The talk describes how service operations in the insurance industry can be optimized using Process Wind Tunnel. Process Wind Tunnel utilizes novel process analytics, process mining, simulation and targeted automation to improve business processes. It also describes a real-world application of process mining to diagnose complex business process and gain insights. This talk was delivered at the Process Mining Camp 2020 and is available on Youtube (https://www.youtube.com/watch?v=ujEoPiuo9As)

1. Optimization of Service Operations using
Process Wind Tunnel
Sudhendu Rai
FP

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▪ Wind Tunnel Project Vision
– Process wind tunnel approach utilizes data analytics, visualization, process mining and discrete-event
simulation optimization for improving a large class of business processes. It delivers far superior
measurable improvements compared to traditional approaches.
▪ Data Collection
– Manual collection: Performed time and motion study where no IT data available
– IT data collection: Obtained data directly from different databases using SQL queries
▪ Current State Analysis
– Data was wrangled and exploratory statistical analysis, visualization and process mining tools &
methods are developed and utilized to gain business & operational insights and establish quantitative
metrics
▪ Future State Design
– Discrete-event simulation model was developed and model parameters are tuned to match baseline
metrics. The model was utilized to perform what-if scenario analysis and optimization studies to develop
future state
▪ Implementation
– In partnership with the operations team, process changes were implemented. Significant productivity
improvements were observed and the changes were implemented on other related processes.
▪ Further impact
– After the implementation, the business saw nearly 50% decrease in turnaround time despite nearly 20%
increase in volume. This also had significant positive ripple effects on downstream processes resulting
in end-to-end reduction in TAT and improvements in productivity.
2
Executive Summary

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A virtual modeling and analysis framework and toolkit/platform to evaluate and optimize
process structure and parameters using real-world data prior to committing to final process
design
Process Wind Tunnel : Data Science + Operations Research
Data
• IT Process Data
• Process Maps &
Business Rule
• Other Operational Data:
facility, employee, system
data and etc.
Discovery of
Process Insights
• Process Mining
• Exploratory Data Analysis
• Data Wrangling
Optimized Process
Design
• Current State Simulation
Model
• Simulation Optimization
Implementation
• Pilots
• System Changes
• New Tools/Assistants
• Change Management
• Process Monitoring
3
Data Collection Current State Analysis Future State Design Implementation

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▪ The primary process within Service Operations that was optimized using the Wind Tunnel approach
was related to taking customer input (paper/digital) and extracting relevant information and sending it
to the downstream service processing department
▪ Approximately 60,000 documents are received and processed each month
▪ Client requests are received in multiple formats – USPS mail, Overnight mail, faxes and emails that
arrive at different times during the day
▪ The operational staff are based in two sites – one domestic and one offshore location
▪ They are responsible for receiving and and processing the documents. The indexed documents are
delivered for further processing to downstream In-force management operations also located in a two
separate locations – one domestic and the other offshore
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Service Operations: Overview

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• For the first three steps of the process
(sort, log, prep) where no IT system
data was being captured, we
conducted time and motion study to
estimate process parameters
5
From the process map and on-site process observations, the process was abstracted using
five key process steps
Manual Data Collection
• For the latter 2 steps, we obtained data
directly from the underlying IT business
applications
IT Data Acquisition
Process Data Acquisition and Wrangling
Data Collection Current State Analysis Future State Design Implementation
Sort
Prep
Scan
USPS
Overnight
Fax
Log
Index

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In-depth Current State Analysis
Exploratory statistical analysis, visualization and process mining tools & methods are developed and
utilized to gain business & operational insights and establish quantitative metrics
Data Collection Current State Analysis Future State Design Implementation

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Model-based prediction and performance optimization to develop process change
recommendations
Build, validate the discrete-event simulation model
and utilize it for multiple scenario analysis and
optimization
Simulation results for improved future state are
compared with current state baseline scenario
Data-Driven Discrete-Event Simulation Models
The optimized simulation model predicted that by optimizing different processing and move batch-sizes and
priorities associated with various processing steps, the turnaround time can be lowered by nearly 50% without
decreasing the number of operators
Data Collection Current State Analysis Future State Design Implementation
Model Output Baseline Data

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The operations team implemented wind tunnel team recommendations and experienced a
very significant improvement in turnaround time and throughput
After implementing wind tunnel recommendations, work
gets completed and delivered to downstream processes
significantly earlier in the day
Despite the increase in volume, the average TAT
decreased by over 50%, as predicted by the model, and
the throughput also increased as a result of our work
Implementation and Impact
The implementation results validated our model predictions and showed significant improvements in operating
performance and productivity
Data Collection Current State Analysis Future State Design Implementation
April
(Pre-WT)
October
(Post-WT)
Model
Prediction
% Work Done
Same Day
95.4% 99.5% 98.9%
Avg TAT (h) 6.5 2.9 2.8
90% TAT 9.0 4.5 6.0
30% increase in volume since
implementation of Wind Tunnel
initiatives (in red)
Throughput increased by 37%
from an average of 21.5 to 29.5
post Wind Tunnel initiatives

9. Process Mining to Gain Insights into
Downstream Service Operations
Sudhendu Rai
FP

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1. Large number of process variants
2. Assignment vs. selection of work items
3. Impact of handoffs from offshore to onshore for downstream operations
4. Impact of handoffs within sites
5. Queueing delay between upstream and downstream operations
6. Rework queueing delay in offshore and onshore
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Insights from Process Mining of the Downstream Service Process
Operations
Upstream Operations Downstream Operations
Onshore Offshore Onshore Offshore

