Identify and develop the current state “as · 5/4/2020 · 2 2 Review and amend cost of poor quality ... Review problem statement and project objective Develop project measure

1

Identify and develop the current state “as-is” value stream process map

Confirm KPOV’s and Identify Key Process Input Variables (KPIV’s)

Define performance standards and the defect

Identify sources of existing data

Establish priority and plan data collection of non-existent data

Conduct measurement system analysis (MSA)

Collect baseline data

22

Review and amend cost of poor quality (COPQ) estimates

Evaluate process capability and performance

Review problem statement and project objective

Develop project measure phase tollgate presentation

3

Process Value Stream Mapping

Check sheets, Data Collection Worksheets, Process Observation

Measurement Systems Analysis

Gage R & R

Baseline performance analysis

QI Macros Statistical Software◦ Histogram with cp/cpk, DPMO

◦ Pareto Chart

◦ Capability Analysis

4

Phase Objectives Key Activities Possible Tools and Techniques Key Deliverables Document the problem statement and establish the charter. Demonstrate alignment with the Business metrics and strategies. Determine Customer requirements and performance standards.

▪ Select Team with Champion

▪ Develop problem statement

▪ Develop Charter ▪ Create SIPOC ▪ Address gap between VOC

and process ▪ Estimate financial benefits

▪ Problem Statements ▪ Project Charter ▪ SIPOC map ▪ COPQ or CODND ▪ Communication Plan

Develop a reliable and valid measurement system of the business process to effectively evaluate the success of meeting customer requirements.

▪ Obtain Customer requirements

▪ Create overall project plan ▪ Develop measurement

plan & compile project metrics

▪ Determine defect tracking requirements

▪ Assess baseline performance-estimate process capability

▪ Measurement Systems Analysis

▪ Process Description ▪ Project Plan & Timeline ▪ Metrics and collection plan ▪ Baseline Performance results ▪ Process capability analysis ▪ Lean Tools Assessment ▪ Measurement Systems

Analysis ▪ Process model – ‘as is’

Utilization of data techniques to gain insight into process. Divide data into groups based on key characteristics and assess the root causes of errors and poor performance. Determine where to focus efforts for improvement.

▪ Statistical tests / tools ▪ FMEA ▪ Pareto chart ▪ Correlation/Regression ▪ Fishbone Diagram ▪ Box plot ▪ Hypothesis Testing

▪ Describe findings – identify potential root causes RCA

▪ Validate findings

▪ Data relationships ▪ Validated Key Input Variables

(KPIVs) & Key Output Variables (KPOVs)

▪ Prioritize sources of variation ▪ root causes

▪ Identify & communicate potential improvements

▪ Summarize benefits & annualized financial benefits

Identify key change opportunities and proactively test for optimization. Develop implementation and communication plan including a change management approach to assist the organization in adaptation of the improvements.

▪ Design of Experiments – describe purpose & build test/ analysis strategy

▪ Evaluate and Confirm results

▪ Analyze KPIVs ▪ Create action plan for

implementation including change management and communication needs

▪ Buy-in assessment

▪ Quantified relationship between key

input and key output variables ▪ Defined process improvements

including impacts and benefits ▪ Implementation Plan ▪ Process model – ‘Should be’ ▪ Impacted Employees are Trained

Definition of optimal process settings and conditions with specified metrics. Implementation of improvements with a control plan to assess & maintain gains.

▪ Implement improvements ▪ Evaluate results ▪ Integrate & manage

improvements in work processes

▪ Complete closure activities

▪ Document process change ▪ Control plan ▪ Determine new process capability ▪ Leverage opportunities for replication ▪ Communicate results ▪ Financial audit ▪ Hand-off to process owner

1.0 Define

Opportunity

2.0 Measure

performance

3.0 Analyze

Opportunity

4.0 Improve

Performance

5.0 Control

Performance

5

Six Sigma Process Improvement Road Map

1

Migration e-Pro Process ImprovementProject Charter

Project Description Error corrections and clarification of benefits are generatingrework throughout the migration and case installationprocesses, accounting for 20% of the total number of e-Prochange transactions. It is estimated that the volume of errorand rework will grow proportionally as the number ofaccounts migrating by 1/1/2004 increases, driving aproportionate increase in cost and potentially dissatisfyingcustomers.

Start Date April 1, 2003

Completion DateScheduled to be completed by September 5, 2003

Baseline Metrics For 1/1/03 migrated accounts:National Accounts- Average number of change transactions: 14.3, of which

2.9 are due to error and rework- Average hours of rework: 309 hoursRegional Accounts- Average number of change transactions: 8.0, of which

1.6 are due to error and rework- Average hours of rework: 137 hours

Primary Metrics 1. Total e-Pro change transactions2. Percentage of change transactions due to error and

benefits clarification3. Average rework hours per error and benefits clarification

Secondary Metrics none

Goal Reduce error and rework in the migration process by 50%starting with 1/1/04 migrating accounts

Customer Customer migration survey results

Financial CODND (Cost of Doing Nothing Differently)4

th Qtr 2003: $500K

Year 2004: $2.5M

Ben

efits

Internal Productivity Estimated cycle time reduction of 18,868 hours (assuming195 accounts migrating 1/1/04).

Define April 1 – April 21, 2003

Plan Projects & Metrics April 14 – April 18, 2003

Baseline Project April 21 – May 2

Consider Lean Tools May12 – May 16, 2003

MSA May 19 – June 2, 2003

Wisdom of the Org. June 2 – June 6, 2003

Passive Analysis June 9 – June 20, 2003

Proactive Testing June 23 – August 4, 2003

Ph

as

e M

ilesto

nes

Control August 4 – September 5, 2003

SUPPLIER INPUT PROCESS OUTPUT CUSTOMER

Sales

Client / Policy HolderHR BenefitsCoordinator

Client Consultant

Third Party BenefitsVendor

Member

GO Decision

Policy Renewal Date

Summary of Benefits

AdministrativeRequirements

AccountOrganizational

Structure

Detail Benefits

Account DataLoaded in System

Member andDependentEligibilityInformationLoaded in System

Member ID Card

Client / PolicyHolder

Third Party BenefitsVendor

Member andDependent

Providers

Claim

Call

1. Conduct migration analysis

2. Complete account profile

3. Load account structure in system

4. Set up and validate account benefits in system

5. Produce account eligibility record

6. Load account data in product claim engines

0Subgroup 10 20 30 40

0

10

20

30

Ind

ivid

ua

l V

alu

e

Mean=10.98

UCL=26.81

LCL=-4.854

0

10

20

Mo

vin

g R

an

ge

1

R=5.952

UCL=19.45

LCL=0

Total e-Pro Change Transactions by Account from Sep 2002 thru Mar 2003Conduct

Analysis

Create Implementation

Guide

Expert Team

Meeting

Draft

EPRO

Draft

e-

PRO

Release e-PRO

Record

e-

PRO

Impl.

Guid

e

Update

EPRO

OK For

Release

to Vendor

Track

Systems

Loads

IMPLEMENTATION

ERW

From

Eligibilit

y

GO

Decisio

n

SALE

S

Set Up Client ID in

End State

Structure

Request

Codes

STRUCTURE

Complete

Structure

Inspection/Verify

with e-PRO

Get

Underwriting

Approval

Yes

No OK? Yes

No

Go back to

Rates

Structur

e in

CDB

No

Yes

Release

ATC

To Vendor

OK?

Review

Draft e-

PRORequest/Receive

Codes

Enrollmen

t File

CLIENT /CUSTOMERClient

Input

To Sales

Run Legislative

Tool

Check vs. e-

PRO

e-PRO

Redo?

Yes

No

YesOK?

Review

Draft

e-PROCreate

Codes

Legislative Tool

Review

BPC &

Class

Codes

To

Structure

BENEFITS

TS

ID Claim

Scenarios

Load Data into

Downstream

Systems

OK?Yes

No

Data

Engines

Loaded

(e.g ATC,

DocGen,

etc)

Test Scenarios

Check vs. e-

PRO

OK?No

Yes

Fix Claim

Errors

No

No

VOB

Yes

EPRO

Rework

CDB

From

Vendor

Member

cancelle

d

in

Legacy

Reformat

Client

Eligibility DataReview

Draft

EPRO

ELIGIBILITY

Receive Enrollment

Data

Match &

Merge

Load data in

CEO

Are

errors

resolve

d?

