantage of existing data?
Sample size & sampling frequency established to ensure valid representation of the process we’re measuring?
Measurement system checked for repeatability and reproducibility, potentially including training of data collectors?
Has the team developed & tested the data collection form for usability and that it can provide consistent, complete data?
Has baseline performance and process capability been established? How large is the gap between current performance and the customer (or project) requirements?
Has the team been able to identify any ‘Quick Wins’?
Has the team begun to fill in the financial benefits worksheets for type 1 and 2 savings?
Have any opportunities to do Kaizen projects been identified to accelerate momentum and results?
Have ‘learnings’ to-date required modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders?
New risks to project success have been identified, added to the Risk Mitigation Plan, & mitigation strategy put in place?
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36
Sign Off
Measure
• I concur that the Measure phase was successfully completed on MM/DD/YYYY • I concur the project is ready to proceed to next phase: Analyze
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
Enter Key Slide Take Away (Key Point) Here Bonacorsi Consulting
37
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
38
Lean Six Sigma Program Name Define
Measure Analyze Improve Control
LEAN SIX SIGMA
Abridged Process Sigma Conversion Table… Long-Term Yeild 99.99966% 99.9995% 99.9992% 99.9900% 99.8000% 99.9970% 99.9960% 99.9930% 99.9900% 99.9850% 99.9770% 99.9670% 99.9520% 99.9320% 99.9040% 99.8650% 99.8140% 99.7450% 99.6540% 99.5340% 99.3790% 99.181% 98.930% 98.610% 98.220% 97.730% 97.130% 96.410% 95.540% 94.520% 93.320% 91.920% 90.320% 88.50% 86.50% 84.20% 81.60% 78.80% 75.80% 72.60% 69.20% 65.60% 61.80% 58.00% 54.00% 50% 46% 43% 39% 35% 31% 28% 25% 22% 19% 16% 14% 12% 10% 8%
Bonacorsi Consulting
Process Sigma (ST) 6.0 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Defects Per 1,000,000 3.4 5 8 10 20 30 40 70 100 150 230 330 480 680 960 1350 1860 2550 3460 4660 6210 8190 10700 13900 17800 22700 28700 35900 44600 54800 66800 80800 96800 115000 135000 158000 184000 212000 242000 274000 308000 344000 382000 420000 460000 500000 540000 570000 610000 650000 690000 720000 750000 780000 810000 840000 860000 880000 900000 920000
Defects Per 100,000 0.34 0.5 0.8 1 2 3 4 7 10 15 23 33 48 68 96 135 186 255 346 466 621 819 1070 1390 1780 2270 2870 3590 4460 5480 6680 8080 9680 11500 13500 15800 18400 21200 24200 27400 30800 34400 38200 42000 46000 50000 54000 57000 61000 65000 69000 72000 75000 78000 81000 84000 86000 88000 90000 40 92000
Defects Per 10,000 0.034 0.05 0.08 0.1 0.2 0.3 0.4 0.7 1 1.5 2.3 3.3 4.8 6.8 9.6 13.5 18.6 25.5 34.6 46.6 62.1 81.9 107 139 178 227 287 359 446 548 668 808 968 1150 1350 1580 1840 2120 2420 2740 3080 3440 3820 4200 4600 5000 5400 5700 6100 6500 6900 7200 7500 7800 8100 8400 8600 8800 9000 9200
Defects Per 1,000 0.0034 0.005 0.008 0.01 0.02 0.03 0.04 0.07 0.1 0.15 0.23 0.33 0.48 0.68 0.96 1.35 1.86 2.55 3.46 4.66 6.21 8.19 10.7 13.9 17.8 22.7 28.7 35.9 44.6 54.8 66.8 80.8 96.8 115 135 158 184 212 242 274 308 344 382 420 460 500 540 570 610 650 690 720 750 780 810 840 860 880 900 920
Defects Per 100 0.00034 0.0005 0.0008 0.001 0.002 0.003 0.004 0.007 0.01 0.015 0.023 0.033 0.048 0.068 0.096 0.135 0.186 0.255 0.346 0.466 0.621 0.819 1.07 1.39 1.78 2.27 2.87 3.59 4.46 5.48 6.68 8.08 9.68 11.5 13.5 15.8 18.4 21.2 24.2 27.4 30.8 34.4 38.2 42 46 50 54 57 61 65 69 72 75 78 81 84 86 88 90 92
Table of the Standard Normal (z) Distribution z 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4
0.00 0.0000 0.0398 0.0793 0.1179 0.1554 0.1915 0.2257 0.2580 0.2881 0.3159 0.3413 0.3643 0.3849 0.4032 0.4192 0.4332 0.4452 0.4554 0.4641 0.4713 0.4772 0.4821 0.4861 0.4893 0.4918 0.4938 0.4953 0.4965 0.4974 0.4981 0.4987 0.4990 0.4993 0.4995 0.4997
