© richard welke 2002 cis 4120 fa11: define/innovate bp’s cis 4120 fa12: define/innovate bp’s...

© Richard Welke 2002

CIS 4120 Fa11: Define/Innovate BP’sCIS 4120 Fa12: Define/Innovate BP’sSession 10:

LSS Improvement Techniques

Part-2

Richard WelkeDirector, CEPRIN

Professor, CISRobinson College of Business

Georgia State UniversityAtlanta, GA

A number of these slides were adapted from a presentation made by Peter

Sherman of Sherman6Sigma.com

CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2

© Richard Welke 2009-11© Peter Sherman & Richard Welke 2011/12

2

Transitioning from Measure to Analysis

The "M" in DMAIIC asks that we specify current values for measures related to performanceThe driver here is, again, variability and its controlThis is examined through the lens of "statistical process control"We're interested, as analysts, in finding out:

Which tasks are producing variabilityHow muchTheir contribution to the overall process outcome variability



(statistical) Control ChartsControl charts are graphical representations of the variation in a process over time. They plot time-

ordered data.

0 1 2 3 4 5 6 7 8 9 10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

7.5

8.5

9.5

10.5

11.5

12.5

Upper Control Limit 3 Standard Deviations Above

the Average

Lower Control Limit 3 Standard

Deviations Below the Average

Average

Dr. Walter A Shewhart of the Bell Laboratories, while studying process data in the 1920s, is credited with developing this powerful tool.

Valu

e (

e.g

. kg

)

Observation number

3

Measure Analyze Improve Implement ControlDefine



4

Control chart anatomy

Control Limits identify the expected limits of normal or random variation (common cause) that is present in the process being monitored. These limits are statistically derived from the data itself. In other words, the Control Limits are set by the process.

Common Causes (aka “Noise”)

99.73%

Special Causes (aka “Signals”)

Special Causes (aka “Signals”)

UCL

LCL

CL




5

Predictable processes

In-control* reflects the presence of Common Causes that makes the process consistent, stable, and

predictable

UCL

LCL

In-Control – All data points are within the upper and lower control limits.

CL

* Control does not mean the product or service will meet our customer’s needs




6

Unpredictable process

Out-of-control reflects the presence of Special Causes that make the process

inconsistent, unstable and not predictable

UCL

LCL

Out-of-Control (Special Causes) – any point touching or beyond the control limits

CL




7

Establishing stability

(b)




8

Establishing process capability



Analyze

99

MEASURE THE CURRENT PERFORMANCE

DEFINE THE OPPORTUNITY

IMPROVE PROCESS EFFICIENCY

ANALYZE THE CURRENT PROCESSES

CONTROL AND ADJUST NEW PROCESSES

IMPLEMENT IMPROVEMENTS

Customer FocusedData DrivenROI Oriented

• Map the process, gather initial performance data and determine current “Sigma” level, defects, delays, deviation

• Is my process in-control?• How capable is my

process?• Assess COPQ

• Analyze data for relationships

• Identify the most significant causes impacting performance

• Root Cause Analysis

• Clearly Identify and scope the problem• Define the Voice of the Customer

• Determine Critical to Quality (CTQ) factors• Link Big X’s with Big Y’s




Analysis

10

De-compose /

Aggregate / Prioritize the Data

Identify and Organize Potential Causes

Isolate and Verify Root

Causes

Quantify Cause-Effect

Relationships & confirm Root

Causes

Run ChartPareto Chart

BrainstormingCause-and-

Effect Diagram

5 Whys Scatter PlotStratified Frequency

PlotContingency Table

Purpose: Objective during Analyze stage is to make sense of the dataWant to identify the root causes (X’s) and relationships among variables that significantly affect our outputs (Y’s) in a process.

