Introduction
Agile Project helps companies address four challenges within software development.
- Plan - Visualize the big picture?
- Improve - Increase efficency, quality and output?
- Forecast - Answers the question when is my Agile project done?
- Report - Data driven reporting and dependency visualization?
Plan - Visualize the big picture?
Easy to use powerful roadmap for one or multiple teams. Visulize the big picture to your stakeholders adding Epics, issues, dependencies, themes and milestones.
Improve - Increase efficency, quality and output?
Sinch (former CLX) provides a cloud PaaS communication platform with over 30 billion transactions per year. Agile Project implements a rich set of actionable flow metrics and it enabled Sinch to implement a feedback loop driving their lean agile transformation.
- Case Study - Implementing Lean Agile at Sinch 2018
- Case Study - Implementing Lean Agile at Sinch 2019
Forecast - Answers the question when is my Agile project done?
Agile Project answers the question "When are you done?" using powerful Monte Carlo simulations. Both forecasting, deadline tracking and timeline visualization on any selection of Jira data is supported.
Report - Data driven reporting and dependency visualization?
Agile Project supports eleven reports enabling data driven reporting to stakohlders. Also for dependency management both epic and issue visualization is supported.
Appendix
A – Monte Carlo Simulation
Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three distinct problem classes:[1] optimization, numerical integration, and generating draws from a probability distribution.
Source: Wikipedia