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0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.0040 0.0438 0.0832 0.1217 0.1591 0.1950 0.2291 0.2611 0.2910 0.3186 0.3438 0.3665 0.3869 0.4049 0.4207 0.4345 0.4463 0.4564 0.4649 0.4719 0.4778 0.4826 0.4864 0.4896 0.4920 0.4940 0.4955 0.4966 0.4975 0.4982 0.4987 0.4991 0.4993 0.4995 0.4997
0.0080 0.0478 0.0871 0.1255 0.1628 0.1985 0.2324 0.2642 0.2939 0.3212 0.3461 0.3686 0.3888 0.4066 0.4222 0.4357 0.4474 0.4573 0.4656 0.4726 0.4783 0.4830 0.4868 0.4898 0.4922 0.4941 0.4956 0.4967 0.4976 0.4982 0.4987 0.4991 0.4994 0.4995 0.4997
0.0120 0.0517 0.0910 0.1293 0.1664 0.2019 0.2357 0.2673 0.2969 0.3238 0.3485 0.3708 0.3907 0.4082 0.4236 0.4370 0.4484 0.4582 0.4664 0.4732 0.4788 0.4834 0.4871 0.4901 0.4925 0.4943 0.4957 0.4968 0.4977 0.4983 0.4988 0.4991 0.4994 0.4996 0.4997
0.0160 0.0557 0.0948 0.1331 0.1700 0.2054 0.2389 0.2704 0.2995 0.3264 0.3508 0.3729 0.3925 0.4099 0.4251 0.4382 0.4495 0.4591 0.4671 0.4738 0.4793 0.4838 0.4875 0.4904 0.4927 0.4945 0.4959 0.4969 0.4977 0.4984 0.4988 0.4992 0.4994 0.4996 0.4997
0.0190 0.0596 0.0987 0.1368 0.1736 0.2088 0.2422 0.2734 0.3023 0.3289 0.3513 0.3749 0.3944 0.4115 0.4265 0.4394 0.4505 0.4599 0.4678 0.4744 0.4798 0.4842 0.4878 0.4906 0.4929 0.4946 0.4960 0.4970 0.4978 0.4984 0.4989 0.4992 0.4994 0.4996 0.4997
0.0239 0.0636 0.1026 0.1406 0.1772 0.2123 0.2454 0.2764 0.3051 0.3315 0.3554 0.3770 0.3962 0.4131 0.4279 0.4406 0.4515 0.4608 0.4686 0.4750 0.4803 0.4846 0.4881 0.4909 0.4931 0.4948 0.4961 0.4971 0.4979 0.4985 0.4989 0.4992 0.4994 0.4996 0.4997
0.0279 0.0675 0.1064 0.1443 0.1808 0.2157 0.2486 0.2794 0.3078 0.3340 0.3577 0.3790 0.3980 0.4147 0.4292 0.4418 0.4525 0.4616 0.4693 0.4756 0.4808 0.4850 0.4884 0.4911 0.4932 0.4949 0.4962 0.4972 0.4979 0.4985 0.4989 0.4992 0.4995 0.4996 0.4997
0.0319 0.0714 0.1103 0.1480 0.1844 0.2190 0.2517 0.2823 0.3106 0.3365 0.3529 0.3810 0.3997 0.4162 0.4306 0.4429 0.4535 0.4625 0.4699 0.4761 0.4812 0.4854 0.4887 0.4913 0.4934 0.4951 0.4963 0.4973 0.4980 0.4986 0.4990 0.4993 0.4995 0.4996 0.4997
0.0359 0.0753 0.1141 0.1517 0.1879 0.2224 0.2549 0.2852 0.3133 0.3389 0.3621 0.3830 0.4015 0.4177 0.4319 0.4441 0.4545 0.4633 0.4706 0.4767 0.4817 0.4857 0.4890 0.4916 0.4936 0.4952 0.4964 0.4974 0.4981 0.4986 0.4990 0.4993 0.4995 0.4997 0.4998
41
Measure Analyze Improve Control
LEAN SIX SIGMA
Lean Six Sigma Program Name Define
Measure Analyze Improve Control
LEAN SIX SIGMA
Measure Phase Road Map Measure
Define
Analyze
Activities
• • • • • • • •
• • Develop Operational Definitions • Develop Data Collection Plan • Validate Measurement System • • Collect Baseline Data • Determine Process Performance/Capability • Validate Business Opportunity • Value Stream Map for deeper understanding & focus • • Quick Wins (Control Plans) • Measure Gate Review •
Bonacorsi Consulting
Improve
Control
Tools
• Identify Key Input, Process and Output Metrics •
Measure
3
High/Low level process Maps Value Stream Map Operational Definitions Data Collection Plan Statistical Sampling Measurement System Analysis (MSA), Gage R&R Constraint Identification Setup Reduction Generic Pull Kaizen TPM Control Charts Process Capability, Cp & Cpk
Project Charter
Measure
Problem/Goal Statement Problem: Describe problem in non-technical terms Statement should explain why project is important; why working on it is a priority Goal: Goals communicate “before” and “after” conditions Shift mean, variance, or both? Should impact cost, time, quality dimensions Express goals using SMART criteria Specific, Measurable, Attainable, Resource Requirements, Time Boundaries Explain leverage and strategic implications (if any)
Financial Impact
State financial impact of project Expenses Investments (inventory, capital, A/R) Revenues
Separate “hard” from “soft” dollars
State financial impact of leverage opportunities (future projects)
Team PES Name PS Name DC Name GB/BB Name MBB Name Core Team Team Member 1 Team Member 2 Team Member 3 Team Member 4 Extended Team Team Member 1 Team Member 2
