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- Unlocking Efficiency: How ActionableAgile™ Analytics Saves Time and Money
In today's fast-paced software development environment, achieving predictable workflows and rapid delivery is essential for developers, agile coaches, project managers, scrum leaders, and leadership alike. ActionableAgile™ Analytics offers a solution that enhances efficiency, reduces delays, and boosts productivity, ultimately leading to substantial time and cost savings. The Problem Software development's primary challenge is the necessity for predictable workflows and rapid delivery. This issue affects all roles, causing misalignment within teams, frequent delays, and a diminished ability to adapt swiftly to market changes. Repercussions include lower customer satisfaction, delayed product releases, and financial losses. The Importance of Predictability and Speed According to the 2023 'The State of Agile' report by Praecipio, a leading Atlassian Solution Partner, velocity (the amount of work delivered in a given time frame) and predictability (the ability to accurately forecast delivery times) are critical metrics for measuring team performance. Teams that can deliver predictably and quickly are better positioned to meet customer expectations and achieve business goals. Case Analysis of a UK-based Financial Services Firm implementing ActionableAgile™️ Analytics Current Situation Significantly Low Productivity: The team faced extended lead times and cycle times. High Work-in-Progress (WIP): An unusually large amount of WIP for a single team. Challenges in Prediction: Difficulty in accurately forecasting completion times. Manual Tracking: Cycle time is tracked through spreadsheets, leading to challenges. Desired Improvements Better Prediction of Delivery Outcomes. Utilization of Flow Metrics. Increased Planning Accuracy Time and cost savings on manual tasks. The result after five sprints? Over a year - What would the savings from automating cycle time calculations look like? How do our users save time with ActionableAgile ™️ Analytics? Joanna Doyle, Agile Delivery Manager: "Before ActionableAgile™️ Analytics, I was nothing numbers within an Excel sheet. Now , it's really easy to discuss with stakeholders without having to explain what the numbers mean ." Alex Priestley, Product Leader at John Lewis / Pro Kanban Trainer: ‘We use the Aging Work In Progress chart during our daily standups to see in real time the issues to jump on and fix as a team. If we want to make decisions today that will make demonstrable improvements for tomorrow, that's my go-to' Elaine Tittanegro Correa, Agility Transformation Director: "ActionableAgile™️ Analytics' proactive nature makes it a powerful tool for driving efficiency and addressing challenges promptly" Ben Parry, Partner & Integration Delivery Lead: "The best benefit of ActionableAgile™️ Analytics is speed. The time between loading a board from JIRA and inspecting the related CFD is less than 5 minutes . There is no data refinement required." To sum it all up… ActionableAgile™ Analytics cuts through the clutter of misleading metrics, focusing on what truly matters—time and money. By leveraging Monte Carlo simulations for real-time forecasting and using the Aging Work In Progress Chart to highlight delays, teams can make informed decisions swiftly and streamline their workflow for better results. Spend your time completing work, not forecasting, with ActionableAgile™ Analytics. If you or someone you know is suffering from a manual data entry prison, help is on its way! Start a free trial here.

- Quick and accurate forecasts with ActionableAgile
When you talk about forecasting with your colleagues you are apt to hear about a wide range of experiences and opinions. There is almost always a difference of opinion on how to actually determine a forecast and there's often disagreement on whether or not you should be forecasting at all. #NoEstimates anyone? I believe that both of these disagreements exist because traditional estimation processes are so time-consuming, and often inaccurate. At 55 Degrees we don't think forecasting should be avoided, but we do need to find a way to create forecasts that can work in uncertainty and doesn't waste time and sanity in the process. What forecasting technique does ActionableAgile use? The strategy we use in our flagship product, ActionableAgile Analytics, uses a technique called probabilistic forecasting. As the name implies it helps you provide a forecast that examines possible outcomes and the likelihood of each. This makes it perfect for situations where there's not a single obvious, inarguable outcome. A probabilistic forecast includes a range of possible outcomes and the probability you'll land on one of the outcomes in that range. Here are some examples: "We have an 85% chance of finishing a work item in 7 days or less." "There's a 95% chance we can finish 6 items or more in a sprint." "There's a 70% chance that we'll finish these 20 items on or before November 1." In ActionableAgile we figure out the range of outcomes and related probabilities by using your data and looking at the Cycle Time and Throughput of your past work. What can I forecast with Cycle Time? Using our Cycle Time Scatterplot you can forecast how long it is likely to take to finish a future work item based on how long it took to complete items in the past. Read more about our Cycle Time Scatterplot! What can I forecast with Throughput? While with Cycle Time you can create forecasts for individual items, Throughput allows you to create forecasts for larger efforts containing multiple work items. This can mean a number of things such as sprints, projects, releases, and more. Using your Throughput, our Monte Carlo simulations take the rate at which you finished work in the past to answer questions like "How many items can I finish by X date?" (Fixed date) and "When will this set of work be finished?" (Fixed Scope). Learn more about how our Monte Carlo Simulations work. What do I need to get started? If you haven't already, start a free trial of ActionableAgile - either in our SaaS version or install our app in your Jira or Azure DevOps instance. Then load in your data and you're off to the races. Our charts and simulations will automatically provide you with forecasts you can use based on your data. You can use the included chart controls to segment or filter your data any way you want to have ultimate control over what you're forecasting. We are always available to help via our support portal located at https://55degrees.se. We hope to hear from you soon!

