Estimating Delivery Dates With Scrum: Techniques And Best Practices
Estimating Accurate Delivery Dates
Estimating accurate delivery dates for software projects can be very challenging, especially when using an Agile framework like Scrum that embraces uncertainty and changing priorities. However, by leveraging key estimation techniques and following best practices around precision, Scrum teams can systematically establish and maintain realistic delivery date estimates.
Challenges with Estimating Delivery in Scrum
Uncertainty and unpredictability
A core tenet of Scrum is welcoming changing requirements, even late in development. This flexibility comes at the cost of predictability and makes precise date estimation difficult. Unknown future product backlog items and requirement changes means teams cannot accurately plan far ahead.
Lack of historical data
For new products or new teams, there is often no historical velocity or benchmark data to better understand capacity over iterations. Legacy date estimating models like parametric and comparative techniques also lose relevance without backlogs and vetted data on past performance.
Changing priorities
The business may re-prioritize the product roadmap and backlog without notice, forcing the team to adapt. Delivery date estimates scoped on old priorities then lose accuracy. The further out estimates go, the more priority changes compound estimate uncertainty.
Key Techniques for Improved Estimates
While unknowns make accurate long-term date forecasts difficult, teams can leverage core techniques to systematically improve estimate consistency and precision.
Relative sizing and story points
Converting product backlog items and requirements into dimensionless story points for relative sizing limits estimate volatility. Story points rate item complexity relative to other items, avoiding misleading precision from absolute time units. Grounds estimates and priorities in data.
Velocity tracking over sprints
Summing story point completions across sprints establishes reliable team velocity averages over time. Velocity measures sustainable rate of story point completion and marks pragmatic upper bound for iteration planning. Tames estimate distortion from shifting items and priorities.
Accounting for capacity and focus factor
Adjusts velocity for real-world team capacity, accounting for meetings, maintenance, vacation, etc that limit dedicated feature time per sprint. 90% utilization makes for an actionable focus factor target given capacity reality. Stabilizes estimate inputs.
Monte Carlo simulations
Running probabilistic Monte Carlo models on the backlog using story point velocity and focus factors establishes confidence intervals for delivery date ranges. Concretely accounts for estimate variability and uncertainty; improving transparency on dates.
Best Practices for Increased Precision
While no perfectly accurate long-term forecast exists, certain practices ratschet up estimate precision to systematically narrow delivery date uncertainty.
Establish definition of “done”
Creates clarity on what work full delivery entails to minimize surprise scope omissions undermining estimates. Foundational reference improving relative sizing accuracy and sprint consistency. Bonds team on quality standards.
Refine estimates over time
Progressively elaborates story point estimates using emergent sprint details. Converges on reference story point standard while tracking changes. Learning velocity from completed items grounds future item estimates. Embraces uncertainty.
Review and adjust after each sprint
Inspects performance vs plan each sprint to validate estimates or trigger needed corrections. Transparently confronts estimate weaknesses early before downstream impacts compound. Course-corrects dating assumptions.
Add buffer for unknowns
Padding dates by 15%+ acknowledges the intrinsic uncertainty in Scrum delivery scoping, protecting the business from blind spots while retaining flexibility. Structures in humbleness.
Automate calculations where possible
Tools like Jira allow automating status quo story point burn rates, velocity averages, and monte carlo models to dynamically recalculate probabilities as new sprint data emerges. Saves time while improving rigor.
Example Code for Monte Carlo Simulation
Running a Monte Carlo simulation allows modeling likely delivery date ranges based on the inherent uncertainty in story point velocity averages and completion probabilities across sprints. The below Python code implements a basic example.
– Python code to run multiple iterations and probability distribution
import random
import matplotlib.pyplot as plt
# Model inputs
numIterations = 1000
velocityMean = 20
velocitySD = 5
storyPoints = 180
sprints = storyPoints / velocityMean
# Initialize arrays
deliveryDates = []
for i in range(numIterations):
# Sample velocity
velocity = random.gauss(velocityMean, velocitySD)
if (velocity < 0):
velocity = 0
# Calculate timeline
timeline = storyPoints / velocity
# Sample delivery date centered on timeline average
date = random.gauss(timeline, 1)
# Store simulation iteration result
deliveryDates.append(date)
# Graph resulting probability distribution
plt.hist(deliveryDates, bins=20)
plt.show()
Business leaders can now allocate resources and plan launches factoring in the probabilistic range of completion targets surfaced through the monte carlo uncertainty modeling.
Summary and Recommendations
Estimating anything complex comes loaded with uncertainty, particularly software delivery dates using a dynamic framework like Scrum. Still, teams can dramatically improve estimate accuracy and precision through certain techniques and practices.
- Estimation is hard, focus on precision over accuracy
Acknowledging the intrinsic inaccuracy in long-term estimates shifts focus toward precision-driven practices improving model reliability and narrowing uncertainty.
- Continuously inspect and adapt estimates
Build in sprint reviews of performance versus plan to correct biases and stale assumptions before downstream impacts compound uncontrollably.
- Balance flexibility and structure
Leverage just enough structured techniques like relative sizing and velocity tracking to ground estimates while retaining the flexibility Scrum requires for the fluid incorporation of learning and change.
With concerted effort, teams can come to dependably scope, size, schedule, and deliver complex work in a world refusing stasis. And that makes all the difference.
