Operational Productivity and Customer Satisfaction in Outsourced Software Projects

1. Operational Productivity and Customer Satisfaction in Outsourced Software Projects Sriram Narayanan

2. Agenda • Background Information and Research Interests • Overview & Motivation • Research Background • Individual learning Study • Results • Questions

3. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Work Background & Research Interests • Prior experience in purchasing in an automobile firm and managing a team of software engineers in a software firm • Dissertation: “Operational Productivity and Customer Satisfaction in Offshore Software Projects” • Stage: To defend proposal in 2 months • Thesis Advisors: Prof. Jayashankar M. Swaminathan Prof. Sridhar Balasubramanian • Research Interests • Learning, Productivity, Quality, and Customer Satisfaction Issues in Service Operations, Contract Manufacturing and Service Outsourcing

4. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Delivery Customer Internal Operations Managing and Optimizing Internal Processes Customer Facing Feedback The two facets of a Software Supply Chain

5. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Eastern Europe Ireland Russia China India Emerging Locations • Strong Labor Pool • Cost Competitiveness • Service Maturity • 51% Custom Software Engineering & • Maintenance • 85% Offshore Component • IT Services Demand • $382.1 billion Globally (2003) • Outsourced - $118.2 billion • Offshore - $70 billion by 2007 • Offshore Trends • - Dedicated Engagements • - R&D Outsourcing [datasource: Nasscom strategic report 2005]

6. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Emerging Dissatisfaction • Dissatisfaction (McDougall 2005, Engiardo 2006) • 51% respondents terminated an outsourcing contract (McEachern 2005) • Satisfaction down to 62% from 79% in 200 • Key problems include • Increased complexity of managing relationships • Reduced operational effectiveness • Quality of output from the outsourcing provider

7. Background InformationOverview & MotivationResearch Background Individual Learning Future Research The two facets of a Software Supply Chain Delivery Customer Internal Operations Managing and Optimizing Internal Processes Customer Facing Feedback

8. Research Questions How can engineers learn at a rapid rate ? The Debate of specialization Vs variety What is good ? Variety and Specialization so far have been studies in separation Simultaneous impact of specialization and variety experience on productivity ? What is the impact of turnovers on learning outcomes ? Background InformationOverview & MotivationResearch Background Individual Learning Future Research

9. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Background Literature • Short Overview • Several studies starting from Adam Smith • Different contexts • Healthcare (Edmondson et al, 2001, Reagans et al. 2005) • Manufacturing (Asher 1956, Baloff 1976; 1981) • Other Domains • Quality (Fine 1986, 1988) • Customer Dissatisfaction (Lapré and Tsikriktsis 2006) • More recently issues such as “Lean-What” and “Learn-How” (Tucker et al. 2006) • Software context – Huntley (2003) • Key Related Studies • Schilling et al (2003) – Variety • Argote et al. (1990) – Turnover • Huntley (2003) – Software Debugging

10. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Experience Balance Breadth + Individual Experience Depth + - Team Level Issues Turnover New Joining Team size Newness of Task + Cumulative Task Variety Model, Theory and Hypotheses Argote (1999) Darr et al. (1995) Reagans et al. (2005) Dutton and Thomas (1984) Simon (1991) March (1991) Productivity Schilling et al. (2003), Simon (1991) March (1991) , Waterman (1970) Cohen and Levinthal (1990), Simon (1985) Edmondson et al. (2001) Banker and Slaughter (1997) Littman et al. (1987) Rajlich (1999)

11. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Research Site • Large Indian Software Firm • More than 10,000 employees spread across multiple sites • Field study of 2 months onsite interviewing Managers to understand issues related to productivity in their environment • Data Collection spanned close to a year • Engineers undertaking debugging tasks on various modules of a large piece of software • Working on independent teams

12. Dataset Description Project Data Project is defined as a group of individuals under a common Supervisor 31 Teams 204 engineers working for varying time periods Training data: Technical, Soft skills and Quality training HR Data: Joining dates, Educational Qualification and Exit Dates Control Variables: Bug type, Bug severity, Process Issues Background InformationOverview & MotivationResearch Background Individual Learning Future Research

13. Model Variables Average time to fix of defect allocated to engineer i in month t Severity Work in progress defects Training undergone Unique components allocated previous month Time in project Total variety experience Total unique components experience Number of other team members Team change/Joining/Resigning Need for Information Background InformationOverview & MotivationResearch Background Individual Learning Future Research

14. Model Variables II Herfindahl-Hirschman Experience Index (HHEI) Adopted from HHI - Typically used in antitrust cases Gives a single coefficient that measures both variety and specialization Easy to use Bounded between 1/N and 1 Background InformationOverview & MotivationResearch Background Individual Learning Future Research

15. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Highly Concentrated Experience The HHEI Modules 1 2 .75^2+.125^2+ .125^2 =.593 3 Task Number 4 5 6

16. Background InformationOverview & MotivationResearch Background Individual Learning Future Research more balanced case The HHEI Modules 1 2 Task Number 3 4 .33^2+.33^2+ .33^2 =.33 5 6

17. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Methodology • Fixed Effects Regression • Choice made after several considerations • Hausman test for Endogenity • Controlled for cluster level error correlations • Outlier checks and sensitivity of the estimates to outliers • Multiple Models Estimated

18. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Results Overview • Accumulated variety experience has a positive impact on productivity after controlling for individual experience • Newer task allocations take a longer time on an average Productivity improvement due to variety is not free !!!

19. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Strike a Balance Very Little variety Very high variety Results Overview • How much Variety ? • More experienced individuals handle newer tasks better

20. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Results Overview • New joining impacts productivity while turnover does not. However… • Turnover and joining matter more in smaller groups ! • A cross model test revealed a significant effect • Significant impact of team change (Sum of turnover and New Joining)

21. Background InformationOverview & MotivationResearch Background Individual Learning Future Research Future Work • Optimization approaches to productivity • Hazard Models and split hazard models • A black box approach to task allocation

22. Thank you