Fix Errors

YesNo Cancel

Member in

Legacy

Create

ERW

ERW

Eligibility

In CED

VENDO

R(ID

CARDS)

ID

Card

s

From

Benefit

s

Get

Underwriting

Approval

ERW To

Implementatio

n

ERW To

Implementatio

n

Create Client IDClient

ID

To

Structure,

Benefits,

and

Eligibility

Migration

Structure

Mapping Job

Aid

GO

Decision

To

Structure,

Benefits,

and

Eligibility

e-PRO

Rework

EPRO

Rework?

SMT linking

legacy

structure to

end state

To

Eligibility

CAIP

Processes shaded in green are specific to

migration

Processes out of scope, but critical to

Employer Services

Employer Services functional areas

OUT OF SCOPE

PROCESS STEPS

Production

Migration

Support

Cancel

Legacy

Structure

Elig.

Rework

e-Pro

Rework

Rework Loops highlighted in Red

10

100

50

0

Contracting

T4

-T1

PMHS

Overpayment

Process Data / Materials

PeopleTechnology

Ÿ Auth / Referral Info missing/incomplete/incorrect

Ÿ OI Info missing/incomplete/incorrect

Ÿ Member Eligibility Info missing/incomplete/

incorrect

Ÿ Benefit Info missing/incomplete/incorrect

Ÿ Provider Fee Schedule Info missing/incomplete/

incorrect

Ÿ Provider/Vendor TIN/SSN Info missing/

incomplete/incorrect

Ÿ Additional Information Necessary to Process

Claim

Ÿ Transaction/Codeset data excluded at gateway

Ÿ Standard Operating Procedures (SOPs)

Ÿ Claim Audit Process >$5K

Ÿ Second/Third Party Internal Review

(Medical Management, Claim Benefit

Build)

Ÿ iTrack - drives usage of paper reports to

sort older claims

Ÿ Skill Level of Processor

Ÿ Accessiblity of Site Coach/Training Staff

Ÿ Aggressive Productivity goals conflict with low

quality requirements

Ÿ Rushed Training Schedule

Ÿ Lack of up-training / reinforcement training

Ÿ Best Practice / Skill Training not conducted

Ÿ OJT training on SOP usage

Ÿ System Error During Processing

Ÿ Data Fallout

Ÿ Aurhorization Mis-Match

Ÿ System Restrictions - LPI Manual Calc

Ÿ Data Set Up Issues (eligibility, provider, benefits)

Ÿ Timeliness of Batch Processing

Ÿ Bank Acct Set-Up Delays

Ÿ Customer Touchpoints Delays

Ÿ Inappropriate assignment or missing hold codes

Ÿ Provider Mis-Match

Ÿ Transaction Limitations on data collected at

gateway

Manual Adjudication &

PMHS Provider Selection

- Manual

End

Manually check

provider/ vendor

on claim system

Check provider

data and claim

data against iView

image

Mismatch?Manually try to find

correct data

Found data

Correct data &

verify COB

Service request to

appropriate area

YES

NO

YES

iTrack

Verify in claim

screen and follow

COB Checklist

Attempt to

adjudicate claim

NO

Claim processed

Hold codes that

require further

research

NO

Re-open the claim

YES

Process will

depend on Hold

Code & SOP/ Job

Aid

CIRF

Attempt to resolve

all Hold Codes at a

line level

Resolve service

requests

Adjudicate claim

(manual or

systematic)

End

ID Task Name

1 DEFINE PHASE

12 MEASURE PHASE

13 Plan Project and Metrics

22 Baseline the Project

23 Select KPOV metric to track process output

24 Estimate process capability/performance at the 30,000-foot-level

25 Categorical failures

26 Create pareto chart

27 Rescope project to a large Pareto category

28 Repeat Baseline the Project steps 23 through 27

29 Non categorical failures

30 Revise estimate for COPQ/CODND

31 Project status update w ith executive sponsor

32 Consider Lean Tools

39 Conduct Measurement Systems Analysis (MSA)

40 Ensure data integrity

41 Perform Gauge R&R

42 Improve gauge


44 Wisdom of the Organization

55 ANALYZE PHASE

56 Use visualization of data techniques to gain insight to processes

57 Conduct inferential statistical tests and confidence interval calculations on individual KPOVs

58 Conduct appropriate sample size calculations

59 Conduct hypothesis tests

60 Describe statistical f indings to others using visualization of data techniques

61 Implement agreed-to process improvement findings


63 IMPROVE PHASE

65 d13 d

18 d

18 d

18 d

20 d

20 d

20 d

23 d

23 d

23 d

27 d

76 d33 d

69 d38 d

38 d

40 d

43 d

69 d48 d

61 d48 d

53 d

60 d53 d

60 d53 d

60 d53 d

60 d53 d

53 d

58 d

62 d

63 d

02 09 16 23 30 06 13 20 27 04 11 18 25 01 08 15 22 29 06 13 20 27 03 10 17 24 31 07 14 21 28 05

March April May June July August September Octob

PMHS

# of Audits

7,321

04/05/2003

# $'s

Under 300 4% Under 1,030,680$

Over 676 9% Over 2,303,562$

No $ Error 914 12% No $ Error -$

No Error 5,431 74% No Error -$

7,321 3,334,243$

A)SEVERITY B)OCCURRENCE

Probability

C)DETECTION

Probability

RISK

PRIORITY

NUMBER ACTION TO IMPROVE

Rate 1-10 Rate 1-10 Rate 1-10 RPN

10=Most

Severe

10=Highest

Probability

10=Lowest

Probability AxBxC A B C

Provider Mis-Match 10 8 9 720

Provider Data Incorrect/Incomplete 9 8 9 648

Data Fallout 9 6 10 540

Data Set Up Issues 9 6 10 540

Provider Fee Schedule Unclear 9 6 9 486

OI Information Needed 9 6 6 324

System Restrictions 6 6 9 324

Hold Codes 9 3 10 270

FAILURE MODE

Process Name: PMHS Claim Processing

Date: 6/30/2003 Revision Level: 3

REVISED VALUES

0Subgroup 10 20 30 40

0

10

20

30

In

div

idu

al

Valu

e

Mean=10.98

UCL=26.81

LCL=-4.854

0

10

20

Mo

vin

g R

an

ge

1

R=5.952

UCL=19.45

LCL=0

Total e-Pro Change Transactions by Account from Sep 2002 thru Mar 2003

Gage R&R http://www.aiag.org/ Part Number http://www.qimacros.com/free-lean-six-sigma-tips/aiag-msa-gage-r&r.html

Average & Range Method 1 2 3 4 5 6 7 8 9 10 Sum

Appraiser 1 Trial 1 0.65 1 3.250

Enter your data here-> Trial2 0.6 1

Trial3

Trial4

Trial 5

Total 1.25 2

Average-Appraiser 10.625 1 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Range1 0.05 0 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Appraiser 2 Trial 1 0.55 1.05 3.100

Enter your data here-> Trial2 0.55 0.95

Trial3

Trial4

Trial 5

Total 1.1 2


Range2 0 0.1 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Appraiser Trial 1

Enter your data here-> Trial2

Trial3

Trial4

Trial 5

Total

Average-Appraiser 3#N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Range3 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

EV (Equipment Variation)0.0332 Equipment Variation (EV)

%EV 11.3% 39.9% # Parts #Trials #Ops % of Total Variation (TV)

AV: (Appraiser Variation)0.02066 2 2 2 Appraiser Variation(AV)

%AV 7.0% 24.8% % of Total Variation (TV)

R&R (Gage Capability) 0.0391 Repeatability and Reproducibility (R&R)

%R&R 13.3% 47.0% NDC 11 % of Total Variation (TV)

PV (Part Variation) 0.2917 Part Variation (PV)

%PV 99.1% 350% % of Total Variation (TV)

Skewness0.41Stdev1.670.20Max6.20

QI Macros is loaded onto Excel as an “add on”

QI Macros has an extensive menu system with Statistical Tools, Template and Charts

6

Clicking on any one of the menus will reveal the options

Some of the tools require you to preload data into Excel while others will ask you to enter data by hand

The nomenclature for accessing the QI Macros tools to be used for each exercise

will be MENU > TOOL7

To open the tool to conduct a Variable Gage R&R study we would go to QI Macros > DOE Gage R&R FMEA > Gage R&R

8

Statistical Tools

Lean Six Sigma Templates

Data Transformation

Capability Charts

Control Charts

Improvement Charts

Other Charts

9

10

Current State Process Map

Customer

Business ProcessX

X’s (Inputs)

• There is a process output (Y) that is influenced by multiple process inputs (Xs).

