1. Statistical Analysis Based Service Catalogue
Raghu K T – Director
Capgemini

2. Table of Contents
Process involved in Statistical Data Analysis & Base lining 01 06
Abstract
Objectives and Drivers of Service Catalogue
Overview of Model Building for Service Catalogue 02 07
Steps Involved in Cluster Analysis & Model Building 08 03
Structure of Service Catalogue
Sample Cluster Analysis of Independent Variables 09 04
Implementation Roadmap
Online Estimation Tool Integration
Predetermined Service Price
& Example 10 05 11
Conclusion

3. Abstract
Abstract
The testing industry is looking for innovative ways to optimize testing efforts and cost. Output Based Service Catalogue allows the customer to select only the services which he is interested in & pay only for the services consumed. This helps in transforming the organisation culture away from an FTE based to Consumption based model. Payment based on milestones which are linked to successful delivery of the expected outputs and not on effort spent by resources.
Online Estimation tool developed for the same purpose provides the effort at a click of a button for the services requested by business. “R” statistical analysis tool is mapped to database from where it fetches the effort at sub tasks level from historical data and provides the range of effort

4. Objectives & Drivers for Output Based Service Catalogue
Drive Organisation Behaviour Change
Need “One-Stop Shopping” for business users
Transform from FTE to Consumption based model
Engagement model standardisation between Business & IT to significantly reduces lead times.
Free up client to focus on “The What” vs “The How”
Client focuses on establishing policies, standards, long term strategies, vision and govern SLA/ KPIs.
Clear ownership on both service requestor & provider. Payment linked to successful outcome.
Predictable service price and output
Service standardisation – inputs, activities, outputs, standards, approvals.
Predetermined Service price, Client insulated from effort fluctuations
Huge reduction in effort & cost to scope new services
Accurate estimate using historical data & Statistical analysis

5. Structure of Service Catalogue

6. Service Catalogue Implementation Roadmap

7. Pre determined Service Size and Price Example
PS: All services cant be offered as Pre- determined Fixed Price Services from the catalogue due to varying degrees of maturity, complexity of project & delivery service requirements. Above is an example of Project level estimate using service catalogue

8. Process involved in Statistical Data Analysis & Base lining
Data Gathering: Identifying & Fetching data for all Dependent & Independent variables
Data Cleaning: Data treatment for all missing and outliers
Model Building:
Process flow
Input Data
Data Processing & Treatments
Variable Selection
Modeling Exercise
Amendments to Predictions
Accuracy
Model Deployment: Deployment of Model on SQL / Any platform, UI Development & maintenance tasks follow once analysis is completed

9. Overview of Model Building for Service Catalogue

10. Application Name + WO Type + WODuration
Steps Involved in Cluster Analysis & Model Building
Application Name
Estimate
Std. Error
t value
Pr(>|t|)
Application A *
-1.16E+03
5.82E+02
-1.993
Application B ***
-1.06E+02
6.46E+02
-0.164
Application C **
-2.93E+01
6.06E+02
-0.048
Application N *
7.64E+02
5.57E+02
1.371
Option
Variables
Stability
OBS
APP
comments 1
Application Name + WO Type + WODuration
63.1%
11637
867
Low Accuracy 2
Application Name + WO Type + WO Duration +Cycles Progression +Cycles Regression + Issues + Risks
73.7%
11332
627
Moderate Accuracy 3
Application Name + WO Type + WO Duration +Progression +Regression +Productivity + Regression
89.6%
High Accuracy

11. Sample Cluster Analysis of Independent Variables

12. Online Estimation Tool Integration
The outcome from the “R” statistical tool is loaded to SQL database and linked to the online estimation tool.
The tool provides the option to select the Service Type, Activity Type, Application Name, and Service Catalogue Item from the drop down list
Once necessary fields are updated, the tool gives back the effort details at a click of button to perform that particular service

13. Conclusion
We have seen how historical data for different services was used to get the effort details at each application and service level at a click of a button.
Benefits realized by implementing Statistical Analysis Based Service Catalogue are:
Huge reduction in time, effort & cost to scope new service engagements Person Days of effort saved on a monthly basis
Moving away from an FTE based to Service Output based model or consumption based model & Payment linked to successful outcome
Predictable quality, Service price & Outcome through standardized Input, Output & tasks 
Complete predictability of service cost and service quality standards
Accurate estimate through use of historical data and statistical analysis tool 
Delivery risks transferred to service provider thereby driving the right behaviours from the partner organisation

14. References & Appendix Service Catalogue Exhibit from Capgemini
Results / Outcome from “R” Statistical Analysis tool used for applications within the project
Business Statistics by Ken Black – Sixth Edition
Service Catalogue models & Algorithms related information from Google

15. Author Biography
Raghu is an experienced Testing Service Professional around 18 years of experience and working as Director with Capgemini from last 3+ years. He holds a Doctorate in Business Administration from Institute of Business Management, PGDBA from Symbiosis and Bachelor of Engineering degree from BMS College of Engineering, Bangalore University
Raghu is a certified Project Management Professional (PMP) from PMI, Certified Scrum Master (CSM), Six Sigma Certified - Green Belt, Capgemini Level 2 Certified Engagement Manager, TMMI Certified Professional with multiple assessments conducted for large financial banks.
Raghu is part of testing services for a client within financial services. He is heading the process transformation and innovation team. Raghu has worked on implementing the Output based Service Catalogue Model within the account at Capgemini and has used “R” Statistical Analysis tool for predictive Analysis

16. Question & Answers
Thank You!!!