CIS 4120 Fa12:
Fa11: Define/Innovate BP’s
Session 10:
LSS Improvement Techniques
Part-2
Richard Welke
Director, CEPRIN
Professor, CIS
Robinson College of Business
Georgia State University
Atlanta, GA
© Richard Welke 2002
A number of these slides were adapted from a
presentation made by Peter Sherman of
Sherman6Sigma.com
Transitioning from Measure to Analysis
The "M" in DMAIIC asks that we specify current
values for measures related to performance
The driver here is, again, variability and its control
This is examined through the lens of "statistical
process control"
We're interested, as analysts, in finding out:
Which tasks are producing variability
How much
Their contribution to the overall process outcome
variability
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
2
(statistical)
Control Charts
Define
Measure
Analyze
Improve
Implement
Control
Control charts are graphical representations of the variation
in a process over time. They plot time-ordered data.
Upper Control Limit
Value (e.g. kg)
12.5
3 Standard Deviations
Above the Average
11.5
10.5
Average
9.5
8.5
Lower Control Limit
3 Standard Deviations
Below the Average
7.5
0
10
20
30
Observation number
Dr. Walter A Shewhart of the Bell Laboratories, while
studying process data in the 1920s, is credited with
developing this powerful tool.
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
3
Control chart anatomy
Define
Measure
Analyze
Improve
Implement
Control
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.
UCL
CL
Special Causes (aka “Signals”)
Common Causes (aka
“Noise”) 99.73%
LCL
Special Causes (aka “Signals”)
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
4
Predictable processes
Define
Measure
Analyze
Improve
Implement
Control
In-Control – All data points are within the
upper and lower control limits.
UCL
CL
LCL
In-control* reflects the presence of Common
Causes that makes the process consistent,
stable, and predictable
* Control does not mean the product or service will meet our customer’s
needs
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
5
Unpredictable process
Define
Measure
Analyze
Improve
Implement
Control
Out-of-Control (Special Causes) – any point touching
or beyond the control limits
UCL
CL
LCL
Out-of-control reflects the presence of Special
Causes that make the process inconsistent,
unstable and not predictable
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
6
Establishing stability
Define
Measure
Analyze
Improve
Implement
Control
(b)
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
7
Establishing process capability
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
8
Analyze
Define
DEFINE THE OPPORTUNITY
Measure
Analyze
Improve
Implement
Control
• 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
CONTROL AND ADJUST
MEASURE THE
NEW PROCESSES
CURRENT PERFORMANCE
Customer Focused
Data Driven
ROI Oriented
IMPLEMENT IMPROVEMENTS
• 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 THE CURRENT PROCESSES
• Analyze data for
relationships
• Identify the most significant
causes impacting
performance
• Root Cause Analysis
IMPROVE PROCESS EFFICIENCY
9
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
9
Analysis
Define
Measure
Analyze
Improve
Implement
Control
Purpose:
Objective during Analyze stage is to make sense of the data
Want 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 2
De-compose /
Aggregate /
Prioritize the
Data
Identify and
Organize
Potential
Causes
Run Chart
Pareto Chart
Brainstorming
Cause-andEffect Diagram
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
Step 3
Isolate and
Verify Root
Causes
5 Whys
Step 4
Quantify CauseEffect
Relationships &
confirm Root
Causes
Scatter Plot
Stratified Frequency Plot
Contingency Table
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
10
Voice of the Customer
Define
Measure
Analyze
Care Call Center - AHT
AHT is creeping
up. Suzie needs to
fix.
AHT is way up!
Barry needs to
fix.
8.0
7.8
7.6
Improve
Implement
Control
Target
AHT
7.5
7.4
7.2
7.0
Suzie does a
good job!
6.8
Q1
Great!..Barry
fixed it. What
did he do?
10
0
2
Q2
1
20
Q3
10
0
2
Q4
10
0
2
c
De
5
c
De
12
c
De
19
c
De
26
n
Ja
2
J
an
9
Ja
n
16
n
Ja
23
C1
Reacting to False Signals…24x7 Fire-fighting
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
11
Voice of the Process
Define
Measure
Analyze
Improve
Implement
Control
No special causes. Process is incontrol  stable, predictable.
Behaving as designed.
