Six Sigma Process Improvement

1. PABIT Solutions
Introduction to Process Improvement –
MGS 618 – Process Analytics and IT Risk Management
Supreet Kaur
5/22/2018

2. DEFINE
26/8/2018

3. Project Outline
3
 Company back ground
 PABIT Solutions, is a small software company with an annual net
worth $10 million.
 The company has 5 centers of operations across the US.
 Buffalo, NY and Cleveland, OH house the product development,
sales and marketing, software engineering, accounting and
shipping departments.
 The other three sites are call centers, in Buffalo, NY, Tucson, AZ,
and Honolulu, HI.
 Each call center handles calls for their region, broken down into
the following types:
 Customer Sales
 Onboarding
 Customer Servicing
 Customer Complaints
6/8/2018

4. Project Outline
46/8/2018
CEO Pamela
Anne
Bronkowski
VP Customer
Contact, Rupert
Fries
Call Center
Manager (CCM)
Joe Smith,
Buffalo, NY
Mary Decker,
Tucson, AZ
Jerry Jones,
Honolulu, HI

5. Project Outline
5
 Background of Call Center Managers
 Buffalo, NY – Joe Smith, is an experienced Six Sigma Green Belt.
He has recently set up a new phone system that allows his team to
track their data on call time, transfers, hold time, etc.
 Tucson, AZ - Mary Decker, is a 15-year veteran in call center
management. She has been with PABIT Solutions since 2001,
 Honolulu, HI - Jerry Jones, has been a call center manager for 4
years. Jerry has been with PABIT Solutions for 8 years.
6/8/2018

6. Project Outline
6
 What has been going right
 The Company is planning to expand their business.
 The CEO, Pamela, has reached out to Rupert Fries to put together
a report for the analysis of the on-going operations of the three
call-centers.
 Rupert has formed a Six-Sigma team and has asked Joe Smith –
an experience Six Sigma Green Belt to lead the Project.
 The workers at Joe’s centers work flexible hours.
 Work schedules are adjusted so the number of FTE(Full-Time
Employees) matched projected volumes.
 Joe has been able to track many performance metrics such as:
 Hold time
 Talk time per FTE
 Call volume
 % on hold
 FTE utilization
6/8/2018

7. Project Outline
7
 What gives cause for concern
 Customer dissatisfaction with PABIT Solutions Customer Service
and Onboarding process.
 The CEO does not have any hard data to analyze the situation and
hence she is concerned.
 The existing data shows that the number of customers failing to
complete Onboarding is rising.
 There was an increase in Onboarding abandonment rates from
2013 to 2014; from an average of 16% in 2013 to 35% in 2014.
 The Buffalo region has set a target of no more than 7% of
Customers withdrawing from the Onboarding process.
 There is a concern over the amount of variation in the data.
 Call volume for Customer Servicing tend to spike the first week of
the month when users are trying to update their patches.
6/8/2018

8. 8
Problem Statement
1. Customers are dissatisfied with the
customer service.
2. The customers are not completing the
onboarding process.
3. CEO does not have data to analyze the
problem.
Project Boundaries
• Process Starts with:
Customer logging into the
official website or placing a
call on the hotline number.
• Process Ends with:
Email is sent to the customer
with the password
credentials.
In Scope:
• Official
Website
• 24*7
Customer
Hotline.
Out of
Scope:
Process
outside
customer
onboarding
and
customer
service.
Goal Statement
• Improve the abandonment rate within 90 days
or less.
• Improve the process to attain customer
satisfaction.
• Collect and document the existing data in a
meaningful manner.
Benefits
Quantitative Qualitative
Reduce or
Remove FTE
Regulatory
Compliance
Increase
Capacity
Risk Reduction
Reduce the
variation in
the data.
Operational Loss
Avoidance
Reduction in
abandonment
rate
Employee
Engagement
Preliminary Timeline
Project Activities Time/ Date
Define: Project Pre-work, Project Scope, 3 Weeks
Measure: SIPOC
Value Stream Mapping
4 Weeks
Measure: Baseline Data Time, Cost, Quality
and Volume
4 Weeks
(concurrent)
Analyze: Root Cause Identified 5 – 8 Weeks
Improve: Solutions Selected 4 – 6 Weeks
Improve: Cost/Benefits Case 2 – 4 Weeks
Improve: Implementation Plan Drafted 2 Weeks
Control: Benefits Reporting Started, Control
Plan
3 Weeks
Project Scope Document
Project Team
Process
Support
Project
Lead
Champion Process
Owner
MBB/
coach
Business
Experts
Team
Member
Aswathi
Nambiar
Team
Member
Shilpa Kaushik Team
Member
Anirudh
Khanna
Team
Member
Project
Support
Analytical William
Davidson
Complianc
e/Legal
Tina Kerry IT Alice Kepler Finance Deb Glass.