- Want to be more predictable? Do these two things every day…
One of the things leaders often say they want most is predictability. Fortunately there are 2 things you can do every day to be more predictable. Predictability is defined as the consistent repetition of a state, course of action, behavior, or the like, making it possible to know in advance what to expect. Dictionary.com If you read that definition carefully, you may notice that the words good or bad are missing. Predictability itself is value-neutral. Being predictable is the attainment of the skills required to allow customers and other interested parties to have the advantage of knowing what to expect. Now, we all can think of an organization that we think of as predictably horrible at customer service (erm… certain cable TV companies come to mind) or a restaurant that has predictably bad food. So, it is fair to say that leaders want value-driving predictability, especially in regards to an organization’s ability to deliver value to its customers on a regular basis. How can we tell how predictable we are? Pretty easily! You can tell how predictable you are just two pieces of data for each work item you finish: the date you started the work item and the date you finished it. Using that data, you can create a Cycle Time Scatterplot and get a very visual view of your predictability. Each dot on a Cycle Time Scatterplot represents a single piece of work that was completed and where the dot is placed tells us how long it took to deliver that piece of work. This duration is referred to as cycle time. If more than one item finishes on the same day with the same Cycle Time, you just make the dot bigger and write the number of items on the dot. In the beginning, when you have little to no predictability, the dots will be widely scattered all over the chart. As you learn how to become more predictable, the range of space the dots inhabit will become vertically narrower. Read our previous blog post “Quick and accurate forecasts with ActionableAgile” to learn how to use this chart to make your forecasts reliable, even when your work and circumstances aren’t varied. Two things you can do daily to be more predictable! Now, that you know how to determine your level of predictability, you probably want to know what to do to improve it! Well, there are two simple, domain-agnostic things that you can do each day to improve your predictability: Thing 1: Stop starting, start finishing One of the biggest culprits of lengthening Cycle Time is multi-tasking. Well, we think we’re multitasking but what we’re actually doing is something called task switching — putting one thing aside to work on another and then coming back to the first thing later. Implementing a limit to the number of things you will work on at a time can help individuals, teams, or even organizations focus on fewer items at a time, allowing them to deliver the items faster and, usually, with better quality. This limit is called a work-in-progress limit, or WIP limit, and is a key component of a Kanban system. These limits can be implemented per person, per team as a whole, or even by activity in a workflow or value stream as seen above. The important thing is that you establish a threshold and adhere to it as often as possible. So, each day, as you make choices about what to work on, finish what you’ve already started before you pick up anything new! Thing 2: Pay attention to the age of work-in-progress Once you have WIP limits in place you can start an effort to make sure no one work item ever sits and ages for too long. To do this, you need to be able to see how old an item is. If you are tracking your cycle time, you already know when you started an item and you probably know the current date. With those two data points, you can get a good picture of how old your work-in-progress is. That information can be used in daily meetings to ensure you prioritize work that’s in danger of getting old. Much like the Cycle Time Scatterplot, the Aging Work In Progress Chart has a dot for each work item but the axes are different. The Y-axis is the age of the item so far. The X-axis represents which stage of your workflow the item is in. The dot is placed at the intersection of these two data points. It is important to remember that all of these dots will (hopefully) end up as a dot on your cycle-time scatterplot. So, we want to make sure they don’t get super old and, even better, that they stay within an expected range. When you take the percentile lines from your Cycle Time Scatterplot and overlay them onto your Aging Work in Progress Chart, you can get a much more detailed understanding of how at risk an item is for finishing in your expected timeframe. The percentile lines tell you how likely your work is to finish by a certain age. So, if you are at the very beginning of your process for an item and it is already older than 60% of your items are when they’re finished, you know that item is at risk of negatively impacting your predictability. And, while one item may be nothing to freak out about, if this happens often enough, you become less predictable and have to provide longer estimates to your stakeholders. So, if you’ve been looking for a leading indicator of predictability, the Aging Work in Progress Chart is just what you asked for! Yes, you can really do it! What’s great about this is that you really don’t need to be a data geek and you don’t need to be using a specific methodology, framework, or toolset to improve your predictability. By focusing on finishing over starting by implementing WIP limits and working on older items first, you can greatly improve your predictability without a huge overhaul of your processes — though it might end up being the start of something quite transformative! If this topic gets you pumped up and wanting to learn more, consider taking a workshop offered by 55 Degrees AB or try out ActionableAgile for free!

- What is Cycle Time?