Tollgate Review Schedule
Project Executive Sponsor (if different from PS) Project Sponsor/Process Owner Deployment Champion Green Belt/Black Belt Master Black Belt Role % Contrib. LSS Training SME XX YB TM XX GB SME XX PS SME XX YB
Tollgate
Scheduled
Revised
Complete
Define:
XX/XX/XX
-
XX/XX/XX
Measure:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Analyze:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Improve:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Control:
XX/XX/XX
XX/XX/XX
XX/XX/XX
Review high-level schedule milestones here:
BFM IT
Bonacorsi Consulting
XX XX
Not Trained Not Trained
4
Phase Completions Tollgate Reviews Trials
Measure
Attitude Charting & Key Constituency Map Key Constituents Map Customer 13%
Operations 25%
“Critical mass must be won-over”
HR 25%
Finance 37%
Attitude Charting
40% 35%
35%
35% 30% 25% 20% 15%
15%
15% 10% 5% 0% Innovators
Early Adopters
Late Adopters
Resistors
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5
Sample Size: Continuous Data
Measure
Use this calculation for CONTINUOUS data 1.96 (Confidence Interval: Za/2)
Desired Confidence Level (90%, 95%, 99%, etc)?
95
* Population Standard Deviation (# hours/days/etc)?
3
3 (Sigma)
** What Precision is Required (within # hours/days/etc)?
1
1 (Delta)
Minimum Sample Size (n):
34
n = ((Za/2 * Sigma)/Delta)^2
* Population Standard Deviation: Your best estim ate based on your know ledge of the data or previous sam ples of sim ilar data. Exam ple - based on sim ilar data from a previous project you assum e Std Dev for call length = 5 m inutes. ** Error: Exam ple - if you w ant to estim ate average call length w ithin +/- 1 m inute, you w ould specify an error of 1.
Finite Population Correction (if n/N >0.05 or 5% where N = Total Population Size): n= N=
34 400
n (finite) =
31
Calculated minimum sample size (above) Total population size n (finite) = n / (1 + n/N)
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6
Sample Size - Discrete Data
Measure
Use this calculation for "YES/NO," "TRUE/FALSE," "DEFECTIVE/NOT DEFECTIVE" (binomial or discrete) types of data. Use only when n * PB >= 5 Desired Confidence Level (90%, 95%, 99%, etc)?
95
* What Precision is Required (within 5, 10, 15%, etc)?
1
** Defective Data (50%, 40%, 30%, 20%, 15%, etc)?
0.2
Minimum Sample Size (n):
76
1.96 (Confidence Interval: Za/2) 0.01 (Delta) 0.002 (P B)
n = (Za/2/Delta)^2 * P B (1-P B )
* Error: Exam ple - if you w ant to es tim ate the proportion defective w ithin +/-2%, you w ould s pe cify an e rror of 2. ** Defective Data: Your es tim ated pe rce ntage of the overall population that w ould be cons ide re d "de fe ctive " data. Exam ple - bas e d on the spec, you e xpect a de fe ct rate of 12%. If you don't know and w ant to be cons ervative, use 50%
Finite Population Correction (if n/N >0.05 or 5% where N = Total Population Size): n= N= n (finite) = defects expected:
76 925 70 0
Calculated minimum sample size (above) Total population size n (finite) = n / (1 + n/N)
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7
Measure
Data Collection Plan Performance Measure
Operational Definition
Data Source and Location
How Will Data Be Collected
Who Will Collect Data
When Will Data Be Collected
Sample Size
Stratification Factors
VOC MSA Process VSM Financials Others
For each performance measure (Y), update a data collection plan
Include MSA measure plan (Gantt chart, MS project plan is Optional)
Add Financial measure plan if separate from performance Y
Add any Time Study or other data collection plans for Value Stream Map
Sample Size Calculation
Use additional slides if needed
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8
How will data be used?
Measure
Operational Definitions
Y – Continuous data (Process start/stop and cycle time boundaries (such as the unit of measure (ex minutes), the unit (the thing you are measuring), will you include weekends, holidays, non-business hours?)