9. Measure
96/8/2018

10. SIPOC
106/8/2018
S
Supplier
I
Input
P
Process
O
Output
C
Customer
Customer Customer Call during
office hours
Customer Data
Collection via hotline.
Customer
Information gathered
Onboarding system
Customer Screenshot capturing
systems
Customer Data
Collection via Web.
Customer information
gathered
Onboarding system
Onboarding system Screenshots from
Website.
Unable to onboard
customer within 5
days
Deletion of
screenshots and
Customer
abandonment
Customer
Auto Voice mail
System
Call from customer Customer calling
after office hours.
Auto Voice mail to
customer.
Customer
System storing
outbound calling
queue.
Outbound calling
queue
Onboarding specialist
calling customer after
5 business days.
Customer information
gathered.
Reasons for
abandonment
Onboarding system Customer data Monthly data
verification by
Supervisor
Entries in Quality
Control(QC) Database
QC Database

11. Current State Maps
116/8/2018

12. Current State Maps
12
 The style map chosen is Swim lane as it helps to clearly visualize
capabilities, roles, and responsibilities for each sub-process in the
business flow.
 Key elements of the process:
 On-Boarding Specialist : Manual data entry to On boarding system
and send welcome mail to the Customers.
 Auto-voice System : To perform automated periodic tasks.
 Supervisor : Responsible for quality check of cases logged in OBS
database.
 Human-to-Human interaction : Customer interacts with OBS to
bring about the onboarding process.
 Database-to-Database interaction : Cases saved into OBS moved to
outbound call queue.
 Key issues observed :
 Time not utilized when the customer data is not filled within 2
business days.
 No process to track whether the customer calls back via
onboarding with hotline.
6/8/2018

13. Time ,Cost and Volume Metrics
13
Year Average
Onboarding
Turn Time
(Days)
Cost of
labor/hour for
Onboarding
Average Hold
Time in
minutes
(Quality)
Attempted
Onboarding
Volume
2013 8 $25.95 1 27420
2012 25 $25.95 14 15564
2011 19 $25.95 8 14376
6/8/2018

14. Data Collection Plan
14
• Data collection plan depicts what would we want to measure at
various underlying processes in the system.
• The focus of data collection is to gather data that helps to improve
abandonment rate, as well as uncovering any factors that provide
clues about how, when, where, we have incurred issues in customer
onboarding.
• It would enable us to identify extra effort, time and resource utilized,
in addition to increase in abandonment rates.
• Data Collection Plan is attached as follows-
6/8/2018

15. Data Analysis – Reasons for more waste & low abandonment rate
156/8/2018

16. 16
 The Pareto chart shows that about 80% of the contribution to the
obstacles to achieving reduction in waste and lowering abandonment
rate is by manual handoffs, missing sales data and by the customer
having incorrect versions of the applications.
 These three reasons constitute 20% of the total number of reasons
(15), which is the consistent with the concept of a Pareto chart.
 Requiring prints of images of customer info and poor communication
with other departments are not very significant factors of the
obstacles.
6/8/2018
Data Analysis – Reasons for more waste & low abandonment rate

17. 176/8/2018
Data Analysis – Onboarding turn time by region

18. 18
 The Pareto chart shows that the turn time for on-boarded accounts
is the highest for Tucson South and Buffalo East. Therefore, we need
to focus more on these two regions while attempting to lower the
turn time.
6/8/2018
Data Analysis – Onboarding turn time by region

19. 196/8/2018
Data Analysis – Abandoned accounts turn time by region

20. 20
 The Pareto chart shows that the turn time for abandoned accounts is
the highest for Honolulu followed by Tucson North. Therefore, we
need to focus more on these two regions while attempting to lower
the turn time.
6/8/2018
Data Analysis – Abandoned accounts turn time by region

