Download - Software Productivity Framework
Zinnov
Productivity Assessment
January, 2010
Framework
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In this module we will define and analyze the various metrics that impact productivity and identify key existing challenges
Why do we need to track productivity?
Understand the throughput from the engineering resources, based on
To Identify the key productivity metrics to measure across organization
Setup and monitor the metrics Measure key productivity gaps across teams
Life cycle of the product Geographic location
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We will leverage our existing framework and customize it to suit Client needs
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Developing The Framework Data Collection Analysis Report &
Optimization
• Review Zinnov’s productivity framework Identify gaps in existing
metrics Finalize the metrics for
monitoring and measurements
• Identify the mechanism to collect the data for metrics
• Review existing artifacts (product and project management documents) to collect the data
• Interview key stakeholders to collect missing information and to validate the data collected through artifacts
• Review the data to understand the key gaps between the teams
• Capture qualitative and quantitative insights from the analysis
• Identify key risks impacting productivity across the organization
• Propose risk mitigation strategies
• Deliver a comprehensive report
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Zinnov will define the productivity drivers and metrics using experiences from other engagements and through discussions with Client (1/2)
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Productivity Drivers`
Organization Structure• Project ownership in India center• Technical ownership • Team alignments• Customer interaction• Level of autonomy
Infrastructure• Internet latency• Commute time• Phone/internet connectivity from
home• IT support• Lab availability
Communication• Travel between the centers• Number/Type of issue escalations• Status reports• Meeting effectiveness• Project transparency Human Resources
• Brand recognition• Team ramp up time• Attrition rate• Capability development• Resource allocation • Interview process efficiency• Employee motivation
Knowledge Transfer and Management
• Time spent on knowledge transfer• Mode of knowledge transfer• Average number of reviews• Effectiveness of KMSDevelopment Process
• Coding standard• Documentation• CMS process• Release processes• Risk planning• Requirement mgmt• Change management• Project initiation
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Zinnov will define the productivity drivers and metrics using experiences from other engagements and through discussions with Client (1/2)
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DEV
ELPO
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QU
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SURA
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SUST
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• % of milestone slippage• Estimation accuracy• Effort variance• Defect density• Field error rate• Defect removal efficiency
• Cost of quality• % of automation• Test case defect
detection efficiency• Bug type distribution• Test cycle distribution• Field error rate
• Defect removal efficiency• Maintenance rework• Response time• # open P1, P2, P3
Definition of Productivity Metrics for the Engineering Group
Key metrics required by Client to measure productivity will be determined and appropriate metrics will be identified Metrics could be based on factors such as center maturity, people maturity, product maturity or PDLC process
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Define baselinesMap complexity vs. impact
Analyze impact on productivity
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In addition will baseline and finalize the metrics and drivers
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Impact
Com
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Productivity Drivers
O
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Impact
Com
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Col
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Productivity Metrics
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Analyze data collection complexity
Review with stakeholders
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Metrics and Drivers baseline
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An excel modeler will be developed that will assist in the collection, monitoring and tracking of the drivers and metrics
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• Metrics and driver information will be inputted into the modeler
• The modeler will have the capability – To compare metrics between
projects– To plot historic trends of
metrics– To plot relationship between
the metrics and drivers• The modeler can also be used as
a dashboard to measure the health of the projects
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Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8100
200
300
400
Planned hours Actual hours
23%
38%16%
19%
4% Design
Personal & Team reviews
Implementation
Unit test
System test
Other
0
10
20
30
40
50
60
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 Fiery PPA0
20
40
60
80
100
120
80
100
6070
3528
IdentifiedFixedRe-Opened
Planned Vs. Actual Hrs Percentage Engineering Effort
Planned Vs. Actual features Details of Bugs re-opened
The productivity metrics will be represented as a dashboard to executive management team
Sample Output
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Case Study 2: We assisted a industry leading product company in defining the productivity and quality measures and benchmarking them in their global centers
Challenge: Productivity measurements were frowned by the client stakeholders due to the complexities involved. Executives were frustrated with the level of details and assumptions provided
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Key Challenges- Process and metrics should to be simple yet powerful- Ability to easily compare productivity across Business Units
and Geographies- Strong framework that would minimize assumptions
Created Objectives
Defined Frameworks
Measured Efforts
Output Metrics
Compared Productivity
Zinnov and the client worked to propose an objective mechanism to measure the productivity of Development and Quality teams
Framework was defined to measure the complexity of the product based on the architecture, feature set, functionality size, internal/external interfaces, data structure etc.
A simple mechanism was devised to calculate the efforts using the experience levels, size of the team, man hours for each activity etc.
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.
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
Zinnov Solution
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Case Study 2: We assisted a industry leading product company in defining the productivity and quality measures and benchmarking them in their global centers
Output: The questionnaire based framework along with the dashboard view provided key insights to the executives on the productivity and quality differences between the teams
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KEY BENEFITS
Ability 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
Ability to measure metrics across business units,
product/feature based teams and
geo diversified teams
Normalizing the assumption to
minimize the error in measurement
Deliverables- Framework with the guidelines for the input- Comprehensive questionnaire with details on scoring mechanism- Excel based tool to capture the complexity of the team based on the User
input- Dash board view to objectively compare the differences between teams
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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
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0 to 2 2 to 4 4 to 6 6+0
5
10
15
20
25
30
35
10
5
7
4
6 3
14
5
4
14
1 2
Fresher Product Engineering Vendor IT Services
Product Development Companies
• Average experience of the development team is about 18 months.
• Only one employee is from a product development background, with 3 – 4 years of experience which means most of the early hires were from service companies.
• There are only 6 engineers in the APPS group with an experience ranging from 3 – 4 years; they are the earliest employees
• The majority of the people working for the past 2 – 3 years are from service companies.
Num
ber o
f Soft
war
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evel
oper
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Years of Experience
Companies Vs. Client Experience – Development team
31
18
11
6
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Case Study 3: We also provided recommendations to enhance productivity
Output: On the basis of our analysis, the Client looked to resolve some constraints that impacted the productivity of the teams
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• There seems to be a high level of threshold for non-performance in the organization. There were not many who had been let go due to non-performance.
• There has been no instances of successful initiation and implementation of change initiatives.
• Even the product managers of different products were not clear on the roadmap for those products. There were at least two scenarios where the engineering team spent months building features/add-ons that the product managers were not aware of or had consented to.
• There are number of projects still in a split team model. The projects carry an inherent overhead in terms of communication and management time from the US. It is also extremely difficult for the US managers to track the tasks executed by the local teams.
Organization cultural issues
Lack of well-defined product planning process
Project model with inbuilt overheads
• Most of the engineers have never met a customer or even seen a video of how the customer uses the products that are built from the India center. Only few engineers have been exposed to customers at trade shows.
Lack of domain exposure to India center
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