Statistical Analysis Based Service Catalogue Raghu K T – Director Capgemini

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

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

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

Structure of Service Catalogue

Service Catalogue Implementation Roadmap

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

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

Overview of Model Building for Service Catalogue

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 0.04713 Application B *** -1.06E+02 6.46E+02 -0.164 0.86986 Application C ** -2.93E+01 6.06E+02 -0.048 0.96145 Application N * 7.64E+02 5.57E+02 1.371 0.17136 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

Sample Cluster Analysis of Independent Variables

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

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. 1200 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

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 https://www.rstudio.com/online-learning/#R

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

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