© 2001 Six Sigma Academy Six Sigma…Key Concepts John Krupar

© 2001 Six Sigma Academy

Six Sigma…Key ConceptsSix Sigma…Key Concepts

John Krupar


Cost ReductionCost ReductionOperational ExcellenceOperational Excellence

Client SatisfactionClient SatisfactionGrowthGrowth

ProcessCapability

VARIABILITYVARIABILITY

FMEA CTQ

Multivariate Statistics

Mistake Proofing

Reliability

SPC

Process Mapping

DOE

C&E Matrix

Problem Solving

SystemsThinking

WASTEWASTE

Visual Controls

Linkages & Flow

Testing forValue

Value Stream Mapping

5S

Takt

Standardize

UnpredictableDemand

Balanced Work

JIT

GROWTHGROWTH

Supplier Capability QFD

Design From Ground Up

Predictive Modeling

ProductDevelopment

TRIZ

The Breakthrough Strategy®The Breakthrough Strategy®


Cost ReductionCost ReductionOperational ExcellenceOperational Excellence

Client SatisfactionClient SatisfactionGrowthGrowth

ProcessCapability

DMAICDMAIC

FMEA CTQ

Multivariate Statistics

Mistake Proofing

Reliability

SPC

Process Mapping

DOE

C&E Matrix

Problem Solving

SystemsThinking

LEANLEAN

Visual Controls

Linkages & Flow

Testing forValue

Value Stream Mapping

5S

Takt

Standardize

UnpredictableDemand

Balanced Work

JIT

DFSSDFSS

Supplier Capability QFD

Design From Ground Up

Predictive Modeling

ProductDevelopment

TRIZ


Solve Difficult Business Problems for the Last Time… Solve Difficult Business Problems for the Last Time…

Using the Appropriate ToolsUsing the Appropriate Tools

Solve Difficult Business Problems for the Last Time… Solve Difficult Business Problems for the Last Time…

Using the Appropriate ToolsUsing the Appropriate Tools


BreakthroughStrategy

Characterization

Optimization

Phase 1:Measure

Phase 2:Analyze

Phase 3:Improve

Phase 4:Control

determination of improvement opportunities

Process Characterization is concernedwith the identification and benchmarkingof key process characteristics, and the

and goals.

Process Optimizationis aimed at identifying,improving and controlling the keyprocess variables which exertundesirable influence over the keyprocess characteristics.

Phase 0:Define

Phase 0:Define

•Define the problem and agree on the objective. Define the metrics.

•Map Process. Validate measurement systems. Collect relevant data and begin basic analysis

•Identify the few key factors which are directly influencing the problem.

•Determine optimum values for the few key factors which resolve the problem.

•Determine long term control measures which will ensure that improvements are sustained.



Key Concepts - Define & MeasureKey Concepts - Define & Measure

Understanding Customer Needs

Six Sigma Starts & Ends with the CustomerSix Sigma Starts & Ends with the CustomerSix Sigma Starts & Ends with the CustomerSix Sigma Starts & Ends with the Customer


Val

ue

Performance

Satisfiers

Satisfier – Non-Smoking Room Available

Linear

Linear -- $10 Discount

Delighters

Delighter – Bellman greets by name, Correct Pillow, Shelled pistachio’s

Voice of the CustomerVoice of the Customer

Kano Analysis…One of Many ToolsKano Analysis…One of Many ToolsKano Analysis…One of Many ToolsKano Analysis…One of Many Tools


Problem Statement

Baseline Performance and EntitlementMeasurement Systems Validation

Process Mapping

Practical Problem:

750,000 calls out of the 4,000,000 received at the call center are not resolved during the initial conversation with the client. These calls require the call center operator to make a return call to the client to answer their request. During a recent client survey, 20% of the clients stated that they were calling regarding an unresolved issue from a previous call.

The Problem Statement is written understanding of the practical problem Process Mapping gives a visual representation of the “Actual Process”

Short Term and Long Term Capability provide us a look at what is possible aswell as what our customers are experiencing from our process

Measurement Systems Analysis ensures that all Parties have the sameOperational Definition of the Problem

Client Iniates Call

Call Center Operator

Answers Call

Validate Customer ID

Number

Client Describes Question

Client Sent to Branch for New

Account Generation

Data Available to

Resolve

Operator Answers Question

Supervisor or Lead available

Yes

No

Research Area Answer

Call

No

Yes

Place Client on Call Back List

No Yes

AutomatedVoice

Response

Auto-Transferverify

Balance

Auto-TransferverifyLast 5

Transactions

Balance Verification

Operator Assisted

Verify Transaction

Current Client

Non-Client

1 2 3

70

80

90

100

Appraiser

Pe

rce

nt

Within Appraiser

1 2 3

50

60

70

80

90

100

Appraiser

Pe

rce

nt

Appraiser v s Standard

Assessment AgreementDate of study: March 21, 2001Reported by: Joe LynchName of product: Call Center - ResolutionMisc:

