Probabilistic Forecasting in Agile

Probabilistic Forecasting in Agile: Breaking Down the Uncertainties

Introduction:

Organizations are constantly attempting to make educated decisions that will drive success in an ever-changing business world. Forecasting is an important part of decision-making because it provides insight into how alternative scenarios can evolve based on past data and future estimates. Traditional forecasting methods, on the other hand, frequently fail to account for the uncertainties and shifting factors that are inherent in complicated projects. This is where agile probabilistic forecasting comes in, providing a more accurate and practical approach. In this post, we will look into probabilistic forecasting in an agile setting and present a real example to demonstrate its use.

Understanding Probabilistic Forecasting in Agile:

In agile, probabilistic forecasting is a strategy that allows project teams to evaluate uncertainties and forecast outcomes using ranges of probable values rather than single-point forecasts. Unlike traditional forecasting, which delivers a single number for a project's completion date or budget, probabilistic forecasting acknowledges project uncertainty and complexity. Agile teams are better suited to adjust and create more accurate estimates by accounting for uncertainties such as unforeseen risks, unpredictable scope changes, and team productivity swings.

Probabilistic forecasting is strongly related to agile methods, which emphasizes flexibility, regular adaption, and iterative development. Agile teams operate in short-term iterations known as sprints, during which they deliver tiny bits of value and continuously alter their plans in response to feedback and changing circumstances. This iterative approach is ideal for probabilistic forecasting because it allows for regular appraisal of uncertainty and forecast recalibration.

Example: Probabilistic Forecasting in Agile

As an example, take a software development project. Team A is developing a new app and is currently in the second sprint of the project. The team accomplished 80% of the planned user stories by the end of the first sprint. They must now predict the project's completion date using probabilistic forecasting.

Team A uses probabilistic forecasting instead of offering a single date prediction, which may be unattainable due to unanticipated events. They provide a range of possible completion dates for each user narrative to illustrate the inherent uncertainties of development. Using historical data from previous sprints and the team's average velocity, they calculate the probability distribution for delivering each user story within specific timeframes.

Team A then combines these probability distributions to get the overall probability distribution for finishing the project. They create millions of different project completion dates, along with associated probabilities, by using mathematical methodologies such as Monte Carlo simulation. This distribution represents probable outcomes more realistically, giving stakeholders a better grasp of the project's uncertainty and risk.

Instead of a specific date, the team can convey these probabilistic estimates to stakeholders, revealing the estimated completion date range (e.g., 70% certainty between 8 and 12 weeks). This method promotes transparency, establishes realistic expectations, and enables improved decision-making based on risk and uncertainty tolerance.

To use probabilistic forecasting in Agile to understand when a product backlog item (PBI) will be completed, a team can follow these steps:

1. Split PBIs into smaller, more manageable parts: Break down the PBIs into smaller user stories or tasks that can be estimated more easily

2. Assign Story Points to each PBI: Estimate the time and effort required to complete each PBI. Assign Story Points using a Fibonacci sequence or any other agreed-upon scale.

3. Track velocity: Track the velocity of the team (the number of Story Points completed in each iteration). Forecasting requires velocity because it determines the team's capacity.

4. Collect historical data: Keep track of completed PBIs, their Story Points, and the time it took to accomplish them. This data will be used to generate probabilistic forecasts.

5. Examine past data: Examine the relationship between the amount of Story Points accomplished and the time required for previous PBIs. This will provide information on the team's average velocity and variability.

6. Use Monte Carlo simulations: Monte Carlo simulations can be used to develop probabilistic projections. These simulations assess the probability distribution of finishing future PBIs using past data, as well as the team's average velocity and standard deviation.

7. Determine the probability of completion: Using the results of the Monte Carlo simulations, estimate the likelihood of finishing a specific PBI within a given timeframe. For example, the team may determine that completing a PBI within two sprints has a 70% likelihood of success.

8. Communicate and plan accordingly: Share the probabilistic forecasts with stakeholders and the product owner. Use this information to plan the product roadmap, set expectations, and make informed decisions regarding product feature prioritization.

