WHITE PAPER

EXL’s “RETURN FROMHOME” SIMULATOR

Gaurav Iyer

VP and Head of Advanced Digital Solutions

Saurabh KhannaVP and Head of Embedded Analytics

Anuj GoyalSenior Manager Embedded Analytics

MayankManager Embedded Analytics

Ashwani SachdevaSenior ManagerEmbedded Analytics

lookdeeper@exlservice.com

July 8, 2020

Written by

Contributors

Analytics -powered tool to understand the impact of multiple covid infectionscenarios on your operations

EXLSERVICE.COM 1

Why do we need an Analytics-powered “Return from home” simulator?

As we move into the post lockdown phase of Covid-19,

business leaders need to plan for percent of sta� that

will “work from o�ice” given the possibility of “Herd

Infection” over the next few months. They need to make

that decision while balancing 3 objectives:

In these turbulent times, most business leaders continue

to rely on heuristic information and government

guidelines that have the following drawbacks:

What is the correct approach and more importantly, what are some of the guiding principles to make the “return from home” (RFH) decision?

We, at EXL, strongly believe that the RFH decision needs

to be based on an analytics-driven framework which can

be customized for various situations and di�erent

management objectives. An internal EXL taskforce built

an easy to use SIMULATOR within 2 weeks to empower

business leaders to -

What is Return from Home Simulator?

Our Analytics framework has an easy to use simulation

generator which empowers business leaders to get a

view of future business productivity by tweaking

multiple input parameters. The simulator leverages the

SIR (Susceptible-Infected-Recovered) methodology and

has the following key input parameters:

Protect their employees

They do not help plan for “herd infection”

scenarios

Government guidelines on o�ice occupancy

do not account for floor, district, client and city

level variations

Inability to quantify sta� availability risks in

discussions with clients and employees

Inability to assess the impact of varying center

and sta� quarantine durations

Minimal disruption to customer service

Do the right thing in the communities that they are a part of

Simulate recurring infection waves and

resulting impact on sta� availability

Understand impact of critical controllable

parameters on floor availability:

WFH : O�ice ratio

Maximum allowed capacity on floor

Quarantine period

Develop custom guidelines for each location

based on above parameters

Enable on-going monitoring to react quickly in

case of new infection waves

EXLSERVICE.COM 2

Base infection rate in city where o�ice is located

O�ice configuration / design

Number of employees working from home vs. o�ice (%)

Number of employees allowed on the floor

Days that o�ice or floor is shut-down a�er infection(s) is identified

Infection characteristics

Length of time for employee recovery (#days)

Length of time in quarantine (#days)

Secondary Infection rate i.e. number of people that 1 infected person impacts

Future Infection waves (that could vary in number, intensity and duration)The Simulator allows leaders to view the impact on “Sta� productivity” as an output, with varying combinations of infection rates, waves, quarantine periods, and home to o�ice strength

What are some of the key insights?

Not all input parameters impact sta� productivity equally. Interestingly, changes to the base infection rate of the district or

city or the length of time that an infected employee takes to recover, have the least impact on sta� productivity.

The 3 parameters that have the biggest impact on productivity are:

% of Sta� working in o�ice vs.

% of Sta� working at home

Floor size and occupation density

Time-window for the floor or center to be quarantined in

case of an employee with infection or an employee with

suspected infection

CLICK HERE TO TEST THE SIMULATOR PROTOTYPE

EXLSERVICE.COM 3

Also refer below charts for understanding:

Sensitivity of various input parameters (Chart 1), and Output representation (Chart 2)

Base Availability 94%90%

92%

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89% 91% 93% 95% 97% 99%

Availability

Base Values Le� Base Right

Work from home % 30% 50% 70%

Workspace/ Floor 400 250 100

Quarantined Days 7 5 3

Recovery Time 40 30 20

40 20 10 per million per million per millionInfection Rate

Chart 1: Parameters Sensitivity

Chart 2: Output

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0.0001000

0.0002000

0.0003000

0.0004000

0.0005000

0.0006000

0%10%20%30%40%50%60%70%80%90%

100%

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

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What are some of the key actions that management can take?

1. No more than 30% sta� in o�ice for the next 3 months

2. POD style compartmentalized floor plan will allow lesser number of people on a floor at any point of time

3. Equip sta� to be able to work from HOME and OFFICE to limit impact of floor quarantine / closure i.e. when floor is

quarantined then teams can continue to work from their homes

4. Split operational teams into Team A and Team B to reduce productivity loss during quarantine window(s)

5. Lower the floor quarantine period by adopting quick sanitization and deep cleansing capabilities

InfectedQuarantinedProductive Daily Infected Rate

5

EXLSERVICE.COM

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