HIGH MATURITY IMPACT: PREDICTIVE PERFORMANCE LEADING TO PRODUCTIVE …conference.qaiglobalservices.com/HMBP-2013/pune/PDF/High... · 2013-10-17 · • These Ensured that the call

Shashank P & Leena S

at

HIGH MATURITY IMPACT:

PREDICTIVE PERFORMANCE

LEADING TO

PRODUCTIVE BUSINESS

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2 Copyright © Capgemini 2012. All Rights Reserved

Presentation Title | Date

About this paper

Like any other industry, IT outsourcing industry too has witnessed a Wave

Pattern.

In last 3 decades, we have seen two prominent waves so far and the third

is in making.

In this paper, we are highlighting the challenges posed by this new wave

of IT outsourcing; and how our QA team has catalyzed to successfully

overcome these; using High Maturity Processes

Also we are showcasing how these HM Processes are taken to next level

to improve not only service delivery but also the top-line growth of IT

Services delivery business.



Waves of IT Outsourcing

-Low Cost English

Speaking Indian Coders

-Currency Arbitrages –

key factor

-‘J-Curve’ Growth

Wave No 1 Wave No 2 Wave No 3

This wave dissipated with

changing economic

scenario: dotcom burst,

end of Y2K etc.

-No more ‘code factories’

-Offerings of Solutioning

capabilities

-Rigorous Quality Models

-Changing business

expectations

-Dynamic Strategies

-Fierce Competition

New set of challenges

arising out of current

economic equations

causing end of 2nd wave

Demands new

strategies for IT

Outsourcing business



Readiness to face challenges

Market challenges faced today:

Service Delivery costs increasing with booming economy

Dynamic economical situations

Need to chase moving targets

Increased expectations from clients

Price competitiveness etc.

Hence to sustain and grow in these challenges; business houses have to be

Productive – for Price Competitiveness

&

Predictive – Help client organizations to meet moving targets.



To Be Productive.. 3 R’s at CapGemini

Traditional

Lean, six–

sigma

methodology

REDUCE

Automation

Pyramid

First time right

REUSE

KEDB

Mutualization of

resources

Skill based ticket

routing

REINFORCE

FMEA / Problem

Management

Left Shift

Improve Application

code quality (CAST)

These 3 R’s

associated with a

BIG R RISK

Helped in 10% –

12% of effort and

cost reduction

15% - 20% of Performance

Improvement – CSAT up

Account Progression /

Growth: Potential of

avg 15 - 20% of Growth

in an account (y-y)

http://archian.files.wordpress.com/2011/09/sixsigma.jpg



Quick Wins

Automation

• Entire account and BU level data integrated

• Sophisticated dashboards displaying live status

• Also access to clients on spectrum of interface – PDA’s

Known Error database

• Rich and Growing KEDB

• Helping improve resolution time by 15% - 25%

Skill Based Routing

• Auto assignment of tickets based on historical data

• Set of skilled resources getting relevant tickets

Direct solutions with low /no risk and high impact on Service Delivery



Automation – Live Performance of Services Delivery

Portal based

Dashboards.

Real time

integration with

Ticketing tool.

Provides live

information on

service delivery.

Helps SDM to

pick up early

signals and

trends of critical

services

delivery

parameters.

Critical Threshold Tracking

In flight SLA

tracking, Response /

resolution. Probable

breaches, Capacity

Utilization, Alerts

Available on PDA’s

Ap

plic

atio

n w

ise

D

ata

APPLICATION HEALTH

Service Availability

Project data monitoring

Response

Resolution Priority

Efforts Severity

Ticket rate S

ervic

e

De

live

ry

ME

TA

DA

TA

Resources Skill

CR’s being Served Preventive

Efforts

Dashboard

DRILL-DOWN TO ACTION

Failure Trends Service Performance

Overview…

… to remediation

Portfolio

Applications

Modules

Tickets / CR

Org

baselines

comparison

Access to

root causes

Sr. MANAGEMENT VISIBILITY

Technology

Domain

Experience

Availability Real ti

me In

teg

rati

on

wit

h T

icketi

ng

To

ol

G:/Local Settings/Temporary Internet Files/OLK779/Documents and Settings/ssa/Local Settings/Temporary Internet Files/Local Settings/Temporary Internet Files/My Documents/Products/Flash Demo.ppt



KEDB – Known Error Database

KEDB Benefis

Rich Knowledge Base of known errors,

helping faster resolution

Portal based, indexed KB’s with

multiple tagging ensures ease of use

Less dependency on SME’s

Solution is readily available

Improved Team productivity

Resolution efforts (AET Ticketed) improved by

15% resulting in Annual savings of 600K €

Project A: control chart shows that there is a reduction in the

resolution efforts (AET value) for (May-June 2012) period as

compared to before (Jan-March 2012) .

Resolution efforts (AET ticketed)

Apr-Jun 2012 v/s September 2012 (After)

for 15 projects -

There is shift of mean in Resolution efforts (AET ticketed) after

deploying KEDB framework.



