{"id":553294,"date":"2025-03-10T11:45:00","date_gmt":"2025-03-10T10:45:00","guid":{"rendered":"https:\/\/www.devoteam.com\/expert-view\/embracing-uncertainty-with-monte-carlo-simulations-in-agile\/"},"modified":"2025-03-10T11:45:00","modified_gmt":"2025-03-10T10:45:00","slug":"embracing-uncertainty-with-monte-carlo-simulations-in-agile","status":"publish","type":"expert-view","link":"https:\/\/devoteam.info\/en-nl\/expert-view\/embracing-uncertainty-with-monte-carlo-simulations-in-agile\/","title":{"rendered":"Embracing Uncertainty with Monte Carlo Simulations in Agile"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">We&#8217;ve all been there: A stakeholder asks for a delivery date, and the team, armed with their intuition and perhaps some historical velocity data, comes up with a number. But what if that number is <strong>only a rough estimate, ignoring the uncertainties<\/strong> inherent in <a href=\"https:\/\/devoteam.info\/en-nl\/services\/cloud-native-apps-development\/\" target=\"_blank\" rel=\"noreferrer noopener\">software development<\/a>?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What if we could offer<strong> a more nuanced, data-driven forecast<\/strong> that acknowledges the intrinsic variability of Agile projects? This is where Monte Carlo simulations provide a powerful tool for probabilistic planning and forecasting.<\/p>\n\n\n\n<div class=\"wp-block-yoast-seo-table-of-contents yoast-table-of-contents\"><h2>Table of contents<\/h2><ul><li><a href=\"#h-a-brief-history-from-nuclear-physics-to-agile-planning\" data-level=\"2\">A Brief History: From Nuclear Physics to Agile Planning<\/a><\/li><li><a href=\"#h-the-cone-of-uncertainty-a-familiar-analogy\" data-level=\"2\">The Cone of Uncertainty: A Familiar Analogy<\/a><\/li><li><a href=\"#h-what-is-a-monte-carlo-simulation\" data-level=\"2\">What is a Monte Carlo simulation?<\/a><\/li><li><a href=\"#h-why-use-monte-carlo-simulations-in-agile\" data-level=\"2\">Why use Monte Carlo simulations in Agile?<\/a><\/li><li><a href=\"#h-how-to-use-monte-carlo-simulations-for-agile-forecasting-a-practical-example\" data-level=\"2\">How to Use Monte Carlo Simulations for Agile Forecasting: A Practical Example<\/a><\/li><li><a href=\"#h-developing-a-probabilistic-roadmap-an-example-of-release-planning\" data-level=\"2\">Developing a Probabilistic Roadmap: An Example of Release Planning<\/a><\/li><li><a href=\"#h-conclusion\" data-level=\"2\">Conclusion<\/a><\/li><\/ul><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-a-brief-history-from-nuclear-physics-to-agile-planning\">A Brief History: From Nuclear Physics to Agile Planning<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Before we dive into the &#8220;how,&#8221; let&#8217;s take a quick look at the &#8220;why.&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The Monte Carlo method isn&#8217;t a new Agile technique. It has its <strong>roots in nuclear physics<\/strong>, developed by John von Neumann and Stanislaw Ulam in the 1940s. During the Manhattan Project, they faced complex problems that were too difficult to solve analytically.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Their solution? Use <strong>randomness<\/strong>! They named it after the famous gambling destination, Monaco, because the method relies on the same principles as randomness. They used repeated <strong>random sampling to obtain numerical results<\/strong>\u2014a technique that has proven incredibly useful for modelling complex systems. We can harness this same principle, the power of repeated random sampling, in Agile.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-the-cone-of-uncertainty-a-familiar-analogy\">The Cone of Uncertainty: A Familiar Analogy<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Think about weather forecasting, especially hurricane predictions. Meteorologists don&#8217;t give you a single point on a map where the hurricane will make landfall. Instead, they show you a &#8220;cone of uncertainty.&#8221; This cone represents <strong>all possible paths<\/strong> the hurricane could take, with the most likely path in the center. The wider the cone, the greater the uncertainty. This is a perfect example of <strong>probabilistic forecasting<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">They use complex models and simulations (often incorporating Monte Carlo methods) to <strong>generate thousands of possible hurricane paths<\/strong>, each with a certain probability. This gives them a much more realistic picture of the potential risks. Like hurricane paths, software development projects are subject to many variables that make accurate predictions impossible. Monte Carlo simulations provide <strong>our own &#8220;cone of uncertainty&#8221; for project timelines and deliverables<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-a-monte-carlo-simulation\">What is a Monte Carlo simulation?<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine you&#8217;re trying to predict how long it will take to build a complex Lego castle. You could try estimating based on your past Lego building experiences, but that assumes everything will go exactly as planned.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A Monte Carlo simulation takes a different approach. It runs thousands of virtual &#8220;Lego building sessions,&#8221; each with slightly different build speeds based on your past performance. The result isn&#8217;t a single number but <strong>a range of possible completion times, each with an attached probability<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, you might find a 50% chance of completing in 10 days, a 75% chance of completing in 12 days, and a 90% chance of completing in 15 days. This gives you a much clearer picture of the project&#8217;s potential trajectory.