Y – Discrete data (Define Success/Defect or other attribute values you will measure
X – The subgroups values or X-factor groupings you will use on your project data collection
Other unique terms that apply to your project that require clear operational definitions
Use additional slides as needed to complete your operational definitions Enter Key Slide Take Away (Key Point) Here
Bonacorsi Consulting
9
Measure
Measurement Systems Analysis (MSA) Gage R&R (ANOVA) for Response
Gage R&R Source Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation
%Contribution VarComp (of VarComp) 0.0015896 3.70 0.0005567 1.29 0.0010330 2.40 0.0003418 0.79 0.0006912 1.61 0.0414247 96.30 0.0430143 100.00
Source Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation
Study Var %Study Var StdDev (SD) (6 * SD) (%SV) 0.039870 0.23922 19.22 0.023594 0.14156 11.38 0.032140 0.19284 15.50 0.018488 0.11093 8.91 0.026290 0.15774 12.68 0.203531 1.22118 98.13 0.207399 1.24439 100.00
Components of Variation % Contribution
P e rc e nt
% Study Var
9.75 9.50
0
Gage R&R
Repeat
Reprod
1
Part-to-Part
2
3
Sa m ple R a nge
2
3
0.10
7
8
9
10
10.00 9.75
0.00
LCL=0
1
2 Operator
Xbar Chart by Operator 2
UCL=9.8422 __ X=9.7996 LCL=9.7569
9.75
3
Operator * Part Interaction
3
Operator
10.00 A v e ra ge
Sa m ple M e a n
6
9.50
10.00
1 2
9.75
3
9.50
9.50
1
2
3
4
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5
Response by Operator UCL=0.1073
_ R=0.0417
0.05
4
Part
R Chart by Operator 1
1
Measurement system is acceptable with the Total % Contribution <10%
10.00
50
Number of Distinct Categories = 7
Response by Part
100
10
5 6 Part
7
8
9 10
Measure
MSA Conclusions
The measurement systems are acceptable. The data is considered to have no potential for significant error. Need to be careful to appropriately use the data during the Analyze Phase. Type of Measurement Error
Description
Considerations to this Project
Discrimination (resolution)
The ability of the measurement system to divide into “data categories”
Work hrs can be measured to <.25 hrs. Tool usage measure to +- 2 min.
Bias
The difference between an observed average measurement result and a reference value
No bias - Work hours and radar startstop times consistent through population.
Stability
The change in bias over time
No bias of work hrs & radar usage data.
The extent variability is consistent
Not an issue. Labor and radar usage is historical and felt to be accurate enough for insight and analysis.
Reproducibility
Different appraisers produce consistent results
Remarks in usage data deemed not reproducible, therefore were not considered in determining which radars were used in each op
Variation
The difference between parts
n/a to this process.
Repeatability
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11
Measure
Probability Plot Probability Plot of Anderson-Darling Normality Normal - 95% CI 99.9 99 95
Percent
90 80 70 60 50 40 30 20
Mean StDev N AD P-Value
24.74 4.177 100 0.380 0.397
10 5 1 0.1
10
15
20
25 QTY
30
35
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12
40
Measure
Baseline Basic Statistics
The current process has a non-normal distribution with the P-Value < 0.05 but does have a normal bell-shape.
Summary for Delivery Time
Since the mean and median are the same in days (29) +/- 0.5 days, we will not transform data.
24
26
28
30
32
A nderson-Darling Normality Test
34
A -Squared P-V alue <
1.95 0.005
Mean StDev V ariance Skew ness Kurtosis N
29.128 2.677 7.169 0.201075 -0.471714 266
Minimum 1st Q uartile Median 3rd Q uartile Maximum
24.000 27.000 29.000 31.000 35.000
95% C onfidence Interv al for Mean
The range is 35 and the standard deviation is 2.7 days
28.805
95% C onfidence Interv al for Median 29.000
29.000
95% C onfidence Interv al for StDev 9 5 % Confidence Intervals
2.468
Mean Median 28.8
28.9
29.0
29.1
29.2
29.3
29.4
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29.451
13
2.927
Measure
Control Chart
255 data points collected with zero subgroups, thus the I&MR control chart selected
I-MR Chart of Delivery Time 40 UCL=37.70 Individual V alue
The current baseline delivery time is stable over time with both the Moving Range (MR) (3.22 days) and Individual Average (29.13 days) experiencing common cause variation
35 _ X=29.13
30 25
LCL=20.56
20 1
28
55
82
109
136 Observation
163
190
244
UCL=10.53
7.5 5.0
__ MR=3.22
2.5
LCL=0
0.0 1
28
55
82
109
136 Observation
163
190
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217
10.0 M oving Range
14
217
244
Baseline Process Capability
Measure
P r ocess Capability of Deliv ery Time
266 data points collected between 11/1/04 thru 11/30/04
LS L
T arg et
USL
W ith in O v erall
Mean 29 days, St. Dev. 2.9 days, CP is 1.16 indicating process needs centering to the LSL of 10 and USL of 30 days. Cpk is .1 indicating that the process is exceeding the USL.