FMEA – Deployed in AM Engagement

• Reduction in call volumes to increase efficiency and better utilize account teams, FMEA technique

was deployed.

• Underwent a 16 week Lean Deployment covering Diagnostic of the processes followed like Problem

Management, Incident Management, Change Management.

• Dependency on users for issues’ investigation

• Lack of user response leading to tickets ‘On-Hold’

• High backlog volume leading to lack of focus

• No permanent fixes leading to recurrence of issues

• Incorrect prioritization of daily work

Findings of Diagnostics

• Usage of standardized work templates

• 3 strike rule with the customer

• Daily brain-storming in front of the physical White board

(via dSTUM)

• These Ensured that the call volumes are brought under

control.

How it was addressed

• The account used the FMA technique to reduce inflow. SIP process to generate ideas to automate number of manual processes.

• The problem management process has now inculcated A3 in it. Every Problem ticket has an A3 for it which is prepared by the

team, reviewed by the Problem Manager of the offshore and after getting the solution reviewed by onshore SME the A3 is

implemented. The A3s’ have encouraged the team to drive the root cause analysis. On an average there are 2 A3’s per month.

2011-2012 2012-2013 2010-2011 2009-2010

Result



Critical – High Risk / High Rewards Solutions

• Here High Maturity Processes came to rescue to address this trade-off

• Having the prediction model for meeting SLA’s, Response / Resolution and resource

availability helps Service Delivery Account to take informed and safe decisions on Resources

restructuring, prioritizing

Trade

off

Services Delivery Quality

SLA Breaches

Bad fixes

Improved Margins

Resource Availability and

Utilization

Pyramid

Restructure Mutualization



Being Predictive…

Predictive processes of High Maturity framework

helping projects implement critical revamping solutions



AM scenario: Prediction Model

Project Ambiguities

Optimum FTE V/s SLA

Compliance

Identifying Bottlenecks

Shift wise allocation

FTE Estimation &

Utilization

Dependency on Past

Performance

Rework / Redundant

work

Piloting new ideas risk free

Skill Availability

• All these ambiguities bear impact on end result of the

project – here in this case – Service Delivery

• The SDM deals with these on daily basis

• SDM needs a model where, by controlling these

unknown input parameters, desired end result is

achieved.

• Additionally SDM also needs to know to what degree

these input variables should be adjusted (process

composition / what –if’s)

• CMMi High Maturity process framework enabled us

to define a prediction model around all these input

parameters and their impact on end result.

• ‘ProcessModel’ tool was selected for prediction

purpose.

ProcessModel



Multi-dimensional Prediction

Sub-Processes

Staff Shift Timings

Shift-wise Resources #

Skill-wise Resource #

Ticket Inflow

KEDB Usage

On-Hold %

Time per Priority

Time per Skill Level

Input to ProcessModel

It helps visualize, analyze, improve and control the

business processes.

Process mapping tool with modeling and real life simulation features

inbuilt.

Critical Sub-Processes

Cycle Time

Hot - Spots



Case Study of HM Implementation – in a nut shell

AM projects make use of Prediction Models in showing quantitative improvements. PM’s can make use of this quantitative data for decision making.

The data in terms of ticket volume, Resolution efforts, shift and skill breakup, SLA targets, etc. is collected from projects to keep a tab on the estimation made by PM. The data is put into the model to predict SLA compliance

of available input parameters.

The PM can predict SLA Compliance, Backlog, Skill and Shift of FTE’s by mid course correction. i.e. at the middle of the month, he can use the actual data to predict for rest of the month, and hence know the failure

points if any.

There can also be few triggers which can cause overruns in output like high Breaches, High Backlog, etc. To bring things back on track, the PM feeds in actual project performance values for the given period and the model predicts the end results. Hence PM can take informed decisions. Eg. If the ticket inflow suddenly increases, then the PM might think of ramping up the resources for that month. But how many resources to ramp up, can be predicted by models thus helping PM in decision making.

• Triggers:

• Seasonal variation of ticket inflow and its impact

• Resource availability/Leave plans of critical resources

• Shift wise changes

• Resource skill related changes

8/15/2013 1



Case Study of HM Implementation – in a nut shell

Quality & Process Performance Objectives**

Achieve targeted Goals & Sustain SLA Compliances (various priorities)

Business Objectives

ADRC* max 56 € OAE* min 80%, CSAT 3.5

Business Vision

Cost Competitiveness Customer Alignment

* - CapGemini Specific Internal Indices - proprietary.

** - Detailed Quantified QPPO Sheet available.

Multiple Team Process Model Used

6 different teams from account configured.

Validated for its correctness with actual data

What-if analysis done to determine skill-wise

team composition. Optimum skill mix V/s SLA

ADRC is a cost index related to skill level spread in team

P2 SLA Compliance QPPO was optimized

in the Process model for the project.



Case Study .. Critical Sub Process Selection

Output generated from the process model is used for comparing the sub-processes with respect to

1. Non value added activities

2. Mean cycle time

3. Standard Deviation of cycle time.

Based on ticket volume and business criticality, the following sub process are selected as critical sub process.