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In Agile, we can <strong>apply this same principle to velocity forecasting, story point estimation, or even delivery date prediction<\/strong>. Instead of relying on a point estimate, we use historical data to simulate possible outcomes, giving us a probabilistic forecast.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-why-use-monte-carlo-simulations-in-agile\">Why use Monte Carlo simulations in Agile?<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Embrace uncertainty: <\/strong>Agile development is inherently unpredictable. Monte Carlo simulations acknowledge this uncertainty, providing a more realistic view of potential outcomes.<\/li>\n\n\n\n<li><strong>Data-driven decisions: <\/strong>These simulations provide data to support sprint planning, release planning, and road mapping decisions.<\/li>\n\n\n\n<li><strong>Improved communication: <\/strong>Probabilistic forecasts facilitate more transparent communication with stakeholders by presenting a range of possibilities rather than a single number.<\/li>\n\n\n\n<li><strong>Increased Confidence: <\/strong>Understanding the range of potential outcomes increases confidence in the team&#8217;s ability to deliver, even if the exact date remains uncertain.<\/li>\n\n\n\n<li><strong>Focus on Value: <\/strong>By understanding probability, teams can better focus on delivering value incrementally with more certainty.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-to-use-monte-carlo-simulations-for-agile-forecasting-a-practical-example\">How to Use Monte Carlo Simulations for Agile Forecasting: A Practical Example<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Let&#8217;s say your team&#8217;s historical sprint velocities are: 10, 12, 8, 15, 20, 11. Here&#8217;s how you might use a Monte Carlo simulation:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Collect Data: <\/strong>Collect your historical velocity data.<\/li>\n\n\n\n<li><strong>Choose a tool: <\/strong>Use a spreadsheet (like Excel or Google Sheets), a dedicated Monte Carlo simulation tool, or even write a simple Python script. (See the example below)<\/li>\n\n\n\n<li><strong>Run the simulation: <\/strong>The simulation will randomly sample your historical velocities to create thousands of possible future sprint outcomes.<\/li>\n\n\n\n<li><strong>Analyse the results: <\/strong>Examine the distribution of results. What are the probabilities of reaching different velocity ranges?<\/li>\n\n\n\n<li><strong>Communicate the forecast: <\/strong>Share the probabilistic forecast with stakeholders, explaining possible outcomes and their associated probabilities.<\/li>\n<\/ol>\n\n\n\n<pre class=\"wp-block-code\"><code>import numpy as np\nimport matplotlib.pyplot as plt\n\nhistorical_velocity = &#91;10, 12, 8, 15, 20, 11]\nnum_simulations = 10000\nnum_future_sprints = 5\n\nsimulated_velocities = &#91;]\nfor _ in range(num_simulations):\n    future_velocities = np.random.choice(historical_velocity, size=num_future_sprints, replace=True)\n    simulated_velocities.append(np.sum(future_velocities))\n\nsimulated_velocities = np.array(simulated_velocities)\npercentiles = np.percentile(simulated_velocities, &#91;10, 50, 90])\n\nprint(f'10th Percentile: {percentiles&#91;0]}')\nprint(f'Median Velocity (50th Percentile): {percentiles&#91;1]}')\nprint(f'90th Percentile: {percentiles&#91;2]}')\n\nplt.hist(simulated_velocities, bins=50, density=True, alpha=0.7, label=\"Simulated Velocities\")\nplt.axvline(percentiles&#91;0], color='r', linestyle='--', label='10th Percentile')\nplt.axvline(percentiles&#91;1], color='b', linestyle='-', label='50th Percentile (Median)')\nplt.axvline(percentiles&#91;2], color='g', linestyle='--', label='90th Percentile')\nplt.xlabel(\"Cumulative Velocity\")\nplt.ylabel(\"Probability Density\")\nplt.title(\"Monte Carlo Simulation of Future Velocity\")\nplt.legend()\nplt.show()<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-developing-a-probabilistic-roadmap-an-example-of-release-planning\">Developing a Probabilistic Roadmap: An Example of Release Planning<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Let&#8217;s say you need to launch a new version of your product. You&#8217;ve estimated that the remaining work requires approximately 60 story points. We can create a probabilistic roadmap for the release using the same historical velocity data (10, 12, 8, 15, 20, 11).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Simulate multiple sprints: <\/strong>Run the Monte Carlo simulation, but this time, instead of just looking at 5 sprints, simulate enough sprints to cover the estimated story points. We don&#8217;t know how many sprints this will take, so the simulation will help us determine the probability.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Calculate release probability: <\/strong>For each simulation run, determine how many sprints it took to reach or exceed 60 story points. This gives you a distribution of possible release times (in sprints).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Determine confidence intervals: <\/strong>Analyze the distribution of release times. For example:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>&#8220;We are 80% confident that we will launch the new version within 6 sprints.&#8221;<\/li>\n\n\n\n<li>&#8220;There&#8217;s a 50% chance we&#8217;ll launch within 5 sprints.&#8221;<\/li>\n\n\n\n<li>&#8220;There&#8217;s a 95% chance it won&#8217;t take more than 7 sprints.&#8221;<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Communicate with stakeholders: <\/strong>Present these probabilistic release dates to stakeholders. Instead of a fixed date, you provide a range of possibilities with associated confidence levels, allowing for more realistic expectations and better planning.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Example communication: <\/strong>&#8220;Based on our historical performance and current estimates, we are 80% confident that we can launch the new version within 6 sprints. While we could launch earlier, this provides a reasonable timeframe for planning purposes. We will continue to monitor our progress and update you if anything changes.