• The inputs (Xs) in the process are manipulated until the variation in output (Y) is minimized and/ or the benefit of the output (Y) is optimized.

Y (output)

Here’s where we are right now – focusing in this area

11

X

X

X

X

.

.

Also known as:◦ The “as-Is” process map

◦ A flow diagram or flow chart

◦ Swim-lane cross functional process map

Purpose is to graphically document the current flow of work through the process to be improved

Useful to people familiar with the process, as well as those that have a need to understand the process

12

Each process step is listed, reviewed and analyzed

Should be done by the team members who are actively participating in the process on a day-in and day-out basis

One of the most critical tools the team uses during the measure phase

Before a process can be improved it must be documented and then measured

13

Some standardized symbols for flow charting or process mapping:

Major Process Step

Wait

Process Start

Decision Point

Re-Work Loop

Major Process Step

Alternate Process

Alternate Process

Re-Work Loop

Major Process Step

Alternate Process

Process Stop

14

15

As a class and facilitated by the instructor, we will spend 20 minutes developing a process map for the first segment of the class scenario◦ Use sticky notes on a white board or flip chart

Process Steps – use notes oriented at 90 degrees

Process Decisions – use note oriented at 45 degrees

◦ The process map should follow the process from the beginning to the end of the project scope

◦ Be sure to scrutinize the map for likely decisions

16

Measure 3 – Process Map Worksheet

• KPOV’s are the key process performance indicators for the process Lead time

Quality – defect rates

Customer Satisfaction scores

Inventory Costs

Inventory Levels/Turns

Right First Time – First Pass Yield

Total Good Output – Rolled Throughput Yield

Delivery to Schedule

Cycle Time

What other metrics have you seen?

17

• Lead Time: The length of time between the initiation and

delivery of a product or service. Example: in manufacturing it would be the time from customer order to delivery.

• Lead time profiles can help to:

document “queue time” versus the “cycle time” content of individual parts of process steps

identify constraining resources (bottlenecks)

Identify non-value added activity (trouble spots) during the process

1818

Some Potential Lean Six Sigma Metrics

• Average Lead time = Work In ProcessCustomer Requirements

• Example: 467 parts Work In Process

102 parts scheduled weekly

= 4.6 weeks

= 22.5 days lead time

19

• Is a function of Lead time

• The longer the lead time the greater the level of inventory

• If the lead time is significantly longer than the process time (VA/NVA) then a proportional part of the inventory can be classed as waste.

• In the Lean world levels of inventory are planned, tested and constantly monitored

• By eliminating the waste, which causes inventory to increase, we can reduce lead times and inventories

20

• Takt is German word for Metronome• In production terms the takt time sets the beat/rate

at which we need to produce parts to meet customer demand

Takt time is: time available

customer requirements

• Takt time has nothing to do with how long a task takes but instead how long you have to do the task

2121

• Time available in printing area: Monday 7AM to Friday 6PM = 108 hours

Time is reduced by

Meal breaks

Shift changeovers

Personnel needs

Meetings

Meeting (1 hour)

Total hours reduced by 25% = 81 hours Convert to minutes 81 x 60 = 4,860 minutes

Divide by Customer Requirements of 91 per week

=1 every 53.4 minutes

22

• A metric that is useful in looking at what goes on inside the process

• Looking at the internal sub processes can help the team target improvement efforts

23

• Overall process has 3 sub-processes• Each sub-process step is calculated as it’s own yield

1,500 Input Units

Sub-process # 1

Rework of 30 Units or .98 Yield

Sub-process # 2


Sub-process # 3


1,411 Output Units

Rework 30 units Rework 44 unitsRework 15 units

1,500 –(89 Units Reworked) = .94 First-Pass Yield1,500 Units Input

When Identifying improvement opportunities: Sub-process # 3 has the lowest yield and possibly the most opportunity for improvement

24

• First pass yield does not account for internal rework in the process – another metric is needed

• RTY - A metric that measures the probability that a process, or service will make it through the process, the first time, without a defect occurring.

• RTY shows the actual yield of a process that has a hidden factory (adds no-value, rework)

• Two methods for calculating:

Method 1 – uses Defects per unit, when we know the total units produced and the number of defects

Method 2 – uses the yield at each opportunity for a defect

25

• Cycle time: The time it takes to successfully complete the

tasks required for a work process.

• Can also be broken down into 2 components of process cycle time:

Work time

Wait time

26

Example: • Process Start: Time a patient enters an emergency

department door – either by foot or ambulance

• Process Stop: Time the patient is officially discharged and leaves the Emergency Department

27

• Defects a part, product, or service that does not conform

to specifications or the customer’s expectations.

• Defects are caused by errors

• Defectives – a unit with one or more defects

• Customer satisfaction is typically measured through a pro-active customer satisfaction survey

28

• Defects per Million Opportunities (DPMO) help to determine the sigma capability at the end of a process, based on the results or output

• Focuses on the capability of the process to meet external customer requirements

DPMO = Defects X 1,000,000

(Units x Opportunity)

29

][

Imagine looking for defects on deck planking. These would constitute 4 opportunities on a single board.◦ Width measurement◦ Length measurement◦ Thickness measurement◦ Presence of knot-holes

Say there were 15 total defects on 55 boards; the DPMO would be:

68,182 = 15 X 1,000,00055 x 4

You can use the file – Distribution DPMO Example in the General Files to calculate DPMO

30

31

Data drives decisions and actions!

Data are measurements or observations we record and use to describe, understand, optimize, or control something such as a process.

32

Attribute

or Discrete

Data

Hours with

Temperature

over 85 Degrees

# of Calls on

Hold Past 30

Seconds

Tank Empty or

Not Empty

Units Exceeding

Target Costs

# of defective items# of errors

33

Advantages:◦ Easy to obtain and calculations are simple

◦ Everyone understands the data

◦ Often readily available

◦ Used to set base line performance

◦ Used by management

Disadvantages:◦ No clue about why a defect happened or how

the process changed

◦ Not accurate at low defect rates (need large sample size)

◦ Cannot predict trends or future events

34

Variable or

Continuous

Data

Hold Time

per Incoming

Call

Average

Temperature

per Hour

Minutes to

Board a Plane

Quantity of

Gas in Tank

Dimensions, Weight, Strength, Time, Cost

35

Advantages:◦ Provides detailed information about the process

using relatively small sample size

◦ Can be used at low defect rates

◦ Can predict trends and future conditions

◦ Key for improvement – DOE, Regression

Disadvantages:◦ Often more difficult to get the data

◦ Analysis is more complex

3636

Attribute/Discrete Variable/Continuous

Pass/Fail Actual Value

Poor Data Definition Excellent Data Definition

Larger Sample Required Smaller Sample Required

Usually Binomial or Poisson Distribution

Often a Normal Distribution

Count Data Continuous Data

37

If you can have ½ of something it is continuous; otherwise it is attribute◦ ½ a minute?

◦ ½ a customer?

◦ ½ of a widget?

◦ ½ second?

Note: Dividing attribute data by other attribute data will give a percentage but the result is still NOT continuous data.