Oracle P2 resolution

Nova P2 resolution

Oracle P3 analyse



Case Study: Output of Simulation – ProcessModel

Based on these simulated predictions,

the SDM takes a proactive /

Corrective Actions to bring the project

performance back on track.

Selected Sub-Process is analyzed and fine tuned so as to yield desired result.

Probable SLA Breaches / Mid-course

Correction

Resource availability – leave plans,

shifts, holidays

Optimum skill mix of resources

Seasonal ticket variation and inflow

Predicted Resource Utilization



Case Study .. Process Optimized

In course correction done, now target is to optimize process

Service Delivery Factory

Mutualization

Pyramid

Skill - Mix

Shift Pattern

On-Demand Dynamic Control of the factory

Improved & Predictable

Service Quality

SLA Compliance

Project Profitability

C-SAT / E-SAT

To enhance further, each of the controls calibrated for optimized service delivery results.

Process Model helping establishing respective predictive baselines



What are benefits to project

This process helps the projects in improving project performance by:

Controlling the Critical Sub processes listed in the Hot Spot by taking Corrective Actions.

Improving the Resource Utilization by taking Corrective actions (change of Shift, readjusting capacity etc) and ensure that SLA breaches are within control.

Reducing the SLA Breaches in the flow, by analyzing the relevant flow and the activities in that flow (Bottlenecks if any).

Working on process steps to improve the Performance, if needed modify the process steps and evaluate the Performance using simulation.

This ensures that the critical business objectives are met. The clients do have a better understanding and predictability about the support engagement and help them dynamically strategize their business.



Was it a cake-walk?

This is a major organizational change management exercise

• Finding additional bandwidth from pre-occupied delivery folks

• Getting a buy-in from delivery community

Since HM is purely data driven, major challenge is

• Correctness and timely availability of relevant data

• Fast and accurate mechanism for data collection

Major Innovations are already happening in many projects

• Since these initiatives are reaping benefits to engagements

• But due to lack of qualification, these cannot be fitted in CMMi HM framework

Change Mgt

Data

Collection

Data Quality

Optimization

Opportunities

Non-

Statistical

Initiatives

Statistical expectations of QWM anticipates

• Granular sub-process-wise data

• Identification of sub-processes, knowledge of statistics in practitioners

The basic business DNA of CapGemini is Services Delivery

• Hence many AM engagements are steady state

• Identifying further optimization needs a microscopic view at projects



Bottom-line improved, now what next?

Bottom-Line Improved.

• HM Practices helped improve Service Delivery account’s bottom line

• Projects are more Productive, Predictive and Profitable with the help of high maturity practices.

Taking to next level

• It was time to take these to next level

• Targeting the business development activity – making more price competent and aggressive business propositions.

Aggressive Pricing

• Professional study revealed that Competitive markets necessitating a need for more aggressive pricing / offering structure

• To perform in competition, innovative solutions approach required.

Price Gap of 25% to 35% vis-à-vis competition while bidding for new business.

How HM practices applicable for addressing

this price gap?



Measures to bridge this price gap

There are challenges faced during designing solutions for new business.

There is a trade-off between:

SLA’s Demanded

Clients wants

better SLA’s than

the incumbents

FTE’s Estimated

Can these SLA’s

be met with

proposed FTE’s

v/s

Proposed SLA’s

Based on

business priorities

SLA’s proposed

Available Capacity

Based on various

inputs: skill level,

tickets inflow etc

v/s

Competitive Cost

Aggressive

Pricing Structure

Proposed

Utilization

How best capacity

can be utilized for

given cost

v/s

HM processes coming to rescue

Input: SLA and ticket inflow known

Predict FTE’s through what-if analysis

See if given SLA’s are met

Input: fixed ticket inflow & FTE count

Propose SLA through what-if analysis

Analyze TAT

Predict %SLA Compliance for given FTE

Cost is the barrier

What-if done on FTE count



Result – Competitive Bids!

With Predictive processes in bidding for new projects, immediate results were seen!

Pyramid

Mutualize Optimized Transition

Shared Services

Skill - Mix

22% – 25%

Lower

Productivity

Offshore Leverage



Contact information

Shashank

Patil Improvement Champion

[email protected]

Leena

Sagar Associate Director, Process

Consultancy Head

[email protected]

mailto:[email protected]

mailto:[email protected]

The information contained in this presentation is proprietary.

© 2012 Capgemini. All rights reserved.

www.capgemini.com

About Capgemini

With more than 120,000 people in 40 countries, Capgemini is one

of the world's foremost providers of consulting, technology and

outsourcing services. The Group reported 2011 global revenues

of EUR 9.7 billion.

Together with its clients, Capgemini creates and delivers

business and technology solutions that fit their needs and drive

the results they want. A deeply multicultural organization,

Capgemini has developed its own way of working, the

Collaborative Business ExperienceTM, and draws on Rightshore ®,

its worldwide delivery model.

Rightshore® is a trademark belonging to Capgemini

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