[ , ] 95.0% CI

Percent

Actual (LT)

Potential (ST)

3210

Process Performance

USLLSL

Actual (LT)

Potential (ST)

1,000,000

100,000

10,000

1000

100

10

1

200010000

Potential (ST)Actual (LT)

Sigma

PPM

(Z.Bench)

Process Benchmarks

1851.60

0.80

212889

2.90

Process Demographics

0

2.5

Opportunity:

Nominal:

Lower Spec:

Upper Spec:

Units:

Characteristic:

Process:

Department:

Project:

Reported by:

Date:

Report 1: Executive Summary

Call Center Case Study – Measure PhaseCall Center Case Study – Measure Phase

Illustr

ative E

xample


Key Concepts - AnalyzeKey Concepts - Analyze

Entitlement

Variation


EntitlementEntitlement

The focus should be to shift the overall performance to the Entitlement level. This drives dramatic short-term improvements in cost and quality with minimal investment in technology.

Only after the Entitlement level is achieved should an investment be made to redesign / reengineer the systems or infrastructure.

The optimum level that a process currently performs is the Entitlement.

This can be replicated once the variables are truly understood.

Output Variation

Days 1 5.5 11 18 38

MeanEntitlement

Performance Shift

Delivery of client Orders

Upper Specification Limit

Avoid Capital Investment Until Entitlement is ReachedAvoid Capital Investment Until Entitlement is ReachedAvoid Capital Investment Until Entitlement is ReachedAvoid Capital Investment Until Entitlement is Reached


VariationVariation

Average vs. Variation

• Average tells little about client experience

• In example 1, the average number of days it takes to deliver our product is 40 days. However, this process has a high variation, with 50% of the orders filled between 40-80 days

• In example 2, the average number of days it takes to deliver our product is also 40 days. However, in this process, there is very little variation and the order is filled in no more than 50 days

• To drive dramatic improvements in performance, the variance in a process must first be minimized

Days 10 40 80

Output Variation

Mean

Example #1

Example #2

Product Delivery Process

.Businesses do not Excel Managing ‘Averages’. Businesses (and Customers)Businesses do not Excel Managing ‘Averages’. Businesses (and Customers)

Are Negatively Impacted by Extremes in the Variation of the ProcessAre Negatively Impacted by Extremes in the Variation of the Process

Businesses do not Excel Managing ‘Averages’. Businesses (and Customers)Businesses do not Excel Managing ‘Averages’. Businesses (and Customers)

Are Negatively Impacted by Extremes in the Variation of the ProcessAre Negatively Impacted by Extremes in the Variation of the Process


Data Collection Plan

Hypothesis Testing

Multi-Vari Analysis

A Multi-Vari begins with a specific data sampling plan The Multi-Vari provides a graphical representation of the data

The Vital Few Variables are those X’s that are supported by the data and deserve further analysis

Hypothesis Tests are statistical tests that attempt to prove statistically ifthe averages and standard deviations of groups are the same or different

Statistical Problem:Data was collected from each of 16 operators, three times a day, over three days from each of the three call centers. The graphical representation identified 4 of the operators as having a lower mean and a very low standard deviation for resolution of client calls. This phenomenon was evident only in the morning.

Vital Few Variables

321

321321321

3.2

2.2

1.2

time of day

min

utes

to

r

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

Multi-Vari Chart for minutes to r by operator - time of dayday

operator

1.20.70.2

95% Confidence Intervals for Sigmas

P-Value : 0.000

Test Statistic: 8.066

Levene's Test

P-Value : 0.000

Test Statistic: 120.764

Bartlett's Test

Factor Levels

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Minutes to Resolve Based on the Multi-Vari Analysis and confirmed by the Hypothesis Testing, Operator and time of day was statistically significant. The X’s identified in our process exploration that are potentially impacted by this interaction are:

x1 Technical Support Availabilityx2 Bandwidth Availabilityx3 Market Volumesx4 Client Performance

Call Center Case Study – Analyze PhaseCall Center Case Study – Analyze Phase

Illustr

ative E

xample


Key Concepts - ImproveKey Concepts - Improve

WasteTransfer Function(Re)Design “To Be” State


Waste EliminationWaste Elimination

Lean (Waste Elimination) can be summarized in five principles*:

Principle 1 - Precisely specify the value of a specific process

Principle 2 - Identify the value stream for each process

Principle 3 - Allow value to flow without interruptions

Principle 4 - Let the customer pull value from the process

Principle 5 - Continuously pursue perfection

* Womack, J. P. and D. T. Jones, 1996, Lean Thinking, Simon & Schuster


Pareto of Effects

Statistical SolutionMain Effects Plot

Interaction Plots

A Design of Experiments is the only true Cause and Effect tool to understandwhat impact each of the vital few variables has on the output

The final output of a Design of Experiment is a Mathematical Transfer Functiondescribing Y=f(x).

Main Effects Plots identify those variables whose impact is directly tiedto the output in question.