P ote ntia l (W ithin) C a pa bility Cp 1.16 C PL 2.22 C PU 0.10 C pk 0.10 C C pk 1.16 O v e ra ll C a pa bility Pp PPL PPU P pk C pm
With an overall PPM of 371,895 defects per million opportunity, the current process has a Sigma Quality Level of 1.8 or a 62% yield
P roce ss D a ta LS L 10 T a rge t 20 USL 30 S a m ple M e a n 29.1203 S a m ple N 266 S tD e v (W ithin) 2.87033 S tD e v (O v e ra ll) 2.69154
12
16
20
24
O bse rv e d P e rform a nce P P M < LS L 0.00 P P M > U S L 281954.89 P P M T ota l 281954.89
E xp. PPM PPM PPM
28
32
W ithin P e rform a nce < LS L 0.00 > U S L 379619.67 T ota l 379619.67
36 E xp. O v e ra ll P e rform a nce P P M < LS L 0.00 P P M > U S L 371895.18 P P M T ota l 371895.18
Enter Key Slide Take Away (Key Point) Here Bonacorsi Consulting
1.24 2.37 0.11 0.11 0.35
15
Measure
Sigma Calculator: Continuous Data 1. Label The Normal Curve With The Following:
n n
Example:
n n
s
LSL = 8
X value X+s value USL & Shade Area To The Right LSL & Shade Area To The Left s
USL = 22
Area 2
Xbar 15 S 2 USL 18 LSL 5 Sigma = 3 Sigma
Area 1 10 18 x x+s
1.5
Example: The average processing time = 15 days (Xbar = 15)
=
0.933193
The standard deviation was 2 days (s = 2)
=
0.066807
=
-5
=
2.87E-07
Area 2 =
2.87E-07
2. Determine Area 1: Find Z1
Z1 =
USL – x s
=
(
)
–
(
(
)
=
)
702009 O ##
Look up Z1 in Normal Table
Norm Dist (Z1) = Normal Table Look Up for Z1
Area 1 = 1 – Look Up
Area 1 = 1 – Norm Dist (Z1) = 1 –
(
)
3. Skip this step if there is no LSL 2. Determine Area 2: LSL – x s
(
)
–
(
)
Find Z2
Z2 =
Look up Z2 in Normal Table
Norm Dist (Z2) = Normal Table Look Up for Z2
=
(
)
Area 2 = Look Up
4. Determine Total Area:
Total Area = Area 1 + Area 2 =
5. Yield = 1 – Total Area
Yield = 1 – Total Area = 1 –
(
(
)
+
(
)=
)
= x 100%
6. Process Sigma Comes From Table Look Up Of Yield
Enter Values in Yellow Mean (Average) Standard Deviation Delete if no USL (Upper Specification Limit) Delete if no LSL (Lower Specification Limit) Sigma Quality Level
SigmaST = Look Up Value in Sigma Table
A unit processed longer than 18 days was too late to the customer (USL = 18) A unit processed faster than 5 days was too early to the customer (LSL = 5)
Sigma Quality Level = 3
0.066807
=
0.933193
=
93.3193%
=
3.00
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16
Measure
Sigma Calculator: Discrete Data
General Worksheet For Calculating Process Sigma
1 Number Of Units Processed 2 Total Number Of Defects Made (Include Defects Made And Later Fixed) 3 Number Of Defect Opportunities Per Unit 4 Solve For Defects Per Million Opportunities (DPMO) 5 Look Up Process Sigma In Abridged Sigma Conversion Table
Enter Values Below in Yellow N= 200 D= 35 O= DPMO = #DIV/0! Sigma = #DIV/0!
Example: 200 pairs of boots were supplied (N = 200) 35 shoelaces were found broken (D = 35) Each shoe had 1 lace and there were 2 shoes per pair (O = 2)
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Measure
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
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Measure
5s Sort
?
?
Set Order
?
?
Shine
5s FORM IDENTIFICATION ITEM NAME
TAG NUMBER
CLASSIFICATION
RAW MATERIAL
TOOLS
FURNITURE
WIP
SUPPLIES
OFFICE MATERIAL
EQUIPMENT
BOOKS/MAGAZINES
FINISHED GOOD
QUANTITY
UNNECESSARY
LEFTOVER MATERIAL
?
DEFECTIVE
UNKNOWN
NON-URGENT
OTHER (EXPLAIN) DISPOSITION REQUIRED
?
DISCARD
TRANSFER
?
LONG-TERM STORAGE
OTHER (EXPLAIN)
IN-CELL STORAGE
REDUCE
ACTION TAKEN
?
ACTION DESCRIPTION
?
NEW LOCATION
APPROVED BY DATE NEW CELL / AREA
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OTHER (EXPLAIN)
REASON
?
Sustain
CELL / AREA
Standardize
TAGGED BY
TAG DATE
19
Measure
PDCA
Plan: ? ? Do:
?
? ?
?
Check: ? ?
?
Act: ? ?
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Plan
Do
Check
Act
Measure
Benchmark Analysis CTQ
Process Capability (X/Y)
Benchmark
Gap / Opportunity
Source
Assumptions
Based on the information above, what is the performance objective*? • • •
Reduce defects by % Reduce long-term DPMO from Improve short-term Z from
to to
. .
