Your browser does not support JavaScript! Hyper-Productive Scrum Metrics
  • LinkedIn
  • YouTube
  • RSS

Hyper-Productive Metrics

Hyper-Productive metrics are designed to help Scrum Masters carefully tune their teams into a hyper-productive state. Hyper-Productivity is defined as a 400% increase in Velocity over the baseline Velocity with corresponding quality. The baseline Velocity (100%) is established for a team during their first Sprint.

Estimated time for this course: 105 minutes
Audience: Advanced
Suggested Prerequisites: Velocity, Scrum Master

Upon completion you will:

  • Be versed on the 10 KPIs that make up the Hyper-Productive metrics
  • Understand the reasons for having more metrics than just Velocity
  • Learn how to calculate Hyper-Productive metrics
  • Know how to use Hyper-Productive metrics to keep your team(s) tuned
  • Qualify for PMI PDUs. See FAQ for details
Hyper-Productive Metrics Overview:
These metrics are tools to closely examine Teams and conduct experiments to improve their productivity. They can be used to identify strengths and weaknesses, but more importantly allows the evaluation of Teams in flight. Sprint-by-Sprint these metrics can guide the Team towards ever greater productivity. One caution, Hyper-Productive teams can be very fragile and need constant tuning. These metrics will allow you to see problems as they emerge.

These metrics are also applicable across Teams. Often it is difficult for management to determine the productivity of one Team versus another as Story Point value can vary from Team to Team.

View and Download Class Slides

[slideshow_deploy id='5534']

Download Hyper Productive Metrics Slides

The Metrics
  • Velocity - ∑ of original estimates of all accepted work
  • Work Capacity  - The sum of all work reported during the Sprint, whether the Backlog Item toward which the work was applied finished or not.
  • Focus Factor - Velocity ÷ Work Capacity
  • Percentage of Adopted Work - ∑(Original Estimates of Adopted Work) ÷ (Original Forecast for the Sprint)
  • Percentage of Found Work - ∑(Original Estimates of Found Work) ÷ (Original Forecast for the Sprint)
  • Accuracy of Estimation - 1-(∑ (Estimate Deltas) ÷ Total Forecast)
  • Accuracy of Forecast - (∑Original Estimates) ∑ (∑ Original Estimates + ∑Adopted Work + ∑Found Work)
  • Targeted Value Increase - (TVI+)Current Sprint’s Velocity ÷ Original Velocity
  • Success at Scale - For each Point on the Fibonacci Scale (Fp), the formula is: (∑ No. Accepted Attempts of scale Fp) ÷(No. of All Attempts of scale Fp)
  • Win/Loss RecordEach Sprint is a Win only if: a) A minimum of 80% of the Original Forecast is Accepted and b) Found + Adopted Work During the Sprint remains at 20% or less of the Original Forecast.

Papers
Papers:

Hyper-Productive Metrics

S. Downey and J. Sutherland, Hawaii International Conference on Software Systems, Maui, Hawaii, 2013

Shock Therapy: A Bootstrap for Hyper-Productive Scrum

J. Sutherland, S. Downey, and B. Granvik, in Agile 2009, Chicago, 2009.

6 Agile Ways To Increase Employee Engagement

employee support

Organizations who utilize Agile ways of working experience a 20-30% improvement in employee engagement. Engage, Equip & Empower your employees.

learn more button click here

Categories:

en_USEnglish
fr_FRFrench es_ARSpanish en_USEnglish
Shares