Problem Diagnosis Framework

Step 1

Step 3

Step 2

Step 4




11

Voice of the Customer

Jan 23

Jan 16

Jan 9

Jan 2

Dec 2

6

Dec 1

9

Dec 1

2De

c 5

Q4 201

0

Q3 201

0

Q2 20

1

Q1 201

0

8.0

7.8

7.6

7.4

7.2

7.0

6.8

C1

AHT

7.5

Care Call Center - AHT

Great!..Barry fixed it.

What did he do?

Suzie does a good job!

AHT is creeping up. Suzie

needs to fix. AHT is way up! Barry needs to

fix.

Reacting to False Signals…24x7 Fire-fighting

Target




Voice of the ProcessNo special causes. Process is

in-control stable, predictable. Behaving as

designed.

Control charts help us focus on the right actions at right time

7.5 min target

12




Four states of the process

LSL USL

Not In Control( Not Stable)

In Control(Stable)

Not meeting Customer

Specifications(Not Capable)

Meeting Customer

Specifications(Capable)

CustomerSpecification

Control Limit

LSL USLLSL USL

LSL USL

Voice of the Process

Vo

ice

of

the

Cu

sto

mer

Chaos

Ideal

Threshold

Kidding Ourselves

13



Alignment of VOC and VOP

Voice of the Customer

Voice of the Process

Tells us when we are not meeting our customer

specifications

Tells us when and how to take action

1st 5 Days

Unpredictable

J an 10

Dec 1

0

Nov 1

0

Oct 1

0

Sep 10

Aug 10

Jul 1

0

Jun 10

May 10

Apr 1

0

Mar 10

Feb 10

Jan 10

70

60

50

40

30

Indiv

idual V

alu

e

_X=49.55

UCL=68.37

LCL=30.74

CSAT ScoresI Chart

Predictable

14



Alignment of VOC and VOPIf the voices are not in alignment:

Shift the process aim (i.e., lower or raise the mean)Reduce the process variationChange the specifications*Proceed with caution

15



Pareto ChartPareto Charts used to prioritize problems or issues so that major problems or issues can be identifiedThe Pareto Principle, also known as the 80-20 rule, states that 80% of the consequences stem from 20% of the causes Pareto analysis focuses efforts on the problems that offer the greatest potential for improvement

By showing their relative frequency (or occurrence) in a descending bar graph

16

Vilfredo Pareto

1848-1923

The Pareto Principle: “The vital few and the trivial

many”Dr. Joseph M.

Juran1904-2008


16



Constructing a Pareto Chart

Steps to construct a Pareto Chart:1. Identify the metrics of consequences

(Y-axis) and the cause categories (X-axis)

2. Sort the categories by frequency in descending order.

3. Sum the counts and calculate the percentages for each category

4. List the categories on the horizontal axis and frequencies of consequences on the vertical axis

5. Draw the cumulative percentage line showing the portion of the total that each category represents

6. Interpret the results. Typically, this involves focusing on those categories that have the most frequent occurrence

17




18

Pareto chart example

Make numbers…make sense!