*If you do not benchmark, performance standards are based on: • • •
For a process with ≤ 3 sigma level, decrease % defects by 10x. For a process with > 3 sigma level, decrease % defects by 2x. Other….please explain (corporate mandate, compliance/legal, VOC data, etc)
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Risks
ntim e
4.0
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Pr
ic
e r de R el rL at ea io ns d Ti hi m p N e M ew an Pr ag od em uc en tD t ev el op m Br en an Pr t od d Im uc ag tO e ff e rin Pr g ox Br im ea ity dt h to C us to m er
al O
CTQ Importance
Sp ec i
%
D el iv C er om y pl et e W O rd ar er ra nt y R et ur In ns ve nt or y Tu C rn or s re ct In vo ic e
O
Key Buying Factor Analysis Company
22
Measure
Comp 1 Comp 2
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Comp 3
10.0
9.0
8.0
7.0
6.0
5.0
Measure
Sources of Waste Defect
Overproduction
Transportation
Sources of Waste Area 1
Area 1 Sub area 1
<Sub area 1>
Sub area 1
NVA Area 1
Processing
?
z
? z?
Area 1 Area 1
Sub area 1
Sub area 1
Motion
Inventory
z
?
z
?
Sub area 1
5%
< Insert your waste percentage as shown in pie chart >
z
Waiting
5%
5% 5%
30%
10%
40%
Defect Overproduction Transportation Waiting Inventory Motion Processing
z? z?
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Sample Value Stream Mapping Symbols Machining Quotes
Measure
C/T = 36 Sec
I
Set Up Time 7 Min
Queue/ Inventory
Uptime 86% Data Box Process Box
Flow (Physical) Flow (Information)
Electronic Information
1 Electronic Data System
Personnel
Truck Shipment
Physical Transport
Supplier/ Customer
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Physical Pull “Go See” Monitoring
Sign Off Point
Push Systems
Project Burst
Supermarket Replenishment 24
Kanban Station
FIFO FIFO Lane
Paper Kanban
Measure
Value Stream Map – “Current State” Service lead time = 384 min
Order Mgmt Supervisor
Weekly Update
Customer call time = 24 min
SUPPLIERS
CUSTOMER
Phone Call
Automate Monitoring
Large Business
Trigger: Completion Criteria: Cycle Time: Takt Time: Number of People: Number of Approvals: Items in Inbox: % Rework: # of Iterations (cycles): # of Databases: Top 3 Rework Issues: 1. 2. 3.
6 Customers
Small Business 5 Customers Simplify/ Mistake Proof
Home
3 Customers
Phone Call
Order Mgmt
4
Screen for Acct Mgr
P/T = 3 min 2-5
Forecast Improvement
Order Mgmt
Customer Info 4
Product Need
Lost calls=10% Volume=1200
day s
Simplify/ Combine
Improve Visibility Order Mgmt
Order Mgmt
Pricing
Shipping Info
4
4
P/T = 2 min
P/T = 6 Min
P/T = 6 Min
P/T = 2 Min
Error Rate=2% Volume=800
Error Rate=0% Volume=800
Error Rate=2% Volume=800
Error Rate=1% Volume=800
5 min 3 min
Order Mgmt
Manual Update
DIST 10
20 Orders
P/T = 120 Min Error Rate=1% Volume=1200
240 min 2 min
6 min
6 min
2 min
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Pick Pack & Ship
25
120 min
Measure
Value Stream Map (VSM) - “As Is” < ERP >
Questions
Supplier
Customer
What are the biggest Opportunity areas?
<Step1> 1
<Step3>
<Step3> 1
2
What is VA to NVA percentage?
What is the Takt Time?
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Measure
Value Stream Map (VSM) - “IDEAL” < ERP >
Questions What is the VA to NVA percentage?
Supplier
Customer
<Step1>
<Step3>
<Step3>
1
2
1
How was IDEAL future state VSM constructed?
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Measure
Spaghetti Diagram EAST Foyer
To Office Parking Lot
Lines indicate paper/information travel: - No set path
Order Entry 1
Order Entry 2
Order Entry 3
(paper and office supplies) Records Room
Printer, Fax
Order Taker 2
OM Lead
CC & Val 2
CC & Val 1
CC & Val 3
OM Supr Office
Order Taker 3
Restrooms
Cafeteria
Vault (finance) Engineering Offices Planning & Scheduling
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Reception
Room
(Order Management)
Indicates an in-box or outbox where work (forms/ information) waits to be worked on or transferred
Order Taker 1
Copier
- Lots of rework
Supply
28
AS-IS Process Mapping Symbols
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29
Measure
Oval shapes – Start/Stop of process Diamonds – Decision points Rectangles – process steps Half-Moon – Delay/Queue Time
Swim Lane Process Map Client Mgr
Client HR
Notify HR of employee exit date
Places information into HR database
Measure
Note: Steps in blue shapes are non-value added steps
Sends exit date to IT, telecom & facilities
Avg. Delay 2 days
Form require approval?
No
Re-verifies with mgr on employee’s exit status
Yes Client Contact
NT Admin
Secure approval(s)
Sends Email to Admin
Create ticket if request coming directly from client
Utilize e-mail vendor’s web tool to submit delete request to vendor
Avg. Delay 1 day
Avg. Delay 1 day
Admin
Email Vendor Bonacorsi Consulting
Delete account
Avg. Delay 2 days
Sends Email to Admin
Avg. Delay 2 days
Admin closes ticket and manager notified
Generates ticket & forwards to Admin
Avg. Delay 4 days
Mark request as completed on admin web site
30
Measure
Process Map Process Map (A 10 feet view) Inputs
C/N/Co/S
Process Steps
Outputs
Questions How was the data collected?