7.5 min
target
Control charts help us focus on the right actions at right time
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
12
Four states of the process
Voice of the Process
Voice of the Customer
Control
Customer Limit
Specification
Not meeting
Customer
Specifications
Not In Control
In Control
( Not Stable)
(Stable)
LSL
USL
LSL
USL
(Not Capable)
Threshold
Chaos
Meeting
Customer
Specifications
LSL
USL
LSL
USL
(Capable)
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
Kidding
Ourselves
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
Ideal
13
Alignment of VOC and VOP
1st 5 Days
Voice of the Customer
Unpredictable
Tells us when we are not
meeting our customer
specifications
CSAT Scores
I Chart
70
UCL=68.37
Predictable
Tells us when and how to take
action
Individual Value
60
_
X=49.55
50
40
LCL=30.74
30
Voice of the Process
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
n
Ja
10
Fe
b
10
ar
M
10
r
Ap
10
ay
M
10
n
Ju
10
0
10
10 t 10
10
10
10
l1
v
c
g
p
n
Ju
Oc
Ja
No
De
Au
Se
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
14
Alignment of VOC and VOP
If the voices are not in alignment:
Shift the process aim (i.e., lower or raise the mean)
Reduce the process variation
Change the specifications*
Proceed with caution
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
15
Pareto Chart
Define
Pareto Charts used to
prioritize problems or issues
so that major problems or
issues can be identified
The 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
Measure
Analyze
Implement
Control
Vilfredo Pareto
1848-1923
The Pareto Principle: “The vital few
and the trivial many”
Dr. Joseph M. Juran
1904-2008
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
Improve
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
16
16
Constructing a Pareto Chart
Define
Measure
Analyze
Improve
Implement
Control
Steps to construct a Pareto Chart:
1. Identify the metrics of consequences (Yaxis) 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
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
17
Pareto chart example
TT Channels (2010) Count
3rd Party Advisory
396
48 Hour Call
63
Account Review
17
Ad Campaign
2
AR
5
Bankruptcy Chp 11
2
Bankruptcy Chp 13
1
Billing Team
11
CAT
406
CAT ESC
614
CBOL
6545
CBOL (rep)
645
Channel Partner
139
Channel Sales
13
CHANNEL SUPPORT
974
Chronic
194
Chronic Proactive
483
Chronic Referral
184
Chronic Retention
192
Chronic Retention-Trans 297
CSA
61
CSA - High
2
CSA - Low
1
CSA - Medium
2
CSAT
38
TT Channels (2010) Count
CSR
1
Cust Email
41
Cust Letter
3
Customer
1524
Customer Call
76193
Customer Call/Email
65
Customer Care
1111
Customer email
807
Customer Mail
328
Direct Sales
5
E-Mail
1979
EMAIL ABUSE
2504
EMAIL BR
1
EMAIL CC
37
EMAIL MT
1
EMAIL TS
1192
Esc Email - Sales
3
Esc Email - SC
4
Esc Phone - SC
1
Exec Escalation
15
FAX
51
Field Visit
23
Finance
1
Flowthru
1
Internal
3324
TT Channels (2010) Count
Legal
18
LSR
19
Major Accounts
164
MAJOR ACCT
1908
Moves
68
Mozy
182
Netcool
152
Network Mtce
101
Network Outage
410
NMS
89632
NOC
78
Other
92
Partner Access
34
Phone
131660
Post-install
3
300,000
Proactive
1428
Proactive - Escalation
18
Proactive - Other
525
250,000
Proactive NMS
202
Proactive Repeat
8
Proactive Special200,000
Handling1
Projects
48
QA
10
Referral
90
150,000
Retention
70
TT Channels (2010) Count
Retention - High
18
Retention - Low
2
Retention - Medium
39
Retention Voice
2
Retention-Trans
60
Sleepy Report
32
Special Project
712
SSA Support Call
8
SSA Support Email
2
SST Follow-Up
1
SST Remote Set-Up
1
SST Support Call
36
SST Support Email
11
T1 Manager
1996
Vendor
6
Voice Mail
110
Web
10453
Web Chat
3
Web-Partner
68
Welcome Call
65%2
Define
Measure
Analyze
Improve
2010 Trouble Tickets
by Channel - Pareto Chart
Implement
Control
100%
94% 95% 95%
90% 92% 93%
87%
100%
90%
80%
70%
87% of the trouble
tickets are from 3
channels.
39%
100,000
60%
50%
40%
30%
20%
50,000
0
10%
0%
Make numbers…make
sense!
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
18
Cause-Effect diagram
Define
Measure
Also called Ishikawa ,
“fishbone” or C-E
diagram
A graphic display of lines
and words
that represent a
meaningful relationship
between an effect and its
causes
(Cause)
Structures potential
causes so actual root
causes can be identified
and corrective action can
be taken
Creates a shared
understanding of the
problem
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
Analyze
Improve
Implement
Control
Final
Problem
Statement
(Effect)
Dr. Kaoru Ishikawa
1915-1989
1
Two C-E types
Define
Measure
Analyze
Improve
Implement
Control
1. Standard Fishbone (5-M’s*)
Measurement
Machine
Man
Secondary Cause
Primary Cause
Problem / Effect
2. Process Step Fishbone
Materials
Method
Step 5

6th
Step 3
– Mother nature
Step 1
Secondary Cause
Primary Cause
Other possible cause categories
- Environment
- Regulatory
- Policies
- Procedures
- Plant & Equipment
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
Problem / Effect
Step 6
Step 4
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
Step 2
20
C-E and the “5 Why’s”
Define
Measure
Analyze
Improve
Implement
Control
Getting to 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?