21. Data Analysis - Regional comparison of on-boarded rates
21
 The above chart shows that in 2011 and 2012, Buffalo East had the
highest on-boarded volume. However, in 2013, Tucson South picked
up and had the highest on-boarded volume.
6/8/2018
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Buffalo East Buffalo West Tuscon South Tuscon North Honolulu
Annual attempted onboarded volume
2011 2012 2013

22. 22
 The above chart shows that in 2008 and 2009, Buffalo East had the
highest percentage of applications. However, in 2010, the percentage
of applications was highest in Tucson South. The percentage of
applications saw a slight decline throughout the three years in the
case of Buffalo, while Tucson and Honolulu saw an increase each
year.
6/8/2018
Data Analysis - Regional comparison of Application %
0%
5%
10%
15%
20%
25%
30%
35%
40%
Buffalo East Buffalo West Tuscon South Tuscon North Honolulu
Application %
2008 2009 2010

23. 23
 The above graph shows that Tucson South region had the highest
application turn time in 2009.
6/8/2018
Data Analysis - Regional comparison of Application Turn Time
0
2
4
6
8
10
12
14
16
Buffalo East Buffalo West Tuscon South Tuscon North Honolulu
Application turn time 2009
Onboarded Accounts Abandoned Accounts Weighted Average

24. 24
 The above graph shows that March had the highest
customer abandonment rate.
6/8/2018
Data Analysis – Overall abandonment rate by month
0
5
10
15
20
25
30
October November December January February March April May June July August September
Average Customer Abandonment Rate

25. 256/8/2018
Data Analysis – Abandonment rate over time
0
10
20
30
40
50
60
October November December January February March April May June July August September
Abaondoment Rate over time
2011 2012 2013

26. 26
 As we can see in the graph, the abandonment rate in 2011 increases,
peeking in the month of May and then declining again.
 In 2012, the abandonment rate was higher than 2011 and increased
steadily, peeking in the month of February. After February, the
abandonment rate saw a steep decline until the month of June,
followed by a rise and another decline from July to September. In
2013, the abandonment rate was considerably lower than 2012, and
never increased or decreased significantly. Thus, we see the
abandonment rate is decreasing after 2012.
6/8/2018
Data Analysis – Abandonment rate over time

27. 27
 We can see that the abandonment volume is the highest in 2012
when the onboarding time is also the highest. The abandonment
volume is lowest in 2013 when the onboarding time is also the
lowest.
6/8/2018
Data Analysis – Abandonment rate & on-boarding turn time
0
5
10
15
20
25
30
35
40
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Abandonment rate and onboarding turn time
Average Abandonment volume Average Onboarding Turn Time in days

28. 28
 From the simple linear regression equation above, we can see that a
positive correlation exists between abandonment rate and turn time.
If abandonment rate is increased by 1 unit, the turn time will
increase by 0.4043 units.
6/8/2018
Data Analysis – Correlation between abandonment rate and
turn time

29. 296/8/2018
Data Analysis – Before Sigma for Turn Time

30. 30
 The turn time does not seem to be stable as there is a lot of
fluctuation. The mean of turn time is 8.07 and standard deviation is
2.608, and the only stability we see is that turn time ranges from
about 3 days to 15 days.
 Since management wanted the turn time to be around 2 days, the
system is not capable currently.
6/8/2018
Data Analysis – Stability and capability of Turn Time
0
2
4
6
8
10
12
14
16
TurnTime
Calls
Turn time before Six Sigma

31. Analyze
316/8/2018

32. Value-Added Analysis – As-Is Process Map
326/8/2018

33. VA/NVA Table
336/8/2018

34. 8 Wastes
34
Defects
Over
Production
Waiting
Non Used
Talent
Transport
Inventory
Motion
Extra
Processing
6/8/2018

35. Waste Management
35
 Defects
• Password generation procedure is not automated.
• Screenshots are being deleted within 5 business days without any backup.
• Absence of mechanism to track call back from customers.
• Saved unfinished cases are retained for 10 days and abandoned without informing
customer.
 Over Production
• Even after providing OBS direct contact information, abandonment rate of customer
is not improving.
 Waiting
• No OBS support available for customer queries after office hours.
• 2 days customer wait time for entering data into the system by OBS.
 Non Used Talent
• OBS is doing data entry operations along with onboarding .
6/8/2018