Teams acquire a more accurate knowledge of when PBIs can be finished by introducing probabilistic forecasting into Agile. This method recognizes the inherent uncertainty in software development and enables improved adaption and decision-making throughout the project.

There are a few different approaches that agile teams can use to incorporate probabilistic forecasting into their methodology:

1. Monte Carlo Simulation: This method entails constructing hundreds of plausible scenarios based on various variables and probabilities. The team can acquire insights on the possibility of various outcomes and make more educated decisions by aggregating these scenarios.

2. Kanban Flow: Kanban is a visual project management strategy that emphasizes flow and waste reduction. Teams may estimate lead time and cycle time for each job or user story using probabilistic forecasting, allowing them to better plan and manage resources.

3. Forecasting Poker: Using the Fibonacci sequence or other estimation methodologies, the entire team estimates the effort required for each user story or task. The team can allocate a range of probable lengths to each tale and analyze the chance of completing it within the predicted range by using probabilistic forecasting.

4. Percentile-based Forecasting: The team analyzes historical data from past sprints or similar projects to determine the percentage of completion at various points in time in this approach. They can then use this information to forecast the likelihood of finishing current task items within certain periods.

5. Burnup Charts: Burnup charts can be used to track progress visually and forecast future consequences. The team can plot many lines on the chart reflecting different potential outcomes based on different assumptions and uncertainties by including probabilistic forecasting.

These approaches can assist agile teams in incorporating probabilistic forecasting into their processes, allowing them to generate more accurate predictions and adjust their plans as needed.

Pros of probabilistic forecasting in agile:

1. Flexibility: In the face of uncertainty, probabilistic forecasting provides for flexibility and adaptability. It understands that forecasts are not always precise or correct, and it allows for modifications and iterations in response to new information or changing conditions.

2. Transparency: Probabilistic forecasting allows for the clear communication of forecast uncertainty and risks. It enables stakeholders to comprehend the possibility of various outcomes and make informed decisions based on that knowledge.

3. Better risk management: Probabilistic forecasting improves risk management by taking into account a variety of probable outcomes and their likelihoods. It aids in the identification of high-risk regions or potential bottlenecks in the project, allowing the team to take proactive steps to mitigate those risks.

4. Improved decision-making: Probabilistic forecasting gives a more accurate and nuanced picture of the project's future. It aids in making more informed and sensible judgments by taking into account both expected value and the range of possible outcomes.

Cons of Probabilistic Forecasting in Agile:

1. Complexity and time-consuming: When opposed to simple point estimates, implementing probabilistic forecasting in agile might be more complex and time-consuming. It necessitates the collection and analysis of past data, the establishment of probability distributions, and the regular updating of projections based on new information.

2. Uncertainty may create anxiety: Uncertainties and hazards are acknowledged in probabilistic forecasting, which can cause worry and discomfort for some stakeholders. Individuals accustomed to standard deterministic forecasting approaches may find it difficult.

3. Limited accuracy in short-term forecasts: In short-term forecasts, probabilistic forecasting may not be as exact or accurate as deterministic methods. It is based on statistical models and historical data, which may not account for unanticipated changes or events.

4. Resistance to change: Individuals or teams accustomed to rigid or predictable timetables may be resistant to implementing probabilistic forecasting in an agile context. It necessitates a shift in perspective as well as a readiness to accept uncertainty and ambiguity.

Overall, while probabilistic forecasting has its challenges, the advantages of flexibility, transparency, and better risk management make it a valuable technique in agile project management.

Conclusion:

In today's dynamic corporate world, probabilistic forecasting in agile acknowledges project uncertainties and complexities, making it a vital decision-making tool. Agile teams may react to changing conditions and deliver more realistic projections to stakeholders by embracing a range of possible outcomes rather than relying on single-point estimates. Organizations that use probabilistic forecasting in agile get a competitive advantage through better planning, risk management, and resource allocation, resulting in higher project success rates.