&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This approach offers several advantages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Transparency: <\/strong>Stakeholders understand the uncertainty involved and are less likely to be surprised by delays.<\/li>\n\n\n\n<li><strong>Flexibility: <\/strong>The roadmap can be adjusted based on progress and changing priorities.<\/li>\n\n\n\n<li><strong>Data-driven decisions: <\/strong>Release planning is based on data, not guesswork.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-conclusion\">Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Monte Carlo simulations offer<strong> a powerful way to move beyond point estimates <\/strong>and embrace the uncertainty inherent in Agile. Providing a range of possible outcomes enables more informed decision-making, better communication with stakeholders, and increased confidence in the team&#8217;s ability to deliver value. So, cast your crystal ball, embrace the power of probability, and start forecasting confidently!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>A key takeaway for the reader: <\/strong>Consider how you currently plan in your Agile projects. Could Monte Carlo simulations offer a more realistic and helpful approach? Experiment with the provided Python code or explore other available tools. Start small, perhaps!<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">References<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>D. Vacanti, <em>When Will It Be Done?: Lean-Agile Forecasting to Answer Your Customers&#8217; Most Important Question, 2020<\/em><\/li>\n\n\n\n<li><a href=\"https:\/\/www.scrum.org\/resources\/monte-carlo-forecasting-explained\" target=\"_blank\" rel=\"noreferrer noopener\">Monte Carlo forecasting explained<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.scrum.org\/resources\/blog\/monte-carlo-forecasting-scrum\" target=\"_blank\" rel=\"noreferrer noopener\">Monte Carlo forecasting Scrum<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/scrumorg-website-prod.s3.amazonaws.com\/drupal\/2024-05\/Probabilistic%20Forecasting%20and%20Flow%20with%20Scrum%20-%20Whitepaper.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">Probabilistic Forecasting and Flow with Scrum<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.scrum.org\/resources\/forecasting-techniques\" target=\"_blank\" rel=\"noreferrer noopener\">Forecasting techniques<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Monte_Carlo_method\" target=\"_blank\" rel=\"noreferrer noopener\">Wikip\u00e9dia Monte Carlo method<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>We&#8217;ve all been there: A stakeholder asks for a delivery date, and the team, armed with their intuition and perhaps some historical velocity data, comes up with a number. But what if that number is only a rough estimate, ignoring the uncertainties inherent in software development? What if we could offer a more nuanced, data-driven [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","categories":[885],"tags":[],"industry":[],"class_list":["post-553294","expert-view","type-expert-view","status-publish","hentry","category-cloud-native-app-development-en-nl"],"acf":[],"cards":"\n\t<div class=\"single-post-card\">\n\n\t\t\n\n\t\t\n\t\t<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-43282307 wp-block-group-is-layout-flex\">\n\t<p style=\"font-style:normal;font-weight:700\" class=\"has-link-color wp-elements-1 wp-block-lp-post-type has-text-color has-primary-color has-small-font-size\">Expert View<\/p>\n\n\t\t\n\t\t<h3 style=\"font-style:normal;font-weight:400\" class=\"wp-block-post-title has-base-font-size\"><a href=\"https:\/\/devoteam.info\/en-nl\/expert-view\/embracing-uncertainty-with-monte-carlo-simulations-in-agile\/\" target=\"_self\" >Embracing Uncertainty with Monte Carlo Simulations in Agile<\/a><\/h3><\/div>\n\t\t\n\t<\/div>\n\n","yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.4 (Yoast SEO v28.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Embracing Uncertainty with Monte Carlo Simulations in Agile | Devoteam<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/devoteam.info\/en-nl\/expert-view\/embracing-uncertainty-with-monte-carlo-simulations-in-agile\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Embracing Uncertainty with Monte Carlo Simulations in Agile\" \/>\n<meta property=\"og:description\" content=\"We&#8217;ve all been there: A stakeholder asks for a delivery date, and the team, armed with their intuition and perhaps some historical velocity data, comes up with a number. 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But what if that number is only a rough estimate, ignoring the uncertainties inherent in software development? 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But what if that number is only a rough estimate, ignoring the uncertainties inherent in software development? What if we could offer a more nuanced, data-driven&hellip;","_links":{"self":[{"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/expert-view\/553294","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/expert-view"}],"about":[{"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/types\/expert-view"}],"version-history":[{"count":0,"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/expert-view\/553294\/revisions"}],"wp:attachment":[{"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/media?parent=553294"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/categories?post=553294"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/tags?post=553294"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/devoteam.info\/en-nl\/wp-json\/wp\/v2\/industry?post=553294"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}