• Number of people in California who drink wine out of the total population

• The resulting percentage is still attribute data

38

1. Number of foam pads with tears Variable Data ? Attribute Data?

2. The number of tears in each foam pad

Variable Data ? Attribute Data?

3. Delivery time in days for each shipment


4. The number of seats that fail inspection


5. The number of times a shipment is late


6. Weld button diameter in inches Variable Data ? Attribute Data?

7. The length of time spent in the Emergency Department

Variable Data? Attribute Data?

8. The number of bad welds Variable Data ? Attribute Data?

39

Why Sample? ◦ The entire population of a dataset can rarely be

measured

◦ A sample is used to infer the characteristics of the population

Total Population

Sample

Generate Statistics

Inferences about the characteristics of the

population

40

A good sampling plan:◦ Is representative of the population

◦ Includes the variability of the population

◦ Is of a large enough size to be able to estimate the parameters to desired accuracy

Sampling Plan Considerations:◦ Goal of the sampling plan

◦ Levels of sampling

◦ Type of variation present

◦ Level of Risk for customer and manufacturer or organization

41

Questions to consider before sampling:◦ What is the goal of the data collection?

◦ What is the population to be sampled?

◦ How many samples should be measured from each group, shift or run?

◦ How will the random samples be chosen?

◦ How many measurements should be taken?

◦ What is the timeframe?

42

To avoid Bias◦ Utilize systematic sampling

good for business processes

take data samples at certain time intervals

take data sample from consistent transaction

◦ Don’t forget process variation when sampling Different work periods

Different experience levels with the same role

Previous experience with other organizations vs.. seasoned organization associates

Newly graduated vs.. career associates

43

Random Sampling:◦ Sampling without randomness ruins the

effectiveness of any plan

◦ Random sampling gives every part or process an equal chance of being selected for the sample

◦ The sample should be representative of the lot

◦ The sample can be determined by selecting a sample based on a random number table

44

Sequential Sampling:◦ Used for costly destructive sampling

◦ A sample size of one

Stratified Sampling:◦ Used when the group may not be homogenous

(machined with different machines or on different lines)

◦ Select random samples from each group

◦ Observations are based on relevant group not the overall system

45

When sampling a process:◦ Collect smaller samples more frequently

◦ Can take action and track changes through a control chart

Sampling approach:◦ Random sampling

◦ Stratified random sampling

◦ Systematic sampling (every Kth sample)

46

When sampling a large population:◦ Utilize statistics in our sampling

◦ Estimated with a certain degree of confidence typically 95%

47

48

1 An improvement team is interested in studying "first Call Resolution".

Approximately 15% of the callers are sampled to respond to a customer

survey. Only 10% of those sampled were willing to respond.

2 An improvement team is interested in improving billing accuracy. They have

decided to collect a sample of bills processed from 4 to 5 PM every day for

the next 4 days

3 An improvement team is studying the total cycle time for large deals. To

develop an understanding of the current cycle time, the team sampled deals

from the Atlanta Regional Office, who had data readily available.

4 An improvement team is interested in improving billing accuracy. They

decided to sample and pull every 20th bill processed over the next 30 days

5 An improvement team is interested in studying the likelihood that customers

will purchase new service. Approximately 10% of all 7,000 customers are

randomly selected to survey; approximately 80% of those sampled

responded to the survey

1. Define clear, concise data collection goals

2. Decide on what you are trying to measure and the procedures you will use to collect the data

3. Attempt to get an accurate picture of what’s is happening in the process over time

4. Begin collecting data, making sure data is consistent and stable in the way it is collected

5. Continue improving measurement stability & consistency

49

50

Step Action Airline Queue Example

1

Define clear & concise data collection

goals

• desired outcomes

• what should be measured

• customer comments

• why are you collecting data?

• how will the data help you

• what will you do with data

US Air decided to find out how to reduce the time that people had to

wait in lines at the airport:

• average wait time

• when are times particularly bad

• factors contributing to long waits

• special causes associated with long wait

2

Decide on what you are trying to

measure & the procedures you will

use to collect the data

• operational definition

• types of data

• inputs (what do you look at)

• process (efficiency factors)

• output (key quality aspects)

• sampling

• critical measurement points

• timelines

Developed operational definition:

• time people wait - measured in minutes & seconds

• cycle - when a person steps in line to the service representative

• data collection form - stratify time of day; day of week; no. of

representatives in service, no. of people in line.

• use digital stop-watch

• start watch when person enters line, then stop the watch as soon as

the service representative begins to speak

• sample one person every 10 minutes, each day, over a three week

period

• sample at a different time very day

51


3

Attempt to get an accurate

picture of what is happening

over time (consistency &

stability over time)

• measurement plan

• who will collect

• collection form

• agreement on terms

• collecting & recording data

Measure the same thing in the same way throughout the data collection

process:

• looked for data collection stability by determining what factors might

cause measurements to vary

• observers must start the timer the same way every time

• observers must count the number of people in line the same way - do

not count the person at the service counter

• observers must count the number of service representatives the same

way - just those who are assisting customers and not supervisors or

managers

• everyone must agree on what to put down on the collection form by

giving definition to terms

4

Begin collecting data (make

sure data is consistent and

stable in the way it is collected

• check for understanding

• check for agreement

• assign responsibility

Train & practice

• trained all observers on how to use the stop-watch & fill out forms by

having them practice at the airport

• reviewed observer gathering data to make sure the data was collected

accurately

• all observers met to discuss procedures and recommend modifications

for a smooth approach

52


5

Continue improving

measurement stability &

consistency (document any

special issues associated with

study)

• descriptive statistics

• special causes

• common causes

Complete data collection effort:

• calculated the average wait time

• correlated wait time with time of day

• looked at times of day over the course of the week

• data showed that the longest wait times occurred on Sundays between

1pm and 6pm, and during the week on Mondays & Fridays from 7:30am to

10am

• longer wait lines occurred when airlines & travel agencies had special

promotions

QI Macros > Calculators > Sample Size Calculator > Sample Size Calculator Tab

53

Set the a and b levels• How confident you

are that the true mean is between

the Interval

Set the interval (1/2 of the interval you are willing to accept e.g.

your specification range)

• If your spec range is 0.9 to 1.1 then

your interval would be 0.1)

54

Enter the population if known

If you are using attribute data, enter the current defect

rate (approximate if needed)

Enter the standard deviation for the

process

55

Currently estimating that you are late 35% of the time. You are willing to be +/- 3% (0.03). Using the typical a and b levels (0.05 and 0.10 respectively), how many samples would you need to accurately assess the actual rate?

How about if you estimated that you were late 10% of the time?

Try on your own

56

Estimating a 35% rate would require 971 samples to verify…

Estimating a 10% rate would require 384 samples to verify…

57

58

We are at the mercy of chance!

We can’t control it

then

We don’t know enough about it

then

If we can’t accurately measure something

then

59

USABLE INFORMATION

that statistics converts to…

DATA

We measure to get

60

A lot of data is available but not necessarily used

Measure and gather data effectively which provides the improvement team with specific valuable information

Remember to gather data which reflects what your process or product looks like to your customers

6161

Think through what you are trying to learn about the process inputs (X’s or causes) and decide what specifically needs to be measured

There are many inputs (X’s) that can be measured in a process - remember to measure against customer requirements –this will help to keep the team focused

62

Measuring allows the improvement team to see how input variables (X’s or causes) upstream in the process can affect the outputs (Y) of the process under investigation.◦ as an example: the time of day in which a

process happens could impact the overall cycle time a customer experiences when completing the process under investigation

63

64

Example & Question:◦ The improvement team needs to gather data on

a process input (x) of the time a patient arrives in the hospital emergency department

◦ This time has been defined by the team as time the patient walks through the doors of the ED or arrives by ambulance

◦ The existing data available is when the patient checks in at the emergency department registration or triage desk

◦ Will this data work?