Cycle Time is the total elapsed time it takes a work item to travel from one point of your workflow (a start point) to another point (the finish point). This means that, depending on how you define start and finish for your context, you can measure the Cycle Time for a whole process or just a portion of it. Calculating Cycle Time Cycle Time is often expressed as: (Finish TU – Start TU) + 1 TU stands for Time Unit. You can use any granularity you'd like: seconds, minutes, hours, days or more. Why do we add 1? By adding one, this allows us to include both the start and the finish time unit in the Cycle Time so no time is left out. Cycle time is the total elapsed time it took from start to finish. This means it includes active working time as well any time that a work item is sitting there idle. So, whether work is waiting on someone, you’re blocked by a technical issue, or being interrupted by evenings and weekends, the time is included in an item’s Cycle Time. Sometimes teams visualize work time and wait time. It is all included in Cycle Time. Cycle Time cannot be calculated for a work item until it has reached your designated finish point. This means it is a lagging metric – one based on historical data. It is best to speak in the terms of your customer. They think in calendar days and not business days so we calculate cycle time in calendar days and not business days. It reduces misunderstandings. It's not inflating the metric, its reality. Julia Wester, Co-Founder @ 55 Degrees AB Why measure Cycle Time? Cycle Time is one of the four basic flow metrics, along with Throughput, WIP, and Work Item Age. These four flow metrics are baselines metrics that give you some insight into the underlying flow health of a process. Looking at Cycle Times helps us understand how predictably we deliver individual work items. If your process generates a wider range of Cycle Time data now than it did in the past then, objectively, you could say that your process has become less predictable than it used to be. By looking at how long it took you to finish a given percentage of items historically, you can get an idea of how long it may take you to deliver an item in the future - assuming your process hasn't significantly changed. This is best seen on a Cycle Time Scatterplot. Quickly looking at a Cycle Time Scatterplot chart above, in which each dot represents the Cycle Time of a given work item, we can easily say that we finish 95% of these work items in 23 days or less. This type of forecasting is reliable and extremely quick, allowing us to spend time on what’s truly valuable – doing the actual work. A great metric to start with Cycle Time is often the first flow metric that teams attempt and it is very easy to track — even by hand! All you need is to write down the start date and the end date of a work item and you can calculate cycle time. You can even make your own charts! Interested in tracking flow metrics like Cycle Time? Try out ActionableAgile for free today!

- Velocity alone doesn’t measure success
Neither does Throughput Many scrum teams use a metric called velocity as their primary metric. Velocity is commonly defined as the number of story points you finish over time, usually measured by sprints. The input of story points are assigned by scrum teams, often using the Fibonacci sequence, to pieces of work based on their relative complexity. A simple piece of work might be assigned 1 story point while a really complex piece of work could be assigned 13 points or more. A team’s velocity would represent how much complexity reached their definition of done in a given sprint. Measured from sprint to sprint, it becomes a sort of gauge of success. If it stays the same or goes up, that’s great. If it goes down repeatedly, there’s a problem that needs to be investigated. Teams using Throughput, another rate metric that counts the number of individual work items delivered instead of relative complexity, do the same. They measure throughput from across time periods, often sprints, and use it to determine their success is increasing, staying stable, or waning. No single metric defines success Unfortunately, success is more complicated than that and it can rarely if ever, be measured with a single metric. Most teams and organizations don’t just want to deliver things. They want to deliver good quality things and deliver them quickly on top of regularly delivering things. If you don’t see a distinction, let me share the story of a team I started coaching in 2019. This HR team came to me with a (perceived) Scrum process in place. They were using 2-week sprints for planning and retrospecting. But, they were still completely overwhelmed and unhappy. Though it could be higher, their throughput at least looked pretty consistent. To dig deeper, we looked at their Scrum board. The good news is that they understood their workflow. Their board had columns representing their entire workflow from planning to delivery. The bad news? It was chock full of work items. They were close to the end of their sprint yet many items were still in the sprint backlog. Despite the obvious fact that many of the items planned for the current sprint would not be finished by the beginning of their next sprint, they had already filled up the planning column for their upcoming sprint. Looking from multiple angles gives you a well-rounded picture In the end, it was clear that they suffered from a “too much WIP” (work-in-progress) problem. They started so much work that everything took much longer to finish than it would have needed if they had just started less work at one time. This was something we noticed almost immediately when we looked at a metric called Cycle Time. This is a measure of how long it takes a piece of work to be completed (you can define your start- and end-points for this metric). Using a chart called a Cycle Time Scatterplot, we could see that 85% of their work took up to 30 days to complete, while the remaining 15 took even longer! Now, remember their sprints were 14 days long. Houston, we have a problem! This team had unfortunately settled into a pattern of delivering a consistent number of work items — but they were very old work items. They hardly ever finished work items within the same sprint they were started in. By adding the Cycle Time metric to our arsenal we were able to get more insight into what was happening, find a problem, and start experimenting with solutions. In Summary The moral of the story is that one metric can be misleading at best, but it can be immensely harmful at worst. If you were to optimize for one metric without keeping an eye out for unintended consequences, you could end up causing bigger problems than you had when you started. When measuring team success, try to consider multiple aspects of success and establishing measures to represent each. Then look at them together so if you see one improve, you can make sure a negative impact didn’t happen elsewhere. For the curious, here’s what came next for our HR team: We identified 3 major things that would help this team improve their Cycle Time metric, and maybe even their Throughput metric too: Break work items down into smaller valuable pieces. Many of the items took so long because they simply had too many deliverables in them. Many process dysfunctions can be helped by breaking work down. Remove board columns that weren’t necessary. Every board column is a place to store more work-in-progress. There’s always a balance to be struck but it is ok to get rid of columns if they cause more pain than gain. This also meant we talked about not planning too early. Implement WIP limits. Using Velocity or Throughput as a planning tool for a sprint provides a WIP limit for your sprint backlog in that you limit the number of items you allow into a sprint. However, in a sprint, limiting the number of items you start at one time can often cause items to finish earlier and at a steadier pace throughout the sprint.