36. Waste Management
36
 Transport
• During web onboarding, data is manually copied from screenshots.
 Inventory
• Incomplete data entry after office hours is taken up next day.
• Unfinished onboarding are queued to outbound call only after 5 days
 Extra Processing
• Customer information captured in screenshots during web onboarding are being
entered manually by OBS.
6/8/2018

37. Fishbone Analysis
37
 The head of the fishbone analysis is the effect which is Increased
Abandonment Rate.
 The tail has 4 causes which are divided into
 People
 Process
 Higher Management
 System
6/8/2018

38. Fishbone Analysis
386/8/2018

39. Root Cause
39
Following root causes are identified using the Fishbone Analysis
 Lack of co-ordination between employees
 Lack of standard process and procedures
 Too many people involved in the process
 Cumbersome process and too many data entry screens
 Poor data management and documentation of process
6/8/2018

40. Improve
406/8/2018

41. Solution Selection Matrix
41
 As per the analysis performed, we have suggested some solutions for
the problem of incomplete Customer Onboarding, each of which has
been evaluated using 6 metrics, and scored considering Impact,
Cost, and Risk factors.
 Attached is the Solution Selection Matrix excel document:
 The solution with the maximum impact, minimum cost, minimum
risk, and that leads to high quality of customer service and
satisfaction, has been chosen at the end of the analysis.
6/8/2018

42. The Chosen Solution
42
 According to the Solution Selection Matrix, the winner of the problem is
implementation of sophisticated centralized Data Warehouse system,
aimed at customer onboarding process data.
 The Data Warehouse would take care of multiple issues like customer
information retention, missing value management, and data duplicity
issues.
 The solution would require skilled Data Analysts, DBA, and high-end
computer systems.
 This solution can be greatly complemented with the implementation of
the second ranked solution of advanced UI design, with lesser screens,
lesser mandatory fields, and use of drop-downs instead of text fields.
 Along with this implementation, there could be simultaneous low-cost,
high-value efforts of process improvements like hierarchy of
management, clear role assignments, and trigger of email reminders to
customers who haven’t completed the onboarding process.
6/8/2018

43. Future State Map
436/8/2018

44. Comparison of As-Is and Should-Be map
Customer Onboarding
specialist
Auto Voice
System
Supervisor Total
steps
Current
State
VE 0 2 0 2 4 VE
steps
VA 2 2 0 0 4 VA
steps
NVA 1 6 1 1 9 NVA
steps
Future
State
VE 1 4 0 3 8 VE
steps
VA 5 1 0 0 6 VA
steps
NVA 0 0 0 0 0 NVA
steps
This table shows the complete elimination of all nine non-value adding steps.

45. Cost-benefit analysis
Quantitative Benefits Qualitative Benefits
Lower costs, especially due to elimination of auto-
voice system
Improved company reputation
Less number of errors, since manual data entry
reduced significantly
Higher customer as well as employee
engagement
Lower customer abandonment rate Higher safety of sensitive customer information
Improved cycle time since web onboarding is
directly linked with the new data warehousing
system

46. Cost comparison
Implemented
solution
Previous process
cost
New process cost Cost changes
Data warehouse
solution combined
with Web onboarding
UI
$30 per 5000 sheets
= 0.6 cents per sheet
$30 per 10000
sheets = 0.3 cents
per sheet
Since no screenshot
printouts are
required, paper costs
are reduced
24/7 customer
onboarding hotline
$56 per account $25 per account Abandoned
onboarding cost per
account is reduced
Service Level
Agreement for each
onboarding step
$38.95 labor cost per
hour for service calls
$20 labor cost per
hour for service calls
Unfinished
onboarding are
reduced since the
risk of unfinished
onboarding is
identified at each
step, thus lowering
labor cost per hour
for service calls