65

Example & Question:◦ The improvement team has decided to gather

data on a process input (X) of the time a customer spends on the phone with customer service resolving an issue

◦ This time has been defined as the time the customer reaches the first person they speak with to the time the issue is resolved

◦ The existing data available is time stamped with each customer service rep the customer speaks with but does not account for the time spent in-between on hold or transferring

◦ Will this data work?

66

Why won’t the previous 2 examples work?

What do you do?◦ COLLECT REAL TIME DATA THAT REPRESENTS

THE SPECIFIC PARAMETERS

However, use the historical data to provide additional segmentation and trend information

6767

Uncovering what specific data the improvement team needs to gather

Review existing data to ensure the data exactly matches what is needed

Also make sure the existing data is representative of the sample and is of the stratification the improvement team has decided is within the scope of the project

6868

Historical Data◦ Often contains data inaccuracies

◦ Records are often incomplete often specific information on some variables might be

missing

attention was placed on variables that were not vital

some important variables might be missing

◦ Process variables might be strongly correlated but not understood

always validate historical data with real time observations

69

70

Measurement is critical to understanding the process

Determining WHAT to measure is often the most difficult part

Process measurements collected during the Measure Phase are to understand and baseline the current process

71

1. Observe the process

2. Select what to measure

3. Measure for a reason

4. Have a process for measurement

5. Plan and measure performance

72

1. Observe the Process◦ Observe first, then measure

◦ Watch the process step by step

◦ Validate the current state value stream process map

◦ Begin to identify failure modes

73

2. Select what to Measure◦ Understand the measurement type

Attribute inspection a special attribute is inspected resulting in a pass or fail

Defect inspection inspection for defect levels

classified by critical, major or minor defects

Variable measurement a specific parameter is measured at single or multiple

measurement sites

74

3. Measure for a Reason Ask yourself: Why is this particular part of the process

being measured? How does this measurement relate to the customer CTQ’s

There are two reasons to collect data:

◦ Measure efficiency and / or effectiveness

Efficiency measures how well your internal process are running (defect rates, process capability, etc.)

Effectiveness measures how your product of process looks to your customer (CTQ outputs)

◦ Discover how variables (X’s) upstream in the process affect the outputs (Y’s) delivered to the customer

The number of defects affect the cycle time due to rework processes

75

4. Have a process for measurement◦ Review your CTQ tree and SIPOC for KPOV’s and

KPIV’s

◦ Select the best measurements for each CTQ

◦ Develop Operational Definitions

What the measurement is

What the measurement isn’t

What is outside of the measurement scope

The basic definition of the measurement

Detail on how to take the measurement

76

Operational Definition

Elements Examples

What are you

trying to

measure?

Basic definition

or the measure

What the

measurement

isn't

How to take the

measument (in

detail)

➢ Satisfaction of customers in the Northeast

region with telephone support services

➢ Number of surface defects on the rear panel

➢ On-time delivery for Product X

➢ Are "customer comments" included under

"complaints"?

➢ Does "surface defects" include smears or only

scratches and dents?

➢ Satisfaction = X% of customers giving us a

score or 80 or above

➢ Surface defect = any dent or scratch visible from

a distance of 3 feet under normal light

➢ "Start the stopwatch when the customer steps

into the line, and stop it whenthe customer

leaves the front desk."

➢ "Use the standard calipers placed at the X-

junction to measure width in centimeters."

Definition – a clear, understandable description of what is to be observed and measured, so that different people taking or interpreting the data will be consistent

Factor in…Different terminologyAbility to focusSituations which might occurEvent which can be interpreted in several different ways

77

Measure 9 - Operational Definition Instructions

5. Plan and Measure◦ Creating the data collection plan

◦ Stratification factors

◦ Data collection plans

Hints for data collection

78

Determine:◦ What will be measured?◦ Who will collect the data?◦ What are the units of measurement?◦ Stratification factors (i.e. KPIVs)?◦ Over what timeframe will the data be collected

(sampling frequency)?◦ What number of samples will be collected at

each point in time (sample size)? ◦ Does the data already exist, or will it need to

be collected?

79

Measure 10 - Define Measure Data Collection Process

Key Process Input Variable (KPIV) information collected as part of the VOP data collection.

For example:

◦ KPOV → time for inspection

◦ Stratification factors (KPIVs that affect this KPOV):

Type of part

Severity of defect

Experience level of inspector

Time of Data

Others??

80

Data Stratification

FactorsExamples (Slice the

data by...)

Who

When

What

Where

➢Department

➢ Individual

➢Customer type

➢Region

➢ Plant

➢ Type of complaint

➢Defect category

➢Reason for incoming call

➢Month

➢Quarter

➢Day of week

➢ Time of day (hour)

➢Region

➢City

➢ Specific location on product (e.g. top

right corner)

You want to be able to “slice and dice” the data from the CTQ tree so that you can identify where there might be differences (good and bad)

You must gather the stratification information at the same time as the data – otherwise, you won’t be able to link the two

81

Measure 11 - Data Stratification Worksheet

▪ Example Data Collection Plan:

Data Collection Plan

Metric Name KPOV/KPIV

Who will

collect

data ?

Units of

measure

Stratification

factors

Sample

Freq

Sample

Size

Source

/locatio

n

Collection

method

Out of

bounds

How will

data be

used?

How will

data be

displayed

CMM inspection

time KPOV Jen Minutes Part number xx 50/month

CMM

reports/I

nfinity First piece

CAT

manifolds

only

correlation

analysis,

capability

analysis

Control

chart,

scatter plot

Read CMM report KPIV Bob Minutes Inspector

Deviation look up KPIV Dan Minutes Inspector

Notification time KPIV Bill Minutes

Supervisor,

opertor time

82

Data should be collected in it’s “rawest” form – no summary data.

Keep data sheets for future reference

Put data into Excel to facilitate Statistical Analysis

Sometimes data will be collected that is not immediately used…that’s O.K., keep it for future reference.

83

▪ Example Checksheet (with “Whys”):

84

Complete? Accurate? Timely?

▪ Example Process Observation Worksheet:

85

Measure 1 – Process Observation Worksheet

86

Measure 12 - Checksheet

H

W

S

HHH

Types of Damage

H = Hole

S = Smear

W = Wrinkle

This shipping box has several types of damage which can occur during packing and shipping. The relative location of each type of damage is shown on one diagram which represents many boxes. Note the large number of holes in the one location – this would lead the team to believe that some systematic cause is poking holes in the boxes and needs to be investigated.

87

Measure 13 - Concentration Diagram

Transactional Manufacturing Done?Planned

Date

Actual

Date

Planned

Duration

Actual

DurationNotes

Start Date Start Date X 14-Jun 15-Jun n/a n/a

Information Collected

from CustomerOrder Received X 15-Jun 19-Jun 1 days 4 days

Data from

customer

Initial Draft PreparedOrder/Press Set

UpX 17-Jun 18-Jun 2 days 1 day

Review with

CustomerOrder Run X 21-Jun 23-Jun 4 days 5 days

Final Version

PreparedOrder Packaged 22-Jun 1 days

Final Report

SubmittedOrder Shipped 23-Jun 1 days

Completion DataStep

Shows where you are in the process

Tracks data on time taken to complete

steps – lets you track differences

Notes are data which

help explain differences

88

Measure 14 - Confirmation Checksheet

Working as a team, spend 5 minutes…

Based on your understanding, what data do you need to collect on the class process; Use the Measure 6 – Data Collection Sheet to determine the data to be collected

Homework - Determine which check sheet(s) you might need to collect the data (Measure 7 – Data Collection Checksheets or other)

89

Measure 6 – Data Collection Sheet

Measure 7 – Data Collection Checksheets

90

Making sure your measurements are accurate◦ Variable and Attribute data – Gage R&R

Repeatability – can a single operator recreate his/her measurement on each part accurately?

Reproducibility – can two or more operators recreate the measurement on each part accurately?

Analyzing your data graphically and statistically◦ Plots◦ Capability◦ Etc.

