1. Improving Claims Processing
PayUp In$urance Company
TEAM 5

2. Description:
1
Project Description
• Reduce customer complaints due to delays in
claims processing time.
• Reduce cost of processing claims.
Scope (In/Out)
IN SCOPE
• Process/work
flow
• Work schedules
• Staffing
Key Benefits (including financials)
• Reduce Customer Complaints due to claims processing time by 50% by December 2016
• Reduce operating cost of claims processing by 25% by December 2016
Key Interdependencies
• Claims Processors
• Claims Adjustors
• Claims Account Payables
• IT
• Finance
• Customer Service
Critical Assumptions and Risks
• VP of Claims is Project Sponsor
• Assume the call received quality is 95%
• Assumed inventory zero at task one
• Distance traveled for tasks 2-5 are zero
• Quality level 100% for task 3
• Adjustor inspecting and entering data the quality was
changed to 71%
• Wait time of 5 minutes from receiving the call to
transferring and entering the data
• 5 minutes to process the mail for delivery
• The 4 week period from customer receiving the check
and cashing the check
Project Charter- Improving Claims Processing
OUT of SCOPE
• Capital expenditures

3. 2
Role Name and/or Number of Resources
Leadership Team Sponsor VP Claims
Management Team Owner Claims Processing Manager
Transformation Lead OPEX
Category Leads Data Entry, Claims Payables, Claims Adjustors
Others… Customer Service Representative, IT Support
Program Team Structure
DRAFT – FOR DISCUSSION
Core Team
Extended Team
Role Name and/or Number of Resources
IT Leads Software Application
CSR Leads Regional
Others…. Finance

4. PayUp’s Current State Map
3

5. PayUp Calculations
Calculating Lead Time
Demand = 200 Claims
Time = 7.5 Hours
= 450 Min.
Takt Time
= 450/200
= 2.25 Min/Claim
= 135 Sec/Claim
4
Total Productive Time
Receive Call 15 Min.
Enter Data 7 Min.
Adjustor calls Customer
to verify data 20 Min.
Adjustor inspects damage
& enter data 65 Min.
Process & Mail Check 5 Min.
Total 112 Min.
Total Lead Time
Receive Call 0 Min.
Enter Data 2070 Min.
Adjustor calls Customer to verify
data 11 Min.
Adjustor inspects damage &
enter data 0 Min.
Process & Mail Check 900 Min.
Total 2981 Min.
Inventory
Receive Call 0 Cases
Enter Data 920 Cases
Adjustor calls Customer to
verify data 5 Cases
Adjustor inspects damage
& enter data 0 Cases
Process & Mail Check 400 Cases
Total 1325 Cases
Efficiency
Productive Time = 112 Min.
Total Time (Productive +
Lead) =
309
3.25 Min.
Efficiency = 0.04 Min.
Yield
Receive Call 0.95
Enter Data 0.5
Adjustor calls Customer to
verify data 0.89
Adjustor inspects damage &
enter data 0.71
Process & Mail Check 0.94
Yield = 0.28

6. Cost/Claim Summary
• Summary view of the data by region and product to identify
abnormalities in raw data
• This view tells us where to focus our efforts based on the average
cost/claim and the standard deviation
• Product A cost/claim is consistent across all regions while Product B
has a significant variance by region
• Product B in region 3, has the smallest cost/claim variance which
means it is the most consistent
5
Product
Region Data A B Grand Total
1 Average of Cost/claim 257.59 326.37 291.98
StdDev of Cost/claim2 5.88 13.97 36.33
2 Average of Cost/claim 259.07 327.49 293.28
StdDev of Cost/claim2 7.60 7.51 35.36
3 Average of Cost/claim 250.15 295.03 272.59
StdDev of Cost/claim2 6.35 3.01 23.20
Total Average of Cost/claim 255.60 316.30 285.95
Total StdDev of Cost/claim2 7.65 17.72 33.35

7. Graphic View of Data
• This graph showcases
the cost/claim per region
by product type
• Product A has an
average cost/claim is
consistent across all
three regions
• Product B average
cost/claim varies
significantly by region
6
Product B

8. Cost/Claim Trend Over Time
• This graph shows the
cost/claim of each product by
region over the last 25 weeks
• Product B cost/claim in regions
1&2 is increasing
• Product B cost/claim in region 3
is steady
• Product A cost/claim in regions
1&2 is increasing
• Product A cost/claim in region 3
is improving
• There are opportunities for
improvement in regions 1&2 for
both products A&B
7

9. Potential causes for cost/claim
• This graph shows the variables that may cause
an impact on the cost/claim
• We have determined that rework due to errors
is a major contributor to the cost/claim
8

10. Description:
9
Project Description
• Reduce customer complaints due to delays in claims
processing time.
• Reduce cost of processing claims.
• Focus efforts on rework due to errors as a major
contributor to the cost/claim.
Scope (In/Out)
IN SCOPE
• Process/work flow
• Work schedules
• Staffing
OUT of SCOPE
• Capital expenditures
Key Benefits (including financials)
• Reduce Customer Complaints due to claims processing time by 50% by December 2016
• Reduce operating cost of claims processing by 25% by December 2016
Key Interdependencies
• Claims Processors
• Claims Adjustors
• Claims Account Payables
• IT
• Finance
• Customer Service
Critical Assumptions and Risks
• VP of Claims is Project Sponsor
• Assume the call received quality is 95%
• Assumed inventory zero at task one
• Distance traveled for tasks 2-5 are zero
• Quality level 100% for task 3
• Adjustor inspecting and entering data the quality was changed to 71%
• Wait time of 5 minutes from receiving the call to transferring and
entering the data
• 5 minutes to process the mail for delivery
• The 4 week period from customer receiving the check and cashing the
check
Project Charter- Improving Claims Processing

