Predictive Forecasting: What it is and How to Implement it
When it comes to project management, accurate forecasting methods are essential for success.
In software development, the question of "When will tickets be done?" is often asked by stakeholders. And while engineers earnestly strive to provide deadlines and adhere to them diligently, the reality often deviates from these well-intentioned plans. Tickets turn out to be more complex than initially perceived, bottlenecks emerge unexpectedly, and requirements undergo inevitable changes.
Because of this, predictive forecasting is vital for reaching goals as it allows for these factors to be a part of data collection and offers deeper insights into how long a ticket will truly take to be completed. Also known as probabilistic forecasting, predictive forecasting is a dynamic approach for planning and executing projects. In this post, we’ll explain what predictive forecasting is, how it’s superior to traditional planning methods like Scrum, and practical implementation strategies for your team.
What is predictive forecasting?
Forecasts are beneficial as they can help to make more accurate timelines for deliverables and decisions. Predictive forecasting expands on the classic forecast, as it leverages historical data, statistical algorithms, and machine learning to anticipate project outcomes with greater precision.
To put it bluntly, predictive forecasting allows teams to make even better forecasts. Predictive forecasting takes a data-driven approach, considering various factors such as team velocity, past performance, and external dependencies to generate more accurate forecasts.
Superiority over traditional Scrum methods
One major advantage of predictive forecasting is the ability to offer more accurate predictions, as opposed to more traditional Scrum methods which are static, meaning that deadlines and the number of tickets needed don’t shift. Instead, a more agile approach is far superior for software teams, as it is adaptable to changing circumstances and ticket fluctuations.
Additionally, while Scrum methods rely on velocity metrics and story points, predictive forecasting goes a step further by analyzing historical patterns and trends. This results in more precise forecasts that are better equipped to account for uncertainties and variations in story or ticket size, thereby minimizing the risk of underestimation or over-commitment.
Implementing predictive forecasting in engineering teams
Engineering teams can implement predictive forecasting with platforms such as Actionable Agile. It can easily be connected with your current storyboards, whether you’re using Jira, Shortcut, Trello, etc. Implementing predictive forecasting into your current workflow, while fairly straightforward, requires a strategic blend of technology, process refinement, and cultural adaptation. Here's how it can be done effectively:
- Data collection and analysis: The first step of implementing predictive forecasting begins by collecting historical project data, including sprint velocity, cycle time, and ticket sizes. Analyze this data to identify patterns and trends that can inform future forecasts.
- Monte Carlo chart: Utilize Monte Carlo simulation techniques to model different project scenarios and estimate the likelihood of meeting specific deadlines or milestones. This probabilistic approach provides a more nuanced understanding of project uncertainties.
- WIP limits: Implement Work In Progress (WIP) limits within your Kanban system to manage flow and optimize throughput. Thoroughly understanding your WIP limits is important, as it will make it easier for your team to recognize outliers and better focus team energy on getting tickets completed. It also provides even more accuracy when determining how long it should take to complete tickets.
- Continuous improvement: Embrace a culture of continuous improvement, where teams regularly review and refine their forecasting models based on new data and insights. Encourage collaboration between technical teams and decision-makers to align forecasts with organizational goals and priorities.
Sample charts above display the accuracy of forecasts via the Throughput Basis, the Monte Carlo method, and total monthly completion.
Predictive forecasting for sprints and Kanban
Predictive forecasting can be applied to both sprint-based and Kanban workflows, albeit with some nuances:
- Sprint-based forecasting: Predictive forecasting involves analyzing past sprint performance to estimate future sprint outcomes in sprint-based environments. By considering factors such as team capacity, historical velocity, and scope volatility, teams can forecast sprint deliverables more accurately.
- Kanban-based forecasting: Kanban systems visualize workflow and manage work in progress. Predictive forecasting in Kanban involves analyzing cycle time metrics, identifying bottlenecks, and optimizing flow to ensure the timely delivery of work items.
Getting started with predictive forecasting
Predictive forecasting can be incorporated at any project stage, and your team should be able to begin making more accurate forecasts within just one week of data collection. Ready to harness the power of predictive forecasting in your teams? Here are some steps for getting started:
- Define metrics: Identify key performance metrics such as cycle time, lead time, and throughput that are relevant to your projects.
- Gather data: Collect historical data from past projects, including sprint velocity, cycle time, and ticket sizes.
- Select tools: Choose appropriate tools and technologies for data analysis and visualization, such as Monte Carlo simulation software and Kanban boards.
- Train teams: Educate your teams on the principles and techniques of predictive forecasting, emphasizing the importance of data-driven decision-making and continuous improvement.
- Reforecast: Continuously update and refine your forecasting models based on new data and insights, soliciting feedback from both technical and financial stakeholders.
Predictive forecasting offers a powerful solution for technical teams and financial decision-makers alike. It enables them to make informed decisions, mitigate risks, and deliver value with confidence. By embracing predictive forecasting methodologies and leveraging advanced techniques, organizations can pave the way for greater efficiency, agility, and innovation in their project management practices.
7Factor’s teams of highly skilled engineers utilize predictive forecasting, making our deadlines and deliverables extremely accurate. If you are looking for data-driven and quality-focused teams, contact us today! Learn more about the services we can offer here.