TT Channels (2010) Count TT Channels (2010) Count TT Channels (2010) Count TT Channels (2010) Count3rd Party Advisory 396 CSR 1 Legal 18 Retention - High 1848 Hour Call 63 Cust Email 41 LSR 19 Retention - Low 2Account Review 17 Cust Letter 3 Major Accounts 164 Retention - Medium 39Ad Campaign 2 Customer 1524 MAJOR ACCT 1908 Retention Voice 2AR 5 Customer Call 76193 Moves 68 Retention-Trans 60Bankruptcy Chp 11 2 Customer Call/Email 65 Mozy 182 Sleepy Report 32Bankruptcy Chp 13 1 Customer Care 1111 Netcool 152 Special Project 712Billing Team 11 Customer email 807 Network Mtce 101 SSA Support Call 8CAT 406 Customer Mail 328 Network Outage 410 SSA Support Email 2CAT ESC 614 Direct Sales 5 NMS 89632 SST Follow-Up 1CBOL 6545 E-Mail 1979 NOC 78 SST Remote Set-Up 1CBOL (rep) 645 EMAIL ABUSE 2504 Other 92 SST Support Call 36Channel Partner 139 EMAIL BR 1 Partner Access 34 SST Support Email 11Channel Sales 13 EMAIL CC 37 Phone 131660 T1 Manager 1996CHANNEL SUPPORT 974 EMAIL MT 1 Post-install 3 Vendor 6Chronic 194 EMAIL TS 1192 Proactive 1428 Voice Mail 110Chronic Proactive 483 Esc Email - Sales 3 Proactive - Escalation 18 Web 10453Chronic Referral 184 Esc Email - SC 4 Proactive - Other 525 Web Chat 3Chronic Retention 192 Esc Phone - SC 1 Proactive NMS 202 Web-Partner 68Chronic Retention-Trans 297 Exec Escalation 15 Proactive Repeat 8 Welcome Call 2CSA 61 FAX 51 Proactive Special Handling1CSA - High 2 Field Visit 23 Projects 48CSA - Low 1 Finance 1 QA 10CSA - Medium 2 Flowthru 1 Referral 90CSAT 38 Internal 3324 Retention 70

Phon

eNMS

Custo

mer

Cal

lW

ebCBOL

Inte

rnal

EMAIL

ABUSE

T1 M

anag

er

E-Mai

l

Other

0

50,000

100,000

150,000

200,000

250,000

300,000

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

39%

65%

87%90% 92% 93% 94% 95% 95%

100%2010 Trouble Tickets by Channel - Pareto Chart

87% of the trouble tick-

ets are from 3 channels.



Cause-Effect diagramAlso called Ishikawa , “fishbone” or C-E diagramA graphic display of lines and words that represent a meaningful relationship between an effect and its causesStructures potential causes so actual root causes can be identified and corrective action can be takenCreates a shared understanding of the problem

FinalProblem

Statement

(Cause)

(Effect)

Dr. Kaoru Ishikawa

1915-1989


19



Two C-E types

20

1. Standard Fishbone (5-M’s*)

2. Process Step Fishbone

6th – Mother nature

Other possible cause categories

- Environment- Regulatory- Policies- Procedures- Plant & Equipment

Problem / Effect

Machine Man

Materials Method

Measurement

Primary Cause

Secondary Cause

Problem / Effect

Step 3 Step 1

Step 4 Step 2

Step 5

Primary Cause

Secondary Cause

Step 6




C-E and the “5 Why’s”

Effect (Problem)

Cause B1

B2

B3

B4

B5

Stop when you reach the root cause

The problem is caused by B1, which is caused by B2, which is caused by B3, which is caused by B4, which is caused by B5 …Where should you stop?

The actual root cause will always be the lowest “why” you answered

Effect (Problem)

Cause B1

Why?

Why?

Why?

Why?


21

Getting to the Root Cause:



Example C-E diagram

22

Not Obtaining DNSCredentials

5) Remote Follow-up

4) SSA Visit

3) Scheduling

2) Order Entry / Verification

1) Sales Close

6) Activation

7) WelcomeVisit

Pressure to keep orders moving / over-reliance

on RSST / SSAs

Web Developer is big obstacle

CommunicationBreakdown on “Cancelling

Service” (techs should make customers aware of

impact not providingDNS credentials to point

Email / Web Hosting

Customer doesn’t have

Confusion whether customer calls SSA or Remote SST

Little communicationTo SSA to prep

DNS metrics not part ofSC’s scorecard (i.e., clean install)

Right person assignedbut no rigorous

protocols to engage them

Insufficient communicationto customer re: expectation of what SSA will do during visit

and what customer needs to provide

Not Obtaining DNS CredentialsCause and Effect Diagram (v0.2)