11. Descriptive Statistics on
Cost/Claim
Ratio Experience #Claim Types Training Gender # Claims
Mean 25.37% 4.84 3.76 0.52 0.48 295.20
StdDev 14.29% 3.00 2.70 0.51 0.51 126.82
Min 6.00% 0.00 1.00 0.00 0.00 121.00
Max 54.29% 10.00 9.00 1.00 1.00 486.00
10
• This graph shows the variables that may cause an impact on the
cost/claim
• As shown above the more experience you have the more claim types you
have access to fix, and the total number of claims you resolve will
increase as well

12. Measurement Errors
• Data entry error
– Observation 17 had an extra digit that skewed the data
• Time/Cost to correct error has no data
• Training data is subjective and limited estimated to the last six months
– No specific data on the type of training received
• Data on experience is given on only in full year increments
– No indication on where the experience was gained
• Complexity of claim types unknown
• The claim completion percentage data is unknown
11

13. Benchmarking
• Internal Benchmarking
– Product B region 3 is our most consistent in cost/claim
– Product A, on average, is lower cost/claim than Product B
– Determine if we have other products and or regions that can be used
for benchmarking
– We will utilize these values to revaluate our project goals
• External Benchmarking
– Compare our product cost/claim by type and region to competitors
– Employee survey on best practices from previous employers
– Hire managerial consulting team to evaluate best practices across
similar industries
12

14. 13
1 086420
60.00%
50.00%
40.00%
30.00%
20.00%
1 0.00%
0.00%
Experience
Ratio
Scatterplot of Ratio vs Experience
• The more experience the employee has the fewer errors they make
• The Correlation Coefficient is -0.018
• This means that with each year of experience, the error ratio will decrease by
2%

15. 14
9876543210
60.00%
50.00%
40.00%
30.00%
20.00%
1 0.00%
0.00%
_Claim Types
Ratio
Scatterplot of Ratio vs _Claim Types
• The more types of claims the employee processes the fewer errors they make
• The Correlation Coefficient is -0.022
• With the greater variety of claim types, the error ratio will decrease by 2%
• This may be due to the fact that the more experienced employees are given the
more difficult and varied claim types
• Additional data is required to investigate further

16. 15
5004003002001 00
60.00%
50.00%
40.00%
30.00%
20.00%
1 0.00%
0.00%
_ Claims_1
Ratio
Scatterplot of Ratio vs _ Claims_1
• The more claims that the employee handles, the fewer errors they make
• The Correlation Coefficient is -0.00025
• With the greater number of claim types, the error ratio will decrease by 0.025%
• This may be due to the fact that the more experienced employees are given
more claims
• The correlation coefficient is near zero therefore, the relationship is not as
statistically significant as the other variables.

17. 16
• This chart depicts the relationship between error ratio, gender and training.
• From the chart, gender has minimal impact on error ratio.
• Training has an ≈6% reduction in error ratio.
• Training reduces variability ≈10%.
• Density equals number of employees per grouping.

18. SUMMARY OUTPUT
Regression Statistics
Multiple R 0.972796116
R Square 0.946332283
Adjusted R
Square 0.93559874
Standard Error 0.036267397
Observations 25
ANOVA
df SS MS F Significance F
Regression 4 0.463866817 0.115966704 88.16587837 2.07396E-12
Residual 20 0.026306482 0.001315324
Total 24 0.490173299
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% predictive y
Intercept 0.53922 0.020425519 26.39940911 5.09468E-17 0.496614735 0.581828505 0.496614735 0.581828505 y 0.53922
Experience -0.01773 0.004487416 -3.950826798 0.000789231 -0.027089587 -0.008368417 -0.027089587 -0.008368417 10 -0.17729
#Claim Types -0.02208 0.005799133 -3.807191021 0.001103845 -0.034175188 -0.009981628 -0.034175188 -0.009981628 5 -0.11039
Training -0.08027 0.01581556 -5.075241387 5.78282E-05 -0.113258466 -0.047277105 -0.113258466 -0.047277105 1 -0.08027
# Claims -0.00025 0.000123593 -2.055265672 0.053147542 -0.000511826 3.79402E-06 -0.000511826 3.79402E-06 400 -0.10161
0.06967
17
• Equation Y= 0.54 – 0.02 x Years of Experience – 0.02 x No. claim types – 0.08 x training –
0.00025 x No. of claims
Recommendation:
• Years of experience is a significant contributor to error reduction, therefore, hire
experienced employees.
• Provide training to all employees who have not received training.

19. Lean Six Sigma Elevator Pitch
“Not only is this something that we should do, I think it is
something that we must do, if we are going to stay competitive
and maximize our profitability and consistently deliver the best
quality products and services to our customers. The concept of
Lean and Six Sigma are applicable to all areas of the
organization, not just manufacturing. The tools and techniques
I am learning will help us to not only identify costly waste but
will also help us to deliver actual sustained improvements by
utilizing the DMAIC (Define, Measure, Analyze, Improve,
Control) methodology and lean/six sigma tools.”
- Team 5
18

20. Team Five Members
• Rory Donnerstag
• Julio Rivas
• Richard Singletary
• Michael Kenyon
• Farahnaz Sayani
• Cheng-yu Ouyang
• Amari Hanes
19