short form for 90 word session description:
Basic backlogs for a simple project are easy to prioritize and sequence. Backlogs for complex programs of many teams and many interdependent projects, (or complex product suites) are harder to measure value and prioritize. Deriving the optimal story sequence in the release plan in order to maximize project value requires a more scientific approach.
This session will introduce a set of heuristics and tools that will provide a solid introduction and practical tools. It will also describe the flaws of some commonly used financial model project selection techniques.
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Basic backlogs for a simple project are easy to prioritize and sequence, and we trust our Product Owners to prioritize the backlog and sequence stories into the release plan, after considering information on dependencies and risks provided by the team.
Backlogs for complex programs consisting of many teams and many interdependent projects, (or complex product suites) are much harder to measure and compare value and prioritize. Deriving the optimal story sequence in the release plan in order to maximize project value requires a more scientific approach.
This session will introduce a set of heuristics and tools that will provide a solid introduction and practical tools. It will also describe the flaws of some commonly used financial model project selection techniques.
Along with Kano and others, we will introduce “Incremental Funding Method” (Denne and Cleland-Huang) that can help you with roadmap and release planning. The Incremental Funding Method is a data driven financially informed (recognizes intangibles) approach to backlog prioritization and sequencing. IFM and the concept of the “Minimal Marketable Feature” fit harmoniously with incremental delivery of software per Agile, and can help define a path to dramatically accelerate ROI and reduce risk in projects.
What is the IFM heuristic? There is no algorithmic solution to maximizing the NPV of a software development project with multiple features. The problem is a category that mathematicians call NP-Complete. The only way to find the optimum sequence is to calculate the NPV of all feasible sequences and choose the best one. For all but the simplest projects, this is not computationally feasible in finite time. The IFM heuristic provides a way to quickly identify a near-optimal sequence without resorting to unfeasible computational overhead.