91

GRR is a test of your measurement system◦ System includes:

Defect Definition

Measurers

Process

Documentation Tool

GRR becomes even more critical with transactional processes as you have many different people collecting the same data in different areas

92

GAGE R&R Instructions

1. Select 10 DIFFERENT parts and at least two "appraisers" and one gage

2. RANDOMLY, have appraisers measure each part at least twice

3. Then enter the results of each measurement in Cells C3:L7, C11:L15, C19:L23

4. The template will automatically calculate all of the values based on your data

5. Evaluate your measurement system based on %R&R and make adjustments as required.

Gage Repeatability and Reproducibility

Measurement is one common cause of variation. Gage R&R helps improve measurement systems

If repeatability (EV - Can the same person using the same gauge measure the same thing consistently)

is larger than reproducibility, reasons include:

1. Gage instrument needs maintenance

2. Gage needs to be redesigned

3. Clamping or location needs to be improved

4. Excessive within-part variation

If reproducibility (AV-can two appraisers measure the same thing and get the same result)

is larger than repeatability, reasons include:

1. Operator needs to be better trained in how to use and read gage

2. Calibrations on gage are not clear

3. Fixture required to help operator use gage more consistently

Gage System Acceptability

% R&R<10% Gage System Okay (Most variation caused by parts, not people or equipment)

% R&R<30% May be acceptable based on importance of application and cost of gage or repair

% R&R>30% Gage system needs improvement

(People and equipment cause over 1/3 of variation)

93

Measure 15 - Gage R&R Instructions

Scenario - One of the reasons that your boss needs you there on time is that you are the only one who seems to get good data with a variable gage on the shop floor. Over the past few weeks he has had you and another employee run 10 samples two times each to see how well that gage works.

Open the file Gage R&R and follow along with your instructor

94

Gage R&R

Gage R&R http://www.aiag.org/ Part Number http://www.qimacros.com/free-lean-six-sigma-tips/aiag-msa-gage-r&r.html

Average & Range Method 1 2 3 4 5 6 7 8 9 10 Sum

Appraiser 1 Trial 1 0.65 1 3.250

Enter your data here-> Trial2 0.6 1

Trial3

Trial4

Trial 5

Total 1.25 2


Range1 0.05 0 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Appraiser 2 Trial 1 0.55 1.05 3.100

Enter your data here-> Trial2 0.55 0.95

Trial3

Trial4

Trial 5

Total 1.1 2


Range2 0 0.1 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Appraiser Trial 1

Enter your data here-> Trial2

Trial3

Trial4

Trial 5

Total

Average-Appraiser 3#N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Range3 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A









95

After opening the file, go to DOE Gage R&R FMEA > Gage

R&R

Enter the data from the file into the proper cells of the Variable Gage R&R template (you can cut and paste it)◦ Be sure to paste the values!

The part numbers are in row 2

96

The trials for each appraiser on in column B

%EV – Repeatability Red (Not good)%AV – Reproducibility Yellow (Borderline)%R&R Yellow (Borderline)NDC – Number of Distinct Categories Yellow (Borderline)

97

Range Average 0.0340 Constants

XDiff 0.0240 10 Trials9Trials 8 Trials 7 Trials 6 Trials 5 Trials 4 Trials 3 Trials 2 Trials # Trials 2

UCL 0.1112 1.777 1.816 1.864 1.924 2.004 2.11 2.28 2.58 3.27 D4 3.27

LCL 0.0000 0.223 0.184 0.136 0.076 0 0 0 0 0 D3 0

Repeatability(EV) 0.0301 0.308 0.337 0.373 0.419 0.483 0.577 0.729 1.023 1.88 A2 1.88

Reproducibility(AV) 0.0156 0.3249 0.3367 0.3512 0.3698 0.3946 0.4299 0.4857 0.5908 0.8862 K1 0.886226

Gage Capability(R&R) 0.0339 0.7071 0.5231 K2 0.7071

Spec Tolerance 0.7 2 Ops 3 Operators

3.0775 2.97 2.8472 2.7044 2.5344 2.3259 2.0588 1.6926 1.1284 d2

% Using % Using Gage system may be acceptable based on importance of application and cost

AIAG - Automotive Industry Action Group FormulasTV Tolerance Gage may need maintenance, redesign, or better clamping









TV (Total Variation) 0.1532 Total Variation (TV)

19.7%10.2%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

%EV %AV

Components of Variation

98

• A good gage will have an %R&R < 10%; a bad gage should be > 30%

• The Number of Distinct Categories should be >6• You want most of the variation contribution to be made up by

the parts

Range Average 0.0340 Constants

XDiff 0.0240 10 Trials9Trials 8 Trials 7 Trials 6 Trials 5 Trials 4 Trials 3 Trials 2 Trials # Trials 2

UCL 0.1112 1.777 1.816 1.864 1.924 2.004 2.11 2.28 2.58 3.27 D4 3.27

LCL 0.0000 0.223 0.184 0.136 0.076 0 0 0 0 0 D3 0

Repeatability(EV) 0.0301 0.308 0.337 0.373 0.419 0.483 0.577 0.729 1.023 1.88 A2 1.88




3.0775 2.97 2.8472 2.7044 2.5344 2.3259 2.0588 1.6926 1.1284 d2












19.7%10.2%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

%EV %AV


99

Change the value in cell I4 to match I3 and see how it changes the analysis…

Small changes make a difference….




3.0775 2.97 2.8472 2.7044 2.5344 2.3259 2.0588 1.6926 1.1284 d2












17.0%7.6%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

%EV %AV


The tolerance is the span of your customers specification range.

The tolerance dictates the resolution of your gage.◦ If you gage does not allow you to see the difference

between two different values, you will have difficulty accurately measuring the product or service.

100

101

The first value is the contribution of that element to the total variation in the gage

The second value is how much of the part tolerance is taken up by that element• The wider the tolerance, the more useful

the gage will be in that application

Your biggest concern is getting the % of Total Variation down

You must designate a “master” for the MSA; this should be a person who is regarded as the most reliable inspector (this could even be the customer)

MSA Prep◦ Select at least 30 parts from your process with an approximate mix of 50/50 split on

good and bad items; don’t make them obvious one way or the other (use just 10 for this experiment)

◦ Select current inspectors and/or back ups◦ Review the attribute(s) in question with the inspectors

MSA Inspection◦ Label or locate each item so that they can be identified◦ Have each inspector look at the parts ONCE in random order to determine pass and

fail WITHOUT the other inspectors watching (enter the data in the Attribute R&R matrix as they are inspected)

◦ After each inspector has completed the first run, start on a second run with each inspector

Use the tab Attribute GageR&R on the Gage R&R Excel Sheet to enter your data (from the next page); be sure to enter the “experts” column

102

You are also the head inspector for the product that your company produces. Lately there have been complaints about the quality of the product from customers. You need to have 3 inspectors look at the candy to see if they can agree.

Use the data on the next slide to enter into the Attribute MSA in QI Macro; do this exercise on your own

The file is in the Attribute Gage Worksheet tab in the Gage R&R menu you just opened

103

Appraisal ResultsThe Masters

Appraisal

Part Number

104

Appraiser A Appraiser B Appraiser C

Part # Trial 1 Trial 2 Trial 3 Trial 1 Trial 2 Trial 3 Trial 1 Trial 2 Trial 3 Reference

1 1 1 1 1 1 1 1 1 1 1

2 1 1 1 1 1 1 1 1 1 1

3 1 1 1 1 1 1 1 1 1 1

4 1 1 1 0 1 1 1 1 1 1

5 1 1 1 1 1 0 1 1 1 1

6 1 1 1 1 1 1 1 1 1 1

7 1 1 1 1 1 1 1 1 1 1

8 1 1 0 1 1 0 1 1 0 1

9 1 1 1 1 1 1 1 1 1 1

10 1 1 1 1 1 1 1 1 1 1

Ultimate Score

Score of the gage “system” by itself; helps to determine

if the Appraisers need to confer with the “expert”

105

%

Appraiser

% Score

vs

Attribute

A B C A B C

Total 10 10 10 10 10 10

# Match 9 7 9 9 7 9

Mixed 1 3 1

95% UCI 100% 93% 100% 100% 93% 100%

Score 90% 70% 90% 90% 70% 90%

95% LCI 55% 35% 55% 55% 35% 55%

System %

Effective

Score

System %

Effective

Score vs

Ref

Total 10 10

# Match 8 7

95% UCI 97% 93%

Score 80% 70%

95% LCI 44% 35%

Typically, you want a System % Effective Score vs. Reference to be > 80% although through consistent gage studies, visual aids and training, it can go

higher

Appraiser “B” needs to be retrained on the process,

expectations and the visual aids

Statistical analysis is used to:◦ Describe our products and processes

◦ Draw conclusions and predict results

When applying statistics:◦ Measure the right thing (KPIVs and KPOVs)

◦ Make sure the data is “Good” (your R&R)

◦ Be confident the conclusions are correct (have enough data)

◦ Be efficient

106

Descriptive versus Inferential Statistics◦ Descriptive – describes the feature of a set of

any set of data (average, skew, etc.)

