This document discusses defining and tracking productivity metrics for an organization. It proposes identifying key metrics across teams to measure productivity gaps. It suggests developing a framework to collect data, analyze gaps, and deliver a report with optimization recommendations. Sample metrics are provided for engineering, development, sustainment, and quality assurance. Case studies demonstrate defining complexity-weighted productivity comparisons between global teams and addressing constraints impacting productivity.

1. Productivity Assessment Framework January, 2010

4. Geographic location2

6. Identify gaps in existing metrics

7. Finalize the metrics for monitoring and measurements

8. Identify the mechanism to collect the data for metrics

9. Review existing artifacts (product and project management documents) to collect the data

10. Interview key stakeholders to collect missing information and to validate the data collected through artifacts

11. Review the data to understand the key gaps between the teams

12. Capture qualitative and quantitative insights from the analysis

13. Identify key risks impacting productivity across the organization

14. Propose risk mitigation strategies

15. Deliver a comprehensive report 3

17. Number/Type of issue escalations

18. Status reports

19. Meeting effectiveness

21. Team ramp up time

22. Attrition rate

23. Capability development

24. Resource allocation

25. Interview process efficiency

27. Commute time

28. Phone/internet connectivity from home

29. IT support

31. Technical ownership

32. Team alignments

33. Customer interaction

35. Mode of knowledge transfer

36. Average number of reviews

38. Documentation

39. CMS process

40. Release processes

41. Risk planning

42. Requirement mgmt

43. Change management

44. Project initiation4

46. Estimation accuracy

47. Effort variance

48. Defect density

49. Field error rate

50. Defect removal efficiency

51. Defect removal efficiency

52. Maintenance rework

53. Response time

54. # open P1, P2, P3

55. Cost of quality

56. % of automation

57. Test case defect detection efficiency

58. Bug type distribution

59. Test cycle distribution

61. Metrics could be based on factors such as center maturity, people maturity, product maturity or PDLC process5

62. 6 In addition will baseline and finalize the metrics and drivers Metrics and Drivers baseline 1 2 3 4 5 Define baselines Map complexity vs. impact Analyze impact on productivity Analyze data collection complexity Review with stakeholders Productivity Drivers Productivity Metrics P A Q R H H E M I Complexity in Collecting Data Complexity in Collecting Data K K D M D M C C O F O R G B Q L A Impact Impact 6

64. The modeler will have the capability

65. To compare metrics between projects

66. To plot historic trends of metrics

67. To plot relationship between the metrics and drivers

68. The modeler can also be used as a dashboard to measure the health of the projects7

69. 8 The productivity metrics will be represented as a dashboard to executive management team Planned Vs. Actual Hrs Percentage Engineering Effort Sample Output Details of Bugs re-opened Planned Vs. Actual features 8

71. Ability to easily compare productivity across Business Units and Geographies- Strong framework that would minimize assumptions Created Objectives Zinnov and the client worked to propose an objective mechanism to measure the productivity of Development and Quality teams Defined Frameworks Framework was defined to measure the complexity of the product based on the architecture, feature set, functionality size, internal/external interfaces, data structure etc. Measured Efforts A simple mechanism was devised to calculate the efforts using the experience levels, size of the team, man hours for each activity etc. Zinnov Solution Output Metrics For the quality engineering teams also, similar complexity metrics were defined using number of bugs captured, number of bugs not captures, percentage of automation etc. Compared Productivity Using the weighted averages of the complexity and the engineering efforts, we were able to get the directional productivity comparison between teams working across the organizations 9

73. Comprehensive questionnaire with details on scoring mechanism

74. Excel based tool to capture the complexity of the team based on the User input

75. Dash board view to objectively compare the differences between teamsAbility to measure the directional measure productivity without getting into complex calculations Simple efforts from the clients to collect, measure and monitor the metrics on an ongoing basis Normalizing the assumption to minimize the error in measurement KEY BENEFITS Ability to measure metrics across business units, product/feature based teams and geo diversified teams 10

76. 11 Case Study 3: We engaged with a mid-sized product company to analyze the productivity of their product development teams Output: Apples to Apples comparison between global teams were not feasible due to the maturity of the organizations. Only the engineering metrics were collected which resulted in dissatisfaction across teams Companies Vs. Client Experience – Development team 31 18 Number of Software Developers 11 6 Years of Experience 11

79. 13 About Zinnov Zinnov helps organizations globalize their business and improve people strategy. Our consulting solutions are based on rigorous research techniques, data analytics and communities Zinnov Research: http://www.zinnov.com/white_papers.php Zinnov Blog: http://zinnov.com/blog/ Zinnov Services: http://zinnov.com/services.html Zinnov Events: http://www.zinnov.com/zn_events.php Zinnov LinkedIn: http://www.linkedin.com/companies/30724 Zinnov Facebook: http://www.facebook.com/pages/Zinnov/111718952202627 @zinnov For additional details on the topic, contact info@zinnov.com

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