1. Shashank Pawnarkar, TCS
Leveraging Human Capacity Planning for Efficient Infrastructure Provisioning
Shashank Pawnarkar, TCS

2. Contents Solution Overview Earlier Process Solution Details Case Study
Business Benefits
Future Roadmap

3. Capacity Planning for Infrastructure Provisioning - Overview
Benefits
Human Capacity Planning
Allocation through Scientific Approach
Provide Flexibility of schedule modification and capability of What-if Analysis
Automates Schedule and Work Load Generation Process
Number of resources
Skills
SLAs
Volume
Demand Forecasting
Peak Load - Year
Forecast of Volume
Demand Pattern
Peak Load - Month
Peak Load - Day
Infrastructure Capacity Planning
Server Sizing – RAM, CPU, Data size, Number of Machines
Peak Load
Number of Users
Transactions Volume

4. Capacity Planning – Earlier Process
Earlier Scenario – Manual Capacity Planning
Create Historical Volume Data
Run Macros/Apply
Formulae using Excel
Analyze & Manipulate Data to generate Roster
Challenges
Substantial Efforts/ Time needed to Create
Resource Allocation Plan
Not Flexible to do What-If Analysis
Requires Specialized Scheduling Skills
Manual Creation of Historical Volume Data is time consuming

5. TCS Capacity Planning Solutions Overview
Inputs Parameters
Model Development
Data Preparation
Reports
Data Cleaning
Data Cleansing
Data Transformation
Varying SLA
Overlapping Shifts
Cross Skilled Agents
Develop Forecasting and Scheduling Algorithms
Develop Model
Schedule Generation anytime during
the month
Shift Planning with
Named Resource
Solution Overview
Data Collection
Real Time Queue Monitoring
Workflow Engine
Workflows for defining Tasks
Historical Data 1
Skill wise AHT
SLA / TAT
Shifts 2 3 4 5
Client Forecast 6
Resource CAP 7 8
Shift Constraints
Shrinkage
Real Time Dashboard
Real Time Dashboard for Monitoring Associate Activities
Surplus Reports
Deficit
Work Allocation
Rule Based

6. TCS Capacity Planning Solutions Schematic
High Level Solution Schematic & Input Parameters
Data Preparation
Model Development
Model Validation
Model Deployment
Real Time Reports
Closed Loop
Forecasting Parameters
Optimization Parameters
Governance Parameters
Volume Type (Count/Volume)
TAT/SLA
Industry Vertical
QC %
Working Hours in a Day
Customer / Client
AIC / BIC / MAPE Threshold
Utilization
Tower
Cross Skilling
Process / Unit
Attrition Rate
Client Group
Shrinkage / Learning Curve Time
WON
QC Time
Project Phase (Transition / BAU)
Resource Cap
Latency

7. TCS Capacity Planning Solutions Overview
Screen: Historical Data Upload
All process details are captured
Ability to upload Historical Data
Screen: Forecasting Models
User has ability to select Model

8. TCS Capacity Planning Solutions Overview
Screen: Output – Best Fit Model
Best Fit Model Selection Parameters
Graphs for various Models
Screen: Forecasting Models
Single Exponential Smoothing
Double Exponential Smoothing
Triple Exponential Smoothing

9. Case Study – Background and Context
The Customer is among top logistics company in the world. TCS caters for 10+ countries for Data Entry Process. The business has lot seasonality impact due to which the staffing (users) requirements keep on varying and it is imperative for TCS to plan and roster accordingly so that we do not carry excess or low staff than required. This criticality is compounded by a metric named SLA cut-off, there are 150+ SLA cut-offs across the day that needs to be adhered to.
Problem Statement
Since the inception of the project, there was no method/process/tool/capability to identify the arrival patterns & do volume forecasting and strict SLA cut off time. This led to over or under utilization of Infrastructure Capacity.

10. Case Study – Challenges
Goal Statement
To ensure right staffing and optimal rostering such that SLA cut off compliance improves from 84.6 % to 99.7 % by March 2016
Criticality
Not meeting cut-off has lead to penalty imposed on TCS and potential loss of business/contract from client
Challenges
To Manual Workforce Management, requires extensive data gathering and manipulation.
Substantial Efforts/ Time to Create Resource Allocation Plan
Extremely slow response of application when number of users increases affecting productivity and SLAs

11. Case Study – Process Characteristics
Destination
(If applicable)
Sub-Origin
Country of Origin
Transaction
Sub-Type
Type
Process
Data Entry
Dutiable
Auto
Country
1..13
Service Station
Destination
Manual
Non-Dutiable
Every Country will have one or more Service Stations and Destinations if applicable
Each Transaction Type has different AHT (Average Handling Time)
Transaction to be completed before Service Station/Destination Cut-Off (SLA)
Associates are Cross Skilled across transaction cluster types
Associate utilization should be 90% during the shift
Overall 165 Cut-Offs to be managed round the clock (24 / 7)
Process Characteristics

12. Case Study – Capacity Planning - Output
Human Capacity Planning
Infrastructure Capacity Planning
Date
, 6:00 PM to 9:00 PM IST
, 8:00 AM to 8:30 AM IST
Number of Users
100
104
Transaction Volume
20000
30000
Number of CPU required
4 Core
8 Core
Number of RAM required
32 GB
64 GB

13. Case Study – Capacity Planning Benefits also drives Infrastructure Optimization
99.7%
SLA Compliance $1 MM
FTE Optimization
41%
Reduction
Employee Attrition
Before
84.6%
After
99.7%
Before
20 %
After
11.7 %
Before
220 FTE
After
164 FTE
Infra Planning & Optimization driven thro Capacity Planning yields compelling impact

14. Case Study – Customer Testimonial
Client Testimonial
VP Global BPO of Leading Global Logistics Major had participated in Solution Design and appreciated the efforts put in by TCS team
… Ensured Active Participation of Client in Solution Realization
TCS Confidential

15. Case Study – Conclusion
For Effective Infrastructure provisioning, the input from business environment are critical
Past Business Data are leveraged using advanced analytics models.
Hardware resources are gainfully managed at the minute level granularity.