◦ Inferential – analysis of a sample to learn about a population

Data Sets can be characterized by:◦ Shape of distribution

Skew

◦ Central Tendency Mean (average), Median, Mode

◦ Variation Range, Standard Deviation, Variance

107

You can:1. Graph it

2. Calculate it Location – average, median, etc.

Dispersion – standard deviation, range

3. Predict Normality, z score

Non normal data

108

Histograms and Dotplots◦ Shows a snapshot in time

Run Chart◦ Shows a snapshot over time period

Control Chart◦ Is my process in control?

Pareto Chart◦ The vital few

Box and Whisker (Boxplots)

109

A normal (bell curved) distribution is 6 standard

deviations wide! Most of the data is in the middle (+/-

1 standard deviation).

s s s sss

68%

13.5%13.5%2.5%2.5%

Upper Customer Specification Limits

(USL)

Process Performance at a Snapshot in Time

Lower Customer Specification Limits

(LSL)

111

CenterProcess

ReduceSpread

Off-Target Too Much Variation

Centered On-Target

112

The more potential space between the tails of the curve and the specification limits – the higher the sigma level!

Understand the customer requirements◦ Specification limits

Reduce process variation◦ Narrower bell curve

Perform the process consistently time after time

◦ Centered Process between the customer expectations

113

Represent different ways of characterizing the central value of a collection of data

Six Sigma will utilize three measures:◦ Mean

◦ Median

◦ Mode

114

Mean calculation:◦ the average

◦ the sum of the data points divided by the total number of data points

◦ What could be wrong if you only used this measure?

Example: Salaries in the room

How do outliers affect our mean?

115

Median calculation:◦ the middle of the data set

◦ numbers have to be ordered

◦ if the number of values is odd, the median is the center number

example: 4, 6, 6, 7, 8, 9, 10

◦ if the number of values is even, the median is the average of the two center numbers

example: 4, 6, 6, 7 (7.5) 8, 9, 10, 10

116

Mode calculation:◦ the most frequently occurring number within

the data setexample: 4, 6, 6, 7, 8, 9, 10

◦ it is possible for a group of data to have more than one mode

example: 4, 6, 6, 7 , 8, 9, 10, 10

117117

Represents the spread of a distribution

Six Sigma will utilize four measures:◦ Range

◦ Standard Deviation

◦ Variance (standard deviation squared)

◦ Coefficient of Variation (COV)

Standard deviation / mean (or average)

Used to compare the variation in different processes

118

Range calculation:◦ easy way to measure variation

◦ the difference between the largest and the smallest value in a data set

◦ tells us how wide the data set isexample: 4, 6, 6, 7, 8, 9, 10

10 – 4 = 6

range = 6

119

Variance calculation:◦ is a measure of spread, just like range

◦ is equal to standard deviation squared

• The formula for the variance is:

s = (X - u) / N2

120

Where X = the individual data point; u = the overall average; N = the number of samples

2

Standard Deviation:◦ The square root of the variance

◦ The square root of the average deviations of all data values from the mean divided by the number of samples

Why Use Standard Deviation?◦ Unlike the range, the standard deviation takes into

account all the data values in the sample

◦ Unlike the variance, the standard deviation has the same units of measurement as the original data

121121

122

Population Mean

u = Σ ( Xi ) / N i = 1 to N

Population Standard Deviation

O= Σ ( Xi - u ) / N i = 1 to N

-

Sample Mean

X = Σ ( Xi ) / n i = 1 to N

Sample Standard Deviation

O = Σ( Xi - X ) / N - 1 i = 1 to N

-

_

_

Standard Deviation: the square root of the variance

N is used for a population, and N-1 for a sample (to remove potential bias in relatively small samples - less than 30)

Standard Deviation

2 2

A bar graph in which data are grouped into classes.

The height of each bar shows how many data values fall into each class

Shows how well a process is operating

Look at:◦ Is it centered correctly

◦ Wide or narrow dispersion

◦ Symmetrical, lopsided, cliff-like, twin-peaks, flat

◦ Irregularities

123

Enter the data on the right into Excel and run:◦ Be sure to highlight the data

Histograms and Capability > Histograms with Cpand Cpk◦ Subgroup size = 1

◦ Specification Limits

10 for the upper; 0 for the lower (we will cover this later)

◦ Choose 7 bars◦ Note: Rule of thumb depending on the # of data points

if under 50 Need 5 to 7 classes (bars)

50 to 100 Need 6 to 10 classes (bars)

100 to 250 Need 7 to 12 classes (bars)

over 250 Need 10 to 20 classes (bars)

◦ Click OK for the default titles

3.3

1.9

6.2

4.6

3.9

3.5

2.7

1.6

3.2

0.8

0.2

5

1.9

2.2

1.1124

Note: The number of bars is not exact; QI Macros may adjust the number

125

Any Observations?Note: Changing the bars will change

the shape!

126

UCLLCL

Type: symmetric bell-shaped

Behavior: if chart shows no special causes, data may come from a stable process

Action: determine when common causes will occur & control

127

UCL

LCL

Type: skewed

Behavior: large pile up of observations around a maximum reading

Can be skew to one side or the other depending on the

desired target of the process

128

UCLLCL

Type: bi-modal: two humps with special causes

Behavior: data may come from a mixture of two processesAction: investigate before further analysis

129

UCL

LCL

Type: concentration outside of UCL - special cause ?

Behavior: could be different operators / one re-measuring dataAction: standardize process for recording & repeating readings

Cp= Process Capability. A simple and straightforward indicator of process capabilityCpk= Process Capability Index. Adjustment of Cp

for the effect of non-centered distribution◦ Cpk is a measure of both the spread and centeredness of

a process

◦ Cpk looks at the 6 sigma limit of the tail of the curve farthest from the specification midpoint (e.g. tail closest to a specification limit)

◦ The larger the Cpk the better for continuous improvement (look for values of > 1.33)

130

You pull into a your garage and get out of the car and view your parking within the parameters of the space

Cp will look at where you parked in relation to the outside walls and how much room you have before hitting the wall

Cpk will look at how consistently you park in the same spot over and over again

131

Capability – You decide to run capability studies on how long it takes you to get ready to do several stages to see how capable you are at meeting your targets:◦ Stage 1 - Alarm to going downstairs for breakfast (20-

25 minutes)◦ Stage 2 - Fixing breakfast to out-the-door (15-20

minutes)◦ Stage 3 - Out-the-door to arrival at work (30-40

minutes)

Open the file Stage Times and follow along with the instructor as we run a capability study on the First Stage (see the next slide for the steps)

132

Stage Times

1. Highlight the data for the First Stage only

2. Select Histograms & Capability > Histogram with Cp Cpk◦ Choose the subgroup size (chose 1 for this

exercise)

3. Enter the upper specification limit (25)

4. Enter the lower specification limit (20)

5. Select OK on the number of bars and the titles

133

What do you see?