10-13-10

No guidelines / processes for customer to follow

No guidelines / processes how to handle scenarios or

which ones to triage

Lack of process toolsTo manage follow-up calls

No communication of value / critical nature of DNS credentials

Not understanding how to identifywhich scenario customers

falls into

DNS credentials is timeconsuming / not easy process

No detailed documentationof customer scenarios

Little time to allocate to DNSduring Smart Start visit

DNS info not available for SS visit

No visibility to the status of obtaining DNS credentials

throughout the process

No means to track internal DNS status(i.e., DNS credentials populated on

Sales & Activation Worksheet, SC warm transfers to RSST)

Difficulty keeping schedule

Customer not motivated

No communication upstreamTo customer of value / critical

Nature of DNS credentials

Upstream gaps (Sales, SC), RSST not regularly engaged

Need guidelines /processes

Challenges obtaining DNS info.

Inconsistent follow-upBy SSA to RSST


Q: What kind of C-E is this?



Scatter plots

Scatter plots visually show the relationship between two variables. They can help verify causal relationships byDiscovering whether two variables are relatedFinding out if changes in one variable are associated with changes in the otherTesting for a cause-and-effect relationship (but also noting that a relationship does not always imply causation)

23

X (inputs)

Y (

ou

tpu

ts)

Independent

Dep

en

den

t




Scatter plot patterns

24

Possible PositiveCorrelation

Strong PositiveCorrelation

Other Pattern

No CorrelationStrong NegativeCorrelation

Possible NegativeCorrelation




Scatter plot example

25

8007006005004003002001000

20

15

10

5

0

Total Info

CTI

Tim

e1

Scatterplot of CTI Time1 vs Total Info


As Account Summary

info increases

(X-Axis) CTI pop time (Y-Axis) takes

longer?



Conjecture: Variation in training, technique, and procedures at different Cbeyond offices accounts for much of the variation in the time on site for Activation.

Data: Measure time during Activation in different locations.

26

Activation Time(all 3

locations)2.26 hours

Stratified frequency Plots

Atlanta

Dallas

Los Angeles

Cause (X) = discrete data (location)Effect (Y) = continuous data on time on site for Activation

Dallas’ Activation times are faster than those at either Atlanta or Los Angeles.A next step would be to see if we can discover the cause for these locational

differences. Visit the 3 markets to observe the current state.

1.72 hours

2.64 hours

2.1 hours




Conjecture: The shorter the activation time, the more likely customers will be satisfied (be “Promoters” of the product/service)

Data: Measure Activation time spent with customer

Place customers into three separate categories:

Promoter (favorably disposed)

Passive (neutral)

Detractor (not that happy)

27

Stratified frequency plots

Detractor

Passive

Promoter

2.62 hours

1.95 hours

2.3 hours

Example shows that most Promoters occur when Activation Time on site is less than 2 hours. Most Detractors occurred when Activation Time is more than 2.6 hours.

Cause (X) = continuous data - (time with customer)Effect (Y) = discrete data - (Promoter, Passive, Detractor)




IC-10 Scatter Plot

28

Total Info CTI Time82 10.0125 4.0221 6.072 0.0384 20.0133 6.058 0.0125 3.0100 4.058 0.0228 6.0192 6.0303 8.068 0.040 0.034 0.0357 13.0292 9.0442 15.0352 6.0351 6.0236 7.031 0.062 0.014 0.0121 0.050 0.045 0.0141 0.0169 8.082 0.0152 4.0

Scenario:During the a particular trial application of a new software version, the team noticed delays in the screen display of the Account Summary pageJim Smith observed that the amount of data stored for the account summary page (numbers of Service Requests, Interactions, Activities) might be delaying the Cycle Time Interaction (CTI)

To-Do:Using the data at the right, identify the X (input) and Y (variables) and construct a Scatter PlotBe prepared to interpret your findings


© richard welke 2002 cis 4120 fa11: define/innovate bp’s cis 4120 fa12: define/innovate bp’s...

Documents

peter sherman richard

richard welke director

lss improvement techniques

peter sherman of

cis4120 fa12 session

process data

process capability

process inconsistent