How do you stay loyal to the principal of agile development in an organization having deadlines? This article discus challenges and how those are addressed using the cloud solution from agilemontecarlo.com. The foundation of the solution is built on Monte Carlo simulations.
Agile development did enter the scene 15 years ago as an alternative to traditional waterfall software development. Surveys from 2015-2016 states that two-third of the companies interviewed described themselves as “pure agile” or “leaning towards agile”. This makes agile the new norm in software development.
The definition of agile is according to the agile manifesto:
The key take away is that agile is not about implementing the lightweight scrum or kanban process, or if a backlog should be estimated in story points or days. It is a mindset, focus and culture that needs to flow in the entire organisation.
One topic that is discussed is if deadlines are agile. However in development deadlines are just a fact of life! Ranging from external customer commitments, Christmas season product launch or just getting the business case and internal budget approved.
So the topic to discus is not about if deadlines are agile or not. They are here to stay and with agile being the new norm the question is how an organization can work with deadlines still fulfilling the principal of agile development.
One large challenge for organization with deadlines embracing agile is that the deadlines are rather a vehicle of delivering scope and value instead of a fixed commit. All stakeholders needs to be involved securing that the deadline is delivering maximal customer value.
Agile with deadlines drives the need to secure full visibility, in the entire development flow to all stakeholders, which is a big change.
The true value and intention with the agilemontecarlo.com is to drive visibility within R&D as well as being a communication vehicle towards stakeholders. The visibility is the glue that combines deadline with the spirit of agile!
The solution is truly lightweight, with direct Jira support or manual projects, and it supports operating on high-level scope estimates. This is in line with the agile principal “Individuals and interactions over processes and tools”.
Also the key principals of agile such as “responding to changes” and “customer collaboration” are amplified and easier to fulfill with this way of working. Impacts towards the deadline are directly seen and can be managed increasing the organization flexibility.
Import your Jira data directly or add data from your issue tracking system manually.
First a Monte Carlo forecast is created. Then the project is Monte Carlo tracked during the execution phase providing full visibility on progress towards deadline.
The forecast is the communication vehicle with stakeholders to agree on the deadline. It also ensures full visibility on project risk and by that manages the project delivery expectations.
All items in the backlog shall be estimated in story points however as a best practice any scope breakdown should only be done for the 1-3 up-coming sprints. For the remaining backlog it is recommended to only do high-level story points estimation using e.g. a jumping scale 16,32,64,128…
In the forecast you can assign additional expected scope for bugs and new features/improvement. This is very powerful and a deadline without this information is of no use since it is not at all realistic.
Agile Monte Carlo tracking is created from the Agile Monte Carlo forecast. It is the vehicle to ensure that the implementation team and stakeholders are aligned on the progress towards forecasted deadline and underlying reasons behind deviations.
The recommendation is to show the graph in the sprint demo.
The overall goal of the tracking graph is to have one graph visualizing the complete project progress towards the deadline including visibility of the two key dimensions scope and velocity.
The report section has a full set of reports visualizing key project metrics and scope deviations.
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: optimization, numerical integration, and generating draws from a probability distribution.
scrumage.com - Monte Carlo simulations using "takt time”
agileupgrade.com - Monte Carlo simulations made practical
Do you have any questions?
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