- Monte Carlo Simulations and Forecasting
When you hear Monte Carlo you probably have thoughts of the Formula One Grand Prix and extravagant casinos. At 55 Degrees, when we talk about Monte Carlo Simulations we are talking about forecasting. 🤓 What is a Monte Carlo Simulation? Here's a great definition from Investopedia Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. It is a technique used to understand the impact of risk and uncertainty in prediction and forecasting models. (Investopedia) Monte Carlo simulations are widely used in many industries whenever uncertainty exists. Investment firms use them to project potential earnings across different investment options. Insurance companies use them to forecast risk in different populations or areas. Companies like yours can use them to forecast when you'll finish a group of work! Simulations help make sense of uncertainty Rarely can you give someone a forecast and be absolutely, positively certain that you'll be right. There are just too many uncontrollable factors that can get in the way. When we are uncertain about something, one way to learn more is to run experiments. Are you unsure how likely it is that a flipped coin will land on heads? To find out, you can flipping it over many times and use the results to calculate the likelihood. Do you need to know how long it will take to complete a specific work item? You can use your past cycle times and calculate how long it's likely to take. Do you need to know when you'll finish a group of work instead of a single item? That's a bit more complex! Instead of manually doing groups of work repeatedly in real-time, we use Monte Carlo simulations to simulate doing the work thousands of times. Fortunately, it only takes seconds! Two questions you can answer When you're forecasting with Monte Carlo simulations you're likely trying to answer one of these two questions: When will this specific number of work items be completed? (Fixed scope) How many items can we complete by this specific date? (Fixed date) Projects aren't the only situations that require us to answer these questions. You may need to forecast when you'll get to an item that's near the top of your backlog or you may need to help decide how many items to plan for in your upcoming Sprint. I'm sure you can think of more if you stop and think! Running a Monte Carlo simulation To run a Monte Carlo simulation you'll need to provide a few things: A start date - Just as your GPS can't tell you when you'll arrive without knowing when you'll be leaving, the Monte Carlo can't tell you when you're likely to be finished if you don't provide a start date. Throughput data - The simulation samples real Throughput data to project how much you might finish on each day of every trial run by the simulation. There are thousands and thousands of these trial runs in each simulation. The outcome of each of those trial runs is recorded and allows you to calculate the odds of what might happen in the future. The fixed aspect - This is the desired end date when you have a fixed date or the number of items when you have a fixed scope. Using the results to create a forecast The tool you use for running the simulations controls how the results are presented. No matter how it is presented, it should provide you with the tools to create a probabilistic forecast - something like "There's an 85% chance that we'll finish in on or before August 4th." or "There's a 90% chance that we can do 15 or more items by ." Like those above, every probabilistic forecast should have two pieces of information (shown in bold above): a probability a range of outcomes The results can be shown in various ways depending on the tool you use. Here are two that we use in ActionableAgile: Histogram view - this is what you might think of as the raw data view. It shows the different outcomes that happened and the number of times each one happened. With this chart, it is quite simple to calculate probabilities. To find the 50% line, simply find the place where 5000 trials have taken place and then draw a line. For 85%, keep going until you find 8500 trials and do the same. Calendar view - this is a user-friendly view of the information in the histogram. This view makes it simple to talk about other dates what it might take to change the odds of hitting those. Considerations to keep in mind Monte Carlo simulations only factor in conditions that were present when you generated your historical data (team size, skill set, work policies, etc.). If those conditions significantly change, you'll need to generate new data under the new conditions in order to have reliable forecasts using Monte Carlo simulations (as you would with any forecasting method that uses historical data). Monte Carlo simulations often use random sampling of your historical data during the trial runs. When this is the case, as it is with our products, this means that every data point is as likely to be randomly selected as another. If you deliver 0 items most days, then most days in the simulation are likely to also have 0 delivered. In other words, you can deliver 10 items in the span of a week, but it matters how predictably those are distributed. You'll get better results if you work to have a more consistent delivery of a couple each day than aiming to deliver nothing for 4 days and 10 items on the fifth. To improve your predictability, and the resulting forecasts, focus on reducing Work Item Age. Want a tool to help you get started with Monte Carlo Simulations? You've definitely come to the right place. 55 Degrees offer two different products that use Monte Carlo Simulations to provide forecasts: Portfolio Forecaster for Jira and ActionableAgile (available as a SaaS app or embedded in Azure DevOps and Jira). In ActionableAgile you can use Monte Carlo Simulations to provide forecasts for fixed scope and fixed date efforts. Portfolio Forecaster uses Monte Carlo simulations to forecast your Jira Epics or Versions, taking into account how many are in progress at one time and your historical Throughput. Start now by trying either product for 30 days at no cost. We're always here to support you if you have any questions. Happy forecasting!

