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

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

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

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

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

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

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

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

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.

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

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 1..250 Destination 1..2000 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

Case Study – Capacity Planning - Output Human Capacity Planning Infrastructure Capacity Planning Date 2016-07-21, 6:00 PM to 9:00 PM IST 2016-07-22, 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

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

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

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.