What is percentage of NVA to VA?
FPY
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Measure
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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32
Measure
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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Measure
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
34
Measure
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
Measure
Stop
Tollgate Checklist
Tollgate Review
Does the team understand or has gathered the right data to help understand the process? Has the team baselined current process performance?
Measure
Has a more detailed Value Stream Map been completed to better understand the process and problem, and where in the process the root causes might reside?
Has the team conducted a value-added and cycle time analysis, identifying areas where time and resources are devoted to tasks not critical to the customer?
Has the team identified the specific input (x), process (x), and output (y) measures needing to be collected for both effectiveness and efficiency categories (i.e. Quality, Speed and Cost Efficiency measures)?
Has the team developed clear, unambiguous operational definitions for each measurement and tested them with others to ensure clarity and consistent interpretation?
Has a clear, reasonable choice been made between gathering new data or taking advantage of existing data?
Sample size & sampling frequency established to ensure valid representation of the process we’re measuring?
Measurement system checked for repeatability and reproducibility, potentially including training of data collectors?
Has the team developed & tested the data collection form for usability and that it can provide consistent, complete data?
Has baseline performance and process capability been established? How large is the gap between current performance and the customer (or project) requirements?
Has the team been able to identify any ‘Quick Wins’?
Has the team begun to fill in the financial benefits worksheets for type 1 and 2 savings?
Have any opportunities to do Kaizen projects been identified to accelerate momentum and results?
Have ‘learnings’ to-date required modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders?
New risks to project success have been identified, added to the Risk Mitigation Plan, & mitigation strategy put in place?
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Sign Off
Measure
• I concur that the Measure phase was successfully completed on MM/DD/YYYY • I concur the project is ready to proceed to next phase: Analyze
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
38
Lean Six Sigma Program Name Define
Measure Analyze Improve Control
LEAN SIX SIGMA
Abridged Process Sigma Conversion Table… Long-Term Yeild 99.99966% 99.9995% 99.9992% 99.9900% 99.8000% 99.9970% 99.9960% 99.9930% 99.9900% 99.9850% 99.9770% 99.9670% 99.9520% 99.9320% 99.9040% 99.8650% 99.8140% 99.7450% 99.6540% 99.5340% 99.3790% 99.181% 98.930% 98.610% 98.220% 97.730% 97.130% 96.410% 95.540% 94.520% 93.320% 91.920% 90.320% 88.50% 86.50% 84.20% 81.60% 78.80% 75.80% 72.60% 69.20% 65.60% 61.80% 58.00% 54.00% 50% 46% 43% 39% 35% 31% 28% 25% 22% 19% 16% 14% 12% 10% 8%
Bonacorsi Consulting
Process Sigma (ST) 6.0 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 3.5 3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.0 1.9 1.8 1.7 1.6 1.5 1.4 1.3 1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1
Defects Per 1,000,000 3.4 5 8 10 20 30 40 70 100 150 230 330 480 680 960 1350 1860 2550 3460 4660 6210 8190 10700 13900 17800 22700 28700 35900 44600 54800 66800 80800 96800 115000 135000 158000 184000 212000 242000 274000 308000 344000 382000 420000 460000 500000 540000 570000 610000 650000 690000 720000 750000 780000 810000 840000 860000 880000 900000 920000