- What is a Cycle Time Scatterplot?
The Cycle Time Scatterplot chart is arguably the best way to view your Cycle Time data - the total elapsed time it took for individual items to move from one point of your workflow to another - usually from start to finish. Why is it the best? Because Cycle Time is all about time and the Scatterplot lets us see Cycle Time data in the context of time. The position on the horizontal axis tells us when the item was finished and the position on the vertical axis tells us how long the item(s) took. What can I learn from this chart? First and foremost, you can find the cycle time of an individual piece of work You can also see at a glance if your cycle times are getting more or less predictable by looking to see if the range of Cycle Times is increasing or decreasing You can see how long it took to complete work items in the past and use that to realistically forecast expectations for how long a work item may take to complete in the future. You can use this information to set a Service Level Expectation (SLE) for your team. This can be useful as an internal team metric to use in the context of current work item age to help maintain or improve predictability for the future. Additionally, you can learn about your process and the work that goes through it by exploring the clustering patterns of dots as well as the empty space on the chart. Asking questions about why the chart looks the way it does helps you learn about the impacts of certain decisions and events so you can make better decisions in the future. The Cycle Time Scatterplot in ActionableAgile The Cycle Time Scatterplot is the chart that you land on when you load ActionableAgile because it is the one that most people begin with on their journey to better flow. Excited to explore flow with your team? Try ActionableAgile for free today and reach out if you need any help via our support portal.

- What is probabilistic forecasting?
A probabilistic forecast is one that acknowledges a wide array of possible outcomes and assigns a probability, or likelihood of happening, to each. This makes it the perfect method for forecasting in uncertain situations like at work! What makes a forecast probabilistic? Every probabilistic forecast should have 2 components: a range and a probability. In the image above you see that there's a 15% chance that it will rain sometime between 12:00 and 13:00. It's not saying that it will rain the entire time. Just that, there's a 15% chance that sometime in that hour you will experience some rain. This also means that there's an 85% chance that you won't. What data is needed for probabilistic forecasting? When it comes to probabilistic forecasting at work, you're usually trying to answer one of these questions: When will this piece of work be done? aka How long will it take? (Single work item) When will this collection of work be done? (Multiple work items - fixed scope) How much work can we complete by a specific date? (Multiple work items - fixed date) To answer these questions you need to know some basic information about your past work: when each item started and when it finished. With this minimal data you can learn a lot about your system and what it can produce. Keep in mind this underlying rule of thumb: the conditions you had when you generated that data need to be roughly similar to the conditions you expect for the period you're forecasting. When you do this, you can use your data to forecast what is likely to happen in the future. How do I forecast an individual work item? To know how long it is likely to take for an individual piece of work to be completed, you want to look at how long it has taken you to complete work in the past. This data is called your Cycle Time. You can look at this data on a Cycle Time Scatterplot to quickly see what percentage of item finished in a certain range of time. The percentage you choose becomes your probability (component 1) and the range (component 2) is all the possible cycle times up to and including the line. You can see from the data above that 85% of work items finished in 16 days or less. You can turn that into the following probabilistic forecast: There's an 85% chance that you'll finish a work item in 16 days or less. By the way, this means from when it starts! What about larger efforts? If you need to provide a forecast that includes more than one item you can't just add individual forecasts together. You need to understand the rate at which you finished work in the past. Fortunately, that's exactly what the flow metric called Throughput tells yo. However, it is not as easy as looking at your Throughput data on a chart as you can with Cycle Time. If you use the Run Chart for this you can only look at what's likely to happen for one time unit. For most of your forecasts we'll need more than one of those. 😃 So, in these situations you can use a tool called a Monte Carlo simulation. (Learn more about Monte Carlo simulations) How do I forecast for a fixed scope? Sometimes you're trying to find out when a specific amount of work can be completed. That's what we call a fixed scope forecast. Entering a start date and the number of items you have in scope into a Monte Carlo Simulation can help you see how likely you are to finish that scope of work on any given day and it can tell you the probabilities of a range of outcomes using your data. For example, from that data in the image above you can say "There's an 85% chance that we'll finish this scope of work on or before May 11th". If that's not ideal you can look to other dates to see how likely they are and then have conversations about what you'll need to do to make that more likely or, perhaps, have discussions about changing expectations to be more realistic. How do I forecast for a fixed date? If you're working towards a fixed date rather than a fixed scope, the process is almost exactly the same but with one tiny twist. Instead of providing the number of items you have in scope, you provide your fixed date as a finish date. Now it can tell you how many items you are likely to finish by that date with any given probability. With the data from the image above, I can provide a probabilistic forecast: There's an 85% chance that we can finish 19 or more items by July 6th. Can I forecast my portfolio probabilistically? Probabilistic forecasting can be applied at a portfolio level using the same concepts but with some different tooling. At 55 Degrees, we forecast our portfolios using Throughput data and Monte Carlo simulations as explained above but there's an added consideration of how many multiple efforts we have happening concurrently alongside our Throughput and our chosen probability. Our simulation provides information such as how likely we are to finish a given effort by a fixed date and when we're likely to finish based on what remains in the effort and our recent throughput. Learn more about our Portfolio Forecaster here. Forecasting is not a one-time affair Meteorologists don't just give you a forecast for an upcoming storm when they first hear about it and then leave you without updates, right? You don't check your GPS before you leave to find out how long it will take and then shut it off do you? No, of course