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A process variant is a unique path from the beginning to the end of the process
Variant Example 1
1. Large number of process variants
Variant Example 2 Variant Example 3
Onshore has 255 process variants, whereas Offshore has 646 process variants

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Task assignment is based on a “pull” model in Offshore whereas Onshore utilizes a “push”
model coupled with Round-Robin assignment
2. Assignment vs. Selection of work items
In Onshore, 85% of the work is assigned to operator, and 15% of the work is selected by operator.
In Offshore, this number is reversed.
85% Assign
15% Select
85% Select
15% Assign
244
8.7 hrs.
2,861
10.5 mins.
2,540
81.8 mins.
84
11.2 hrs.
899
19.6 hrs.
1,219
52.9 mins.
777
3 hrs.
1,291
6.7 mins.
9,294
7.4 mins.
Offshore Only
Onshore Only
8,872
17.5 sec.
❶
❷
❸
❶
❷
❸

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Scenarios
Percentage
of Cases
Mean TAT –
Business Hour
(Quality Steps
Included)
Mean TAT –
Business Hour
(Quality Steps
Excluded)
All cases 100% 6.4 4.7
Onshore
Only
18% 5 4.1
Offshore
Only
60% 5.6 4.2
Offshore to
Onshore
20% 10.5 8.8
Onshore to
Offshore
~1% 10
Too few
cases
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3. Impact of handoffs from Offshore to Onshore
Service requests where work is handed from Offshore site to Onshore site experience
significantly higher TAT
1%
20%
Offshore
14,609
Onshore
7,102

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Services requests with handoffs between personnel at a given site are seen to experience
significantly higher TAT
Onshore Only
▪ TAT: 4.2 hours
– Without handoffs: 0.8 hours
– With handoffs: 26.6 hours
Offshore Only
4. Impact of handoffs within sites
▪ TAT: 4.1 hours
– Without handoffs: 2.1 hours
– With handoffs: 20.7 hours
Handoff within
site between
operators
For the same
request, there could
be multiple
handoffs within site

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5. Queueing delay between Upstream and Downstream
Operations
Offshore Path: 6.8
hours queueing
delay after DMC
Onshore Path:
5.9 hours
queueing delay
after DMC
Upstream Operations
Downstream
Operations

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6. Rework/transfer queueing delay in Offshore and Onshore
The lag from “Assign to Lock” (Onshore) is significantly longer than the lag from
“Select to Lock” (Offshore)
❶
❶
❷
❸
6.4 hrs.
6,351
3.3 hrs.
5,656
39 sec.
13,321
58.8 min.
9,228
Offshor
e
Onshore
Significant additional time
is lost when put back into
the work queue for
reassignment or selection

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▪ Process Wind Tunnel methodology was utilized to improve a customer service
operations with onshore and offshore teams. It resulted in over 50%
improvement in cycle times even when incoming volumes increased by 20%.
▪ Process mining was utilized to gain insights into service operations for the
downstream processes. These insights led to concrete action items on where
and how to improve these processes.
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Conclusions

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Backup Slides

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Process Mining of Kofax Data

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▪ AnyLogic simulation software was utilized to develop the model
▪ Schematic discrete event simulation model showing different inputs and
activities
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Discrete-Event Simulation Modeling Overview

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Model Inputs
For batching scenarios, we choose the following batch size parameters

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Processing rates, empirical input distributions and staffing schedules description
Model Inputs
Scanning Distribution (sec) Indexing Distribution (sec)
Current State Schedule Proposed Schedule
Onshore Offshore
Time # FTE Time # FTE
7 – 8 3 8 – 9 3
8 – 16 7 9 – 16:30 6
16 – 17 4 16:30 – 17:30 3
Onshore Offshore
Time
Team 1
# FTE
Team 2
# FTE
Time # FTE
7 – 8 2 1 8 – 9 3
8 – 16 2 5 9:00 – 16:30 6
16 – 17 2 2 16:30 – 17:30 3

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Current
state
Baseline
model
Push w.o
batching
Pull w.o
batching
Pull w/
batching
Pull with
7AM Arrival*
% Done
Same Day
72.0% 79.0% 94.1% 99.6% 99.3% 99.3%
Average TAT 5.7 hr 5.2 hr 8.8 hr 4 hr 3.1 hr 2.9 hr
90% TAT 16.3 hr 8.3 hr 22.7 hr 6.7 hr 5.8 hr 5.8 hr
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All statistics below are calculated using all four Monday data from 4/3/2018 to 4/30/2018
Model results with current staffing (9 onshore FTE and 6 offshore FTE)
9A 6M 8A 6M 7A 6M 6A 6M 7A 5M 7A 4M
% Work Done
Same Day
99.3% 99.3% 99.3% 98.6% 98.2% 78.8%
Average TAT 3.1 hr 3.2 hr 3.3 hr 4 hr 4.4 hr 14.1 hr
90% TAT 5.8 hr 6 hr 6.1 hr 7.1 hr 7.5 hr 27.0 hr
Pull model with batching, with reduced staffing (A: Onshore, M: Offshore)
Model Summary Statistics for Mondays
*Note: For 7AM Arrivals, the Offshore Team Schedule is now shifted one hour earlier.
(i.e. For Offshore Team, instead of working 8AM to 5:30PM, they now work from 7AM to 4:30PM.)