47. Implementation Plan
476/8/2018
Task
ID
Action Steps
Respon
sibility
Support
Check
Date
Target
Date
Comments
1.1
Analyze redundant
information on the UI design
Alice
Kepler
Shilpa
Kaushik 21/10/201
8 24/10/2018
Perform in-depth analysis of the UI
and determine the important fields.
1.2
Design the UI with highly
relevant fields with lesser
screens, lesser mandatory
fields, and use of drop-downs
instead of text fields.
Alice
Kepler
Shilpa
Kaushik 04/11/201
8 11/11/2018 Design an user friendly interface.
1.3
Implement the UI design to
onboarding systems for direct
data usage
Alice
Kepler
Shilpa
Kaushik 11/11/201
8 30/11/2018
Implement the final UI design and
perform UI testing thoroughly
1.4
Perform various kinds of
Functional and Performance
testing on the final UI design
Alice
Kepler
Users,
DBA,
Testers
04/12/201
8 20/12/2018
User testing, Database, Load, and
Stress testing to be performed

48. Control
486/8/2018

49. Goal Statement
496/8/2018
 Average monthly abandonment rate needs to be
reduced to a maximum of 7% within the next 12
months

50. Goal Statement
506/8/2018
 Average monthly abandonment rate needs to be
reduced to a maximum of 7% within the next 12
months

51. Hypothesis Testing
516/8/2018
We tried to verify how random are the results using a t-test for
observations before and after the lean six sigma changes
Null-Hypothesis H0 : µ >= 7
Alternative Hypothesis Ha : µ < 7
Significance Level : 5%

52. Hypotheses Testing
526/8/2018

53. Hypothesis Testing
536/8/2018
We reject the null hypothesis that the observed average
abandonment rate after lean sigma changes is greater than 7% as
the p-value (8.33203610040462E-12) from the t-Test is more than
the significance level (.05)
Therefore, we conclude that there is enough evidence to claim that
the average monthly abandonment range has been reduced to a
maximum of 7%.

54. Control Charts
546/8/2018
Failure Point or
Risk Prevention
Check Point or
Trigger Corrective Action
Process
Owner
Database downtime
might interfere with
response time to
customers.
Scheduling database
backup at regular
intervals.
Trigger when customer
is unable to access web
onboarding website.
Schedule database
maintenance when
customer onboarding load
is significantly low
Onboarding
Specialist
Few onboarding
specialist at high
traffic times
Analyse the timperiods
where the number of
customer calling is high
High customer call
Volume
Increase in the number of
onboarding specialists for
that particular time period,
so that all the customers
are attended
Onboarding
Specialist
Customers' voicemails
get unnoticed
Each voicemail should be
assigned a tracking
number/issue number so
as to make sure they are
attended by a specialist.
Voicemail tracking
number is open and
not being
resolved/closed
Send periodic notifications
to the assigned onboarding
specialist to close the issue
in a week.
Onboarding
Specialist
Inability to upload
user data forms
automatically
Form data should be
uploaded by one
onboarding specialist for
a particular customer to
handle concurrency
Trigger to revert to
original state in case of
failure.
Customer form gets locked
in order to avoid
concurrent updation
Automated
Job
Significant increase in
incomplete
onboardings
Assign each incomplete
onboarding to a
onboarding specialist ,
which needs to be
completed in stipulated
time
Increase in the number
of customers in the
outbound call queue
Analyse the factors and
segment them logically to
find out the key reasons
,which are leading to an
increase in customers in
outbound queue
Onboarding
Specialist

55. Future Project Ideas
556/8/2018
 Future Project Suggestions
• Online Chat Onboarding: Extend the service as an
online chat to help reduce the number of callers and thus
improving the efficiency.
• Customer Data Analytics: Perform analysis on the
customers and identify their type and then sell
customized software as per their needs and preferences.
• Customer Rewards Program: Provide added benefits to
existing customers to retain customers. Retaining existing
customers is as important as gaining new ones so
measures must be taken to take care of that.

56. Future Project Ideas
566/8/2018
• Internal Best Practice Sharing Portal: Best practice that
enhance the business value should be shared amongst the other
team members. Proper training should be provided while
onboarding to share these best practices with them.

57. Acknowledgments
576/8/2018
Dear Professor,
This project has helped us a team in understanding the context of
process improvement and the tools and techniques to help achieve
the same. We appreciate your effort in designing this course and
guiding us all the way.
THANK YOU !