not. That would be silly. You'll absolutely want to re-run your forecasts regularly to see how they are affected by current conditions. This ensures that you find out any shifts as early as possible and minimize late surprises. (Help us make #continuousforecasting trend!) What are the benefits of probabilistic forecasting? Simply put, probabilistic forecasts are more inexpensive than traditional methods requiring expert estimation and work breakdown because they take very little time. Because they are cheaper, it is easier for you to provide those needed updates to your forecasts! Probabilistic forecasts are also more accurate because your data already accounts for factors that we struggle to incorporate into our estimates. Read about the German Tank Problem for an example of this. Cheaper. More accurate. No-brainer! In fact, as long as we are winning on even just one of those (and not sacrificing too much of the other) then it is worth a switch. But, you don't have to take anyone's word for it. It's easy to start doing probabilistic forecasting alongside whatever methods you traditionally use. That way you can see for yourself what works better for your context. Want to get started? You can do all of these forecasts by hand (or with excel). However, it might get a bit tedious. So, of course, 55 Degrees has an app for that and you can try them out for free for at least 30 days (or more depending on your platform). Check out ActionableAgile and Portfolio Forecaster today and reach out to us if you have any questions! Frequently asked questions Does all my work need to be the same size? Many people think that probabilistic forecasting won’t work for them if their work is varied in type or size. Your work doesn’t have to be the same size at all for this to work. Obviously, variation will cause the spread of possible outcomes to be wider. If you're not happy with the spread in the range, you can work to improve your process predictability. This will require you to consider many things about your process, one of which may be right-sizing work. However, if the data that goes into the Monte Carlo simulation reflects the variety of your work, the generated forecasts will reflect that variety as well. In fact, your data does a better job at reflecting all of the variety across the various conditions that impact your data than you ever could on your own! My forecast was wrong! What gives? The words “right” and “wrong” when it comes to forecasts should be re-evaluated. Using the language of probabilities reminds us that something unexpected can happen and disrupt our desired timelines. In truth, the only things we can be certain about are the things that have already been delivered. Outside of that, there is no 100% in a probabilistic forecast. If we said there was an 85% chance we would deliver work in 13 weeks or less and it took us 15, it doesn’t mean that we were wrong. We stated upfront there was a 15% chance that work would take longer. OK, am I really ready for probabilistic forecasting? Yes! Any team, even new teams, can use this type of forecasting. This feels like an advanced concept but it really isn’t. It’s just very different than what we’re used to. Don’t have historical data? Use estimates until you finish some work and then switch to using historical data.

- Analyzing Throughput in ActionableAgile
Throughput is a flow metric that tells us about the rate work items are finished in a given process. ActionableAgile has multiple charts that can give you information about your Throughput: Throughput Histogram Throughput Run Chart Cycle Time Scatterplot Cumulative Flow Diagram The first two are specifically made to relay information about the Throughput of your process. The last two happen to tell us throughput as a byproduct! Want to learn more about Throughput in general? Check out our "What is Throughput?" blog post. To learn more about these four charts in ActionableAgile, keep reading. Histogram The Throughput Histogram is a bar chart that displays how often you experience certain daily Throughput – in other words, the frequency of Throughput values. You can use the histogram to see what throughputs are most likely for ONE given instance of your time unit - one day or one week, etc. This is often not sufficient enough for forecasting across multiple instances of your time unit (multiple days, weeks, etc.). Read more about our Throughput Histogram in our product documentation. Run Chart The Throughput Run Chart is a line chart that shows you the variation in your Throughput data over time. This is, hands down, the best chart to use for straight Throughput analysis because of the time axis. We believe that all time-based metrics are best analyzed on a time-based chart. Time-based charts allow you to see patterns in your data over time and ask questions to learn more about how your team worked and why. You cannot discern this pattern-based information in a histogram. Read more about our Throughput Run Chart in our product documentation. Cycle Time Scatterplot The purpose of the Cycle Time Scatterplot is to tell us all about a different flow metric called Cycle Time. However, as the Cycle Time Scatterplot has data points representing all finished work across a time axis, we can look at those points and indirectly calculate Throughput values. In the Scatterplot, you'll toggle on the Summary Statistics box via the Chart Controls. In the example above, you can see that 305 work items were completed in 106 days. As you use other chart controls, including the date or item filters, the summary statistics will update so at any given time you see the total throughput for a set number of days. You do not see how the throughput values change over time as you do in the Run Chart. Read more about our Cycle Time Scatterplot in our product documentation. Cumulative Flow Diagram The Cumulative Flow Diagram is a stacked area chart that is built by adding information from a daily snapshot of your process each day. One of the things you can see in the Cumulative Flow Diagram is how many items left one part of the process and entered the next part. Because Throughput is defined as the number of items that finish in a given unit of time, you can get Throughput information by looking at how area band that denotes your "finished state" is changing over time. However, the CFD doesn't provide this information for you at a glance. That's what the Throughput Run Chart is for. The other related information you can get from the CFD is the average throughput, also known as the average departure rate. You see this by turning on the rate lines. Read more about our Cumulative Flow Diagram in our product documentation. In summary... There are many ways to learn about the Throughput of your process in ActionableAgile. So, here are our suggestions: Use the Throughput Run Chart for seeing how your Throughput changes over time. Use the Cumulative Flow Diagram to see how Throughput interacts with other flow metrics. Finally, use Monte Carlo simulations that work with your Throughput data to forecast efforts containing multiple work items. Excited to explore flow with your team? Try ActionableAgile for free today and reach out if you need any help via our support portal.