134

0

2

4

6

8

10

12

14

16

18

20

19.66 20 20.34 20.68 21.02 21.36 21.7 22.04 22.38 22.72 23.06 23.4 23.74 24.08 24.42 24.76 25.1 25.44

Nu

mb

er

Values

HistogramLSL 20.000 USL 25.000

Mean 23.044Median 23.047Mode 23.044n 50 Cp 1.836

Cpk 1.437CpU 1.437CpL 2.236Cpm 1.165Cr 0.545ZTarget/DZ1.170Pp 1.793Ppk 1.403PpU 1.403PpL 2.183Skewness 0.172Stdev 0.465Min 22.020Max 24.409Range 2.389Z Bench 4.310% Defects 0.0%PPM 0.000Exp PPM ST8.145Exp PPM LT12.775Sigma 5.810

0 1 2 3 4 5 6 7 8 9 10 11

You might gather data over time by taking multiple samples at regular intervals. This would result in “rational subgroups”.

Subgroups are important to the analysis of capabilities.

Long term

Short Term

135

GB+

12/10/2013

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LSL USL

Remember – a single bell curve spans 6 standard deviations. At a Cp of 1.0,

exactly one bell curve would fit within the Upper and Lower Specification Limits

(this would be a 3 sigma process). A Cp of 2.0 would signify that two bell curves

would fit within the specification limits (this would be a 6 sigma process)

A 1.0 Cp / Pp process has no wiggle room for error or

you will be out of specification

12/10/2013

P

g

1

3

7

Which ever value (Cpl or Cpu) is smaller will determine the Cpk for the

process. The lower the Cpk, the more likely the process will be out of spec.

Cpk = minimum value of Cpl or Cpu• Cpl = (average – LSL) / (3 x short term standard

deviation)• Cpu = (USL – average) / (3 x short term standard

deviation)

The calculations for Ppk is identical except you use the long term standard deviation

The “C” values are for short term data; the “P” values are for long term data◦ Process Capability (Cp) is based on short-term

sigma (standard deviation within the subgroup)

◦ Process Performance (Pp) is based on long-term sigma (standard deviation between the subgroups)

The larger the subgroups, the smaller the Cpvalue since the standard deviation rises with the addition of more sample◦ As the subgroup size approaches the total sample

population, Cp approaches Pp

138

Cp/Cpk can be used to predict the “entitlement” of the process.◦ In other words – how good can the process operate

in the ideal world with no outside influence

Pp/Ppk is a more accurate reflection of the long term of the process over time◦ Long term is defined as a situation where 80+% of

the variables have fluctuated across their normal range

139

Homework – Run the 2nd and 3rd stage capabilities and save the resulting analysis; what are your observations? Run different subgroup sizes to see how Cpk changes

140

141

Formulated by Joseph M. Juran

Juran used the 80-20 Principle to focus on the most frequent and serious causes of poor quality

A graphical method of identifying causes of issues

Graphically represents the largest areas of defects

142

Demonstrates the 80-20 Principle, a scientific rule proven in business and economics◦ for many events, 80 percent of the effects come

from 20 percent of the causes

Utilizes discrete data (categories, groupings) ◦ example: complaint categories

143

Open the file Pareto

Highlight columns A and B

Run Pareto Chart in QI Macros

Click OK on the pop up menus

Afterward, run a Pareto on column E (reasons for shipments not being sent)

144

Pareto

34

28

1612

10

34.0%

62.0%

78.0%

90.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0

10

20

30

40

50

60

70

80

90

100

Problem Not

Resolved

Hold Time Problem Not

Resolved to

Satisfaction

Passed to Operator

>2 Times

Unfriendly

Customer Rep

Pare

to C

hart

Categories

Pareto Chart

145

Biggest Hitter

146

Can be used in the Charter to show the state of the current process (KPOV’s)

Also called a time series plot

A trend line can be added to a run chart

Graphical method showing current process performance over time

Utilizes continuous data and requires time/date labels

A run chart shows:◦ Variation over a period of time versus a Histogram which

is a snapshot in time

◦ The data points in the order in which they occur

◦ Shows the variation in the process result over time

147

Pro

cess

Mea

sure

men

t

Time Period

Median or

average

148

QI Macros > Run Charts > Average Use the file Run Chart

Highlight columns A & B

QI Macros > Run Charts > Average

Click “OK” for all titles

Note – this is data for the drive time in the class scenerio

149

Run Chart

150

33.41

33.91

34.41

34.91

35.41

35.91

36.41

36.91

37.41

37.91

38.41

Tim

e t

o W

ork

Date

Time to Work

Time to Work

Average

To add data labels, click directly on the lines formed by the data

Right click and select “Add Data Labels” and data

values will appear

To add a trendline, click directly on the lines formed by the data

Right click and select “Add Trendline…”

Select the type of trendline

151

152

34.4

37.7

35.6

34.3

34.9 34.9

33.8

34.6

37.5

34.5

34.2

36.5

35.6

34.9

34.3

34

34.3

34.7

35.435.6

33.41

33.91

34.41

34.91

35.41

35.91

36.41

36.91

37.41

37.91

38.41

Tim

e t

o W

ork

Date

Time to Work

Time to Work

Average

Linear (Time to Work)

153

Can be used in the Charter to show the state of the current process (KPOV’s)

Another graphical method of showing current process performance

◦ Control Charts can be used in the Define/Measure phases as well as the Control phase

Shows how process performance varies over time Utilizes continuous data

Control Charts:◦ Can determine if a process is in control

◦ Can identify specific causes of noise variation

154

155

0Subgroup 10 20 30 40

0

10

20

30

In

div

idu

al

Valu

e

Mean=10.98

UCL=26.81

LCL=-4.854

0

10

20

Mo

vin

g R

an

ge

1

R=5.952

UCL=19.45

LCL=0

Total e-Pro Change Transactions by Account from Sep 2002 thru Mar 2003

Generally, anything that falls outside the red lines is “Out of Control”. Trends and other conditions can occur and will be marked in red.

QI Macros > Control Charts (SPC) > XmRIndividuals Use the file Run Chart

Highlight columns A & B

Click “OK” for all titles

Note – this is data for the drive time in the class scenario

156

Run Chart

UCL 38.14

CL 35.09

LCL 32.03

31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0

X V

alu

es

1/1/2014 - 1/28/2014

X Chart

The light blue points are the actual data points…

157

UCL 3.75

CL 1.15

0.0

1.0

2.0

3.0

4.0

Ran

ge

1/1/2014 - 1/28/2014

R Chart

This is a range chart – it is looking at the difference between a point and the previous point

The red lines and points denote Out-of-Control conditions

158

A point outside of the Control Limits◦ The control limits are +/- 3 standard deviations

from the mean

6 points in a row increasing or decreasing

2 out of 3 points more than 2 standard deviations from the mean

8 points in a row on one side of the center line

159

160

Champion Measure Phase Checklist

161

Measure 8 – Tollgate and Approval

Measure Phase CommentsWhat are your deliverables for this phase? Summarize the findings.

Has your Problem Statement or Objective Statement changed? If yes, why?

Have you completed a process map? Who was involved in its development? What

did you discover?

Have you determined the current defect level and/or KPOV's (ppm, DPMO, etc. on

metric chart)?

Have you developed your Data Collection Plan?

Have you studied the measurement systems used to collect the data (MSA)? Are

better gauges required and what would be the cost?

Have you implemented your Data Collection Plan?

Have you determined the capability of the process?

Have you completed a Pareto analysis?

Do you have any Run Charts or Control Charts on you KPIVs or KPOVs

Have you determined your next steps?

Have you updated your financial valuation?

What are your conclusions from this phase?

Are you on track to meet the scheduled completion date?

Are you satisfied with the level of cooperation and support you are getting?

Have you obtained the signatures from leadership to move on to the next phase?

Project Team Measure Phase Checklist

162

Measure Tollgate Approval

Champion Approval Signature/Date:

Tollgate review approved unconditionally:

Tollgate review approved with the following contingencies:

Tollgate review dis-approved, list issues for resolution:

163

Documents

Identify and develop the current state “as · 5/4/2020 · 2 2 Review and amend cost of poor quality ... Review problem statement and project objective Develop project measure