Defects Per 100,000 0.34 0.5 0.8 1 2 3 4 7 10 15 23 33 48 68 96 135 186 255 346 466 621 819 1070 1390 1780 2270 2870 3590 4460 5480 6680 8080 9680 11500 13500 15800 18400 21200 24200 27400 30800 34400 38200 42000 46000 50000 54000 57000 61000 65000 69000 72000 75000 78000 81000 84000 86000 88000 90000 40 92000
Defects Per 10,000 0.034 0.05 0.08 0.1 0.2 0.3 0.4 0.7 1 1.5 2.3 3.3 4.8 6.8 9.6 13.5 18.6 25.5 34.6 46.6 62.1 81.9 107 139 178 227 287 359 446 548 668 808 968 1150 1350 1580 1840 2120 2420 2740 3080 3440 3820 4200 4600 5000 5400 5700 6100 6500 6900 7200 7500 7800 8100 8400 8600 8800 9000 9200
Defects Per 1,000 0.0034 0.005 0.008 0.01 0.02 0.03 0.04 0.07 0.1 0.15 0.23 0.33 0.48 0.68 0.96 1.35 1.86 2.55 3.46 4.66 6.21 8.19 10.7 13.9 17.8 22.7 28.7 35.9 44.6 54.8 66.8 80.8 96.8 115 135 158 184 212 242 274 308 344 382 420 460 500 540 570 610 650 690 720 750 780 810 840 860 880 900 920
Defects Per 100 0.00034 0.0005 0.0008 0.001 0.002 0.003 0.004 0.007 0.01 0.015 0.023 0.033 0.048 0.068 0.096 0.135 0.186 0.255 0.346 0.466 0.621 0.819 1.07 1.39 1.78 2.27 2.87 3.59 4.46 5.48 6.68 8.08 9.68 11.5 13.5 15.8 18.4 21.2 24.2 27.4 30.8 34.4 38.2 42 46 50 54 57 61 65 69 72 75 78 81 84 86 88 90 92
Table of the Standard Normal (z) Distribution z 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 3.1 3.2 3.3 3.4
0.00 0.0000 0.0398 0.0793 0.1179 0.1554 0.1915 0.2257 0.2580 0.2881 0.3159 0.3413 0.3643 0.3849 0.4032 0.4192 0.4332 0.4452 0.4554 0.4641 0.4713 0.4772 0.4821 0.4861 0.4893 0.4918 0.4938 0.4953 0.4965 0.4974 0.4981 0.4987 0.4990 0.4993 0.4995 0.4997
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0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.0040 0.0438 0.0832 0.1217 0.1591 0.1950 0.2291 0.2611 0.2910 0.3186 0.3438 0.3665 0.3869 0.4049 0.4207 0.4345 0.4463 0.4564 0.4649 0.4719 0.4778 0.4826 0.4864 0.4896 0.4920 0.4940 0.4955 0.4966 0.4975 0.4982 0.4987 0.4991 0.4993 0.4995 0.4997
0.0080 0.0478 0.0871 0.1255 0.1628 0.1985 0.2324 0.2642 0.2939 0.3212 0.3461 0.3686 0.3888 0.4066 0.4222 0.4357 0.4474 0.4573 0.4656 0.4726 0.4783 0.4830 0.4868 0.4898 0.4922 0.4941 0.4956 0.4967 0.4976 0.4982 0.4987 0.4991 0.4994 0.4995 0.4997
0.0120 0.0517 0.0910 0.1293 0.1664 0.2019 0.2357 0.2673 0.2969 0.3238 0.3485 0.3708 0.3907 0.4082 0.4236 0.4370 0.4484 0.4582 0.4664 0.4732 0.4788 0.4834 0.4871 0.4901 0.4925 0.4943 0.4957 0.4968 0.4977 0.4983 0.4988 0.4991 0.4994 0.4996 0.4997
0.0160 0.0557 0.0948 0.1331 0.1700 0.2054 0.2389 0.2704 0.2995 0.3264 0.3508 0.3729 0.3925 0.4099 0.4251 0.4382 0.4495 0.4591 0.4671 0.4738 0.4793 0.4838 0.4875 0.4904 0.4927 0.4945 0.4959 0.4969 0.4977 0.4984 0.4988 0.4992 0.4994 0.4996 0.4997
0.0190 0.0596 0.0987 0.1368 0.1736 0.2088 0.2422 0.2734 0.3023 0.3289 0.3513 0.3749 0.3944 0.4115 0.4265 0.4394 0.4505 0.4599 0.4678 0.4744 0.4798 0.4842 0.4878 0.4906 0.4929 0.4946 0.4960 0.4970 0.4978 0.4984 0.4989 0.4992 0.4994 0.4996 0.4997
0.0239 0.0636 0.1026 0.1406 0.1772 0.2123 0.2454 0.2764 0.3051 0.3315 0.3554 0.3770 0.3962 0.4131 0.4279 0.4406 0.4515 0.4608 0.4686 0.4750 0.4803 0.4846 0.4881 0.4909 0.4931 0.4948 0.4961 0.4971 0.4979 0.4985 0.4989 0.4992 0.4994 0.4996 0.4997
0.0279 0.0675 0.1064 0.1443 0.1808 0.2157 0.2486 0.2794 0.3078 0.3340 0.3577 0.3790 0.3980 0.4147 0.4292 0.4418 0.4525 0.4616 0.4693 0.4756 0.4808 0.4850 0.4884 0.4911 0.4932 0.4949 0.4962 0.4972 0.4979 0.4985 0.4989 0.4992 0.4995 0.4996 0.4997
0.0319 0.0714 0.1103 0.1480 0.1844 0.2190 0.2517 0.2823 0.3106 0.3365 0.3529 0.3810 0.3997 0.4162 0.4306 0.4429 0.4535 0.4625 0.4699 0.4761 0.4812 0.4854 0.4887 0.4913 0.4934 0.4951 0.4963 0.4973 0.4980 0.4986 0.4990 0.4993 0.4995 0.4996 0.4997
0.0359 0.0753 0.1141 0.1517 0.1879 0.2224 0.2549 0.2852 0.3133 0.3389 0.3621 0.3830 0.4015 0.4177 0.4319 0.4441 0.4545 0.4633 0.4706 0.4767 0.4817 0.4857 0.4890 0.4916 0.4936 0.4952 0.4964 0.4974 0.4981 0.4986 0.4990 0.4993 0.4995 0.4997 0.4998
41
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