- Analyzing WIP in ActionableAgile
WIP (or Work In Progress) is a flow metric that tells us how many work items are in progress at any given time in any process - that is items that have started but not yet finished. Once you know how to measure WIP, you will want to start analyzing the data. There are three charts in ActionableAgile that provide insights into current and past WIP levels. WIP Run Chart Aging Work in Progress Chart Cumulative Flow Diagram WIP Run Chart The WIP Run Chart is a line chart that shows the number of items in progress per day across time. With this ability to clearly see how WIP levels change over time, you can get early signals of changes in Cycle Time and Throughput - for better or worse! This allows you to have better conversations about the impact of WIP on your process. Learn more about the WIP Run Chart in our product documentation. Aging Work in Progress Chart Another chart where WIP can be seen is the Aging Work in Progress chart. The primary purpose of this chart is to analyze another flow metric, Work Item Age, but you can also calculate WIP for the day being viewed. While you can click on a dot in the WIP Run Chart to see which items were in progress on a given day, this chart allows you to see more details about the WIP from any given day in greater detail. From here you can see what workflow status each work item is in as well as the age of each work item. On this chart you can use Aging Replay control to see this information about WIP for any day reflected in your data. Learn more about the Aging Work In Progress Chart in our product documentation. Cumulative Flow Diagram The final chart that provides insight into WIP within ActionableAgile is the Cumulative Flow Diagram. This chart provides a visualization of the interplay between WIP, Cycle Time, and Throughput. The height of the color bands in the CFD show you an actual count of items in each workflow stage on any given day. You can use the chart’s WIP Tooltips control to show WIP by stage, or collectively as a system, as your cursor moves through the timeline. By looking at the thickness of the color band(s) over time, you can see how WIP changes and the correlating change in Approximate Average Cycle Time and Average Throughput. You may even be able to help determine good WIP limits by looking how much WIP you had when Throughput and Cycle Time were ideal. Learn more about the Cumulative Flow Diagram in our product documentation. In Summary... There are many ways to learn about the WIP in your process with ActionableAgile. So, here are our suggestions: Use the WIP Run Chart for seeing how your WIP changes over time. Use the Aging Work in Progress Chart to learn more about the WIP from any given day. Use the Cumulative Flow Diagram to see how WIP interacts with other flow metrics and decide on any adjustments you might need to make in your WIP levels. Excited to explore flow with your team? Try ActionableAgile for free today and reach out if you need any help via our support portal.

- Managing Work Item Age in ActionableAgile
Work Item Age is the elapsed time since the work item started. It is one of four key flow metrics alongside Cycle Time, Throughput, and WIP. Of the four flow metrics it is, arguably, the most important because controlling age is a key way to improve process predictability. ActionableAgile provides a feature-rich Aging Work in Progress chart to help you measure and control Work Item Age. The Aging Work In Progress Chart The Aging Work in Progress chart is a lot like a visual board as the columns reflect your workflow stages and the items show as dots in the appropriate column. The vertical placement of the dot reflects the items Work Item Age. A dot may reflect more than one work item if they are in the same workflow stage and have the same Work Item Age. How to use the chart to manage Work Item Age Only while an item appears on this chart can you exert any control over where it will end up in your Cycle Time data. If you look at the last column of this chart you will notice that there are no work items represented. When an item reaches this workflow stage, it is complete and appears as historical data on your Cycle Time Scatterplot instead. Nothing you do now can change how long it took to complete that item. Because you use Cycle Time data to answer “How long will it take?” for a single work item, Work Item Age should be a key consideration when making your plan for the day. But, knowing the age of a work item isn’t enough information on its own. In order to know if the age of a work item is bad, good, or indifferent you need context. ActionableAgile overlays percentile lines from the Cycle Time data to add this context right where you need it. In the image above below you can see that 85% of past items have finished in 16 days or less. Now, you can keep that in mind as you track work items and make daily plans. If you want to maintain that level of predictability, you’ll need to continue to finish 85% of work items in 16 days or less. Getting early signals of slow work It's easy to know if an item near the end of the workflow is in danger of finishing beyond the desired age. Knowing that about items early in the workflow is more difficult. ActionableAgile’s pace percentiles help provide early signals that work is moving at a slower pace than past work. Learn more about the Aging Work in Progress chart and the various chart controls in our product documentation. In Summary... If you can only measure and manage one thing, make it Work Item Age. At its core, Work Item Age is a process improvement metric. When you see items aging more than expected, you can experiment with tactics to see if they help. There is no single fix but common tactics include limiting WIP, controlling work item size, reducing dependencies, and more. Once you manage Work Item Age, your Cycle Time data should stabilize and make forecasting easier! Excited to explore flow with your team? Try ActionableAgile for free today and reach out if you need any help via our support portal.