Cause B1
Stop when you
reach the root
cause
Why?
B2
B3
B4
B5
Why?
Why?
Why?
Effect (Problem)
(Problem)
Effect
The actual root cause will always be the lowest “why” you answered
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
21
Example C-E diagram
Define
Not Obtaining DNS Credentials
Cause and Effect Diagram (v0.2)
10-13-10
1) Sales Close
Measure
Analyze
Improve
Implement
Control
Q: What kind of CE is this?
3) Scheduling
5) Remote
Follow-up
7) Welcome
Visit
Customer doesn’t have
No communication of value / critical
nature of DNS credentials
No guidelines / processes for
customer to follow
Little communication
To SSA to prep
Difficulty keeping
schedule
Customer not
motivated
No communication upstream
To customer of value / critical
Nature of DNS credentials
Not understanding how to identify
which scenario customers
falls into
Insufficient communication
to customer re: expectation of
what SSA will do during visit
and what customer
needs to provide
Lack of process tools
To manage follow-up calls
Communication
Breakdown on “Cancelling
Service” (techs should
make customers aware of
impact not providing
DNS credentials to point
Email / Web Hosting
Little time to allocate to DNS
during Smart Start visit
DNS info not
available for SS visit
Upstream gaps (Sales, SC),
RSST not regularly engaged
Need guidelines /
processes
No visibility to the status
of obtaining DNS credentials
throughout the process
Pressure to keep orders
moving / over-reliance
on RSST / SSAs
Not Obtaining DNS
Credentials
DNS credentials is time
consuming / not easy process
No guidelines / processes
how to handle scenarios or
which ones to triage
No detailed documentation
of customer scenarios
6) Activation Inconsistent follow-up
By SSA to RSST
Confusion whether customer calls SSA or Remote SST
DNS metrics not part of
SC’s scorecard (i.e., clean install)
Challenges obtaining DNS info.
4) SSA Visit
Right person assigned
but no rigorous
protocols to engage them
Web Developer is big obstacle
No means to track internal DNS status
(i.e., DNS credentials populated on
Sales & Activation Worksheet,
SC warm transfers to RSST)
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
2) Order Entry /
Verification
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
22
Scatter plots visually show
the relationship between two
variables. They can help
verify causal relationships by
Discovering whether two
variables are related
Finding out if changes in one
variable are associated with
changes in the other
Testing for a cause-andeffect relationship (but also
noting that a relationship
does not always imply
causation)
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
Y (outputs)
Define
Measure
Analyze
Improve
Implement
Control
Dependent
Scatter plots
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
X (inputs)
Independent
23
Scatter plot patterns
Define
Measure
Analyze
Improve
Implement
Strong Positive
Correlation
Strong Negative
Correlation
No Correlation
Possible Positive
Correlation
Possible Negative
Correlation
Other Pattern
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
Control
24
Scatter plot example
Define
Measure
Analyze
Improve
Implement
Control
Scatterplot of CTI Time1 vs Total Info
As Account
Summary info
increases (XAxis) CTI pop
time (Y-Axis)
takes longer?
20
CTI Time1
15
10
5
0
0
100
200
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
300
400
Total Info
500
600
700
800
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
25
Stratified frequency Plots
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.
Activation
Time
(all 3
locations)
2.26 hours
Define
Measure
Analyze
Improve
Implement
Control
2.1 hours
Atlanta
1.72 hours
Dallas
2.64 hours
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.
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
26
Stratified frequency plots
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:
Define
Measure
Analyze
Improve
Implement
Control
2.62 hours
Detractor
2.3 hours
Passive
1.95 hours
Promoter (favorably disposed)
Passive (neutral)
Detractor (not that happy)
Promoter
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)
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
27
IC-10 Scatter Plot
Define
Measure
Analyze
Scenario:
During the a particular trial application of a new
software version, the team noticed delays in
the screen display of the Account Summary
page
Jim 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 Plot
Be prepared to interpret your findings
© Peter
Richard
Sherman
Welke 2009-11
& Richard Welke 2011/12
CIS4120 Fa12 Session 10 LSS Improvement Techniques Part-2
Improve
Implement
Total Info
82
125
221
72
384
133
58
125
100
58
228
192
303
68
40
34
357
292
442
352
351
236
31
62
14
121
50
45
141
169
82
152
Control
CTI Time
10.0
4.0
6.0
0.0
20.0
6.0
0.0
3.0
4.0
0.0
6.0
6.0
8.0
0.0
0.0
0.0
13.0
9.0
15.0
6.0
6.0
7.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
8.0
0.0
4.0
28