1.81.61.41.21.00.80.60.40.20.0

A

B

AB

C

ABCD

ABC

BD

AD

ACD

ABD

BCD

BC

AC

D

CD

Pareto Chart of the Effects(response is Time of, Alpha = .10)

A: TechnicaB: BandwidtC: Market VD: Client P 1

4

7711

3.5

2.5

1.5

0.5

Bandw idth

Technical Su

Mea

n

Interaction Plot (data means) for Time of Reso

Interaction Plots identify those variables that their result depends on the settingof another variable.

Client ProfiMarket VolumBandwidthTechnical Su

21217141

2.4

2.0

1.6

1.2

0.8

Tim

e o

f R

es

o

Main Effects Plot (data means) for Time of Reso

Conclusions: From the mathematical model, it is obvious that when bandwidth and Technical Support are available, many of our operators are ableto resolve the clients questions during the first call.

Call Center Case Study – Improve PhaseCall Center Case Study – Improve Phase

Illustr

ative E

xample


Key Concepts - ControlKey Concepts - Control

Measure … Forever

Control …. The Key Differentiator of Six SigmaControl …. The Key Differentiator of Six SigmaControl …. The Key Differentiator of Six SigmaControl …. The Key Differentiator of Six Sigma


Aspects of ControlAspects of Control

AMOUNT OF CONTROL

Ver

bal I

nstr

uctio

ns

Writ

ten

Inst

ruct

ions

Vis

ual M

anag

emen

t

Sta

tistic

al P

roce

ss C

ontr

ol

Pok

a -

Yok

e(M

ista

ke P

roof

ing)

Des

ign

for

Six

Sig

ma

Amount of Effort of Process Owner

Aut

omat

ion

Desired Direction of Control

Systemic Change


“Someone, who with their team, solves a difficult business problem…

Definition of a Black BeltDefinition of a Black Belt

…for the last time.”


Practical Solution

Financial ResultsFinal Capability

Control Continuum

The Control Phase focuses on implementing the practical solution

Driving the solution to a region of greater control assures the process ownerthat the solution is simpler than the problem… and the solution is mistake proof

Each 6 Sigma project is individually audited to ensure that it has a positiveimpact on the business, the client, and employee satisfaction.

AMOUNT OF CONTROL

Ver

ba

l Ins

tru

ctio

ns

Writ

ten

Ins

tru

ctio

ns

Vis

ual P

roce

ss

Sta

tis

tica

l P

roc

es

s C

ntr

l

Pok

a -

Yo

ke(M

ista

ke P

roo

fing

)

Des

ign

for

Six

Sig

ma

Amount of Effort Expended by the Process

1. Bandwidth Availability:A legacy tool, which requires a significant amount of bandwidth is used by a few of the operators. This tool could only be practically employed during the morning hours of the CC East and CC South,when users logged on to the system were at a minimum. This legacy tool was thought to be obsolete, but when IT was informed that operators still had a need for the screen, they reconfigured it to use just a fraction of the bandwidth. This allowed all operators to have the screen added to available on-line help.

2. Technical Resource Availability:When the “better operators” were monitored, it was shown that they were able to quickly answer their clients questions when a couple of supervisors in Cashiering were available by phone. These resources were generally only available during the early morning hours. When we spoke with these supervisors, we determined that they were reviewing information from a level of a help screen not authorized to the call center operators. A risk assessment was completed and it was determined that all call center operators should have this access.

Actual (LT)

Potential (ST)

210

Process Performance

USLLSL

Actual (LT)

Potential (ST)

1,000,000

100,000

10,000

1000

100

10

1

200010000

Potential (ST)Actual (LT)

Sigma

PPM

(Z.Bench)

Process Benchmarks

77.9617

2.47

6818.44

3.78

Process Demographics

0

2.5

Opportunity:

Nominal:

Lower Spec:

Upper Spec:

Units:

Characteristic:

Process:

Department:

Project:

Reported by:

Date:

Report 1: Executive Summary

The final capability is the statistical proof that the process is now improved

Defect Rate Reduction: 97%DPMO Reduction 205,000Sigma Level Improvement 0.88Cost Savings

$2.75M

Summary

Call Center Case Study – Control PhaseCall Center Case Study – Control Phase

Illustr

ative E

xample


A Critical “Unintended” ConsequenceA Critical “Unintended” Consequence

Leadership

Development


How Is Six Sigma Different From How Is Six Sigma Different From Other Change Initiatives?Other Change Initiatives?

IT IS NOT:

Just a “quality” program

IT IS:

Focused on strategic business priorities

Fact-based decision making

Focused on minimizing waste and variation

Dedicated resources with clear accountability

Narrowly scoped projects that are measured, statistically validated, controlled, and sustained

Quantified project benefits

Demonstrated track record of success across industries

An Enabler for Cultural Transformation

• Common Language

• Common Methodology

• Common Metrics

Documents

© 2001 Six Sigma Academy Six Sigma…Key Concepts John Krupar