- When an Equation Isn't Equal
This is post 1 of 9 in our Little's Law series. Try an experiment for me. Assuming you are tracking flow metrics for your process -- which if you are reading this blog, you probably are -- and calculate your average Cycle Time, your average Work in Progress (WIP), and your average Throughput for the past 60-ish days. [Note: what data to collect and how to turn that data into the four basic metrics of flow is covered in a previous blog post]. The exact number of days doesn't really matter as long as it is arbitrarily long enough for your context. That is, if you have the data, you could even try this experiment for longer or shorter periods of time. Now take your historical average WIP and divide it by your historical average Throughput. When you do that, do you get your historical average Cycle Time exactly? Another quick disclaimer, for the purposes of this experiment, it is best if you don't pick a time period that starts with zero WIP and ends with zero WIP. For example, if you are one of the very few lucky Scrum teams that starts all of your Sprints with no PBIs already in progress, and all PBIs that you start within a Sprint finish by the end of the Sprint, then please don't choose the first day of the Sprint and the last day of the Sprint as the start and endpoint for your calculation. That's technically cheating, and we'll explain why in a later post. You've probably realized by now that we are testing the equation commonly referred to as Little's Law (LL): CT = WIP / TH where CT is the average CT of your process over a given time period, WIP is the average Work In Progress of your process for the same time period, and TH is the average Throughput of your process for the same time period. It may seem obvious, but LL is an equation that relates three basic metrics of flow. Yes, you read that right. LL is an equation. As in equal. Not approximate. Equal. In your above experiment, was your calculation equal? My guess is not. Here's an example of metrics from a team that I worked with recently (60 days of historical data): WIP: 19.54, TH: 1.15, CT: 10.3 In this example, WIP / TH is 16.99, not 10.3. For a different 60-day period, the numbers are: WIP: 13.18, TH: 1.03, CT: 9.1 This time, WIP / TH is 12.80, not 9.1. And one last example: WIP: 27.10, TH: 3.55, CT: 8.83, WIP / TH is 7.63, not 8.83. Better, but still not equal. If you are currently using the ActionableAgile tool, then doing these calculations is relatively easy. Simply load your data, bring up the Cumulative Flow Diagram (not that I normally recommend you use the CFD), and select "Summary Statistics" from the right sidebar. Here is a screenshot from an arbitrary date range I chose using AA's preloaded example data: From the above image, you'll see that: WIP: 26.40, TH: 3.04, CT: 9.48 However, 26.40 / 3.04 is 8.68, not 9.48. As evidence that I didn't purposefully select a date range that proved my point, here's another screenshot: Where 28.11 / 3.51 equals 8.01, not 8.86. In fact, I'd be willing to bet that in this example data -- which is from a real team, by the way -- it would be difficult to find an arbitrarily long time period where Average Cycle Time actually equals Average WIP divided by Average Throughput. Just look at the summary stats for the whole date range of pre-loaded data to see what I'm talking about: 21.21 / 2.31 equals 9.18, not 9.37 -- still close, but no cigars. I'd be willing to bet that you had (or will have) similar results with your own data. If you tried even shorter historical time periods, then the results might even be more dramatic. So what's going on here? How can something that professes to be an equation be anything but equal? We'll explore the exact reason why LL doesn't "work" with your data in an upcoming blog post, but for now, we'll actually need to take a step back and explore how we got into this mess, to begin with. After all, it is very difficult to know where we are going if we don't even know where we came from... Explore all entries in this series When an Equation Isn't Equal (this article) A (Very) Brief History of Little's Law The Two Faces of Little's Law One Law. Two Equations It's Always the Assumptions The Most Important Metric of Little's Law Isn't In the Equation How NOT to use Little's Law Other Myths About Little's Law Little's Law - Why You Should Care About Daniel Vacanti, Guest Writer Daniel Vacanti is the author of the highly-praised books "When will it be done?" and "Actionable Agile Metrics for Predictability" and the original mind behind the ActionableAgile™️ Analytics Tool. Recently, he co-founded ProKanban.org, an inclusive community where everyone can learn about Professional Kanban, and he co-authored their Kanban Guide. When he is not playing tennis in the Florida sunshine or whisky tasting in Scotland, Daniel can be found speaking on the international conference circuit, teaching classes, and creating amazing content for people like us.