Once upon a time, I decided to join a digital signage startup.

Back then, Nave was a software development agency and our purpose was to help startups drive their development efforts in the right direction.

The company was aiming to deliver the first streaming service for public displays. Developing a cutting-edge digital signage solution that aims to disrupt the status quo is a big deal, especially if your entrepreneurial initiative is just starting out.

The problem with the current value propositions on the market was that they didn’t provide the possibility to present real-time content. The startup recognized this opportunity and they were very well-positioned to make a huge impact in the digital signage industry.

The cross-functional team consisted of 5 people, who were managed directly by the two founding partners of the company. And what the partners wanted was to have full confidence in the team to hit their targets.

A Sneak Peek Into The Reality

Let me give you some more context.

The demand was overwhelming. Every day a new priority emerged and it wouldn’t necessarily follow any established approach, or aim to bring either customer value or transparency to the decision-making process.

The team had adopted Scrum, but the implementation of the framework was really poor. The thing is, teams can only experience the full benefits of Scrum by understanding and adhering to the values and principles the framework promotes. And here, that simply wasn’t the case.

This is what a typical business day looked like: the managing partners came up with an idea, it felt interesting, and so it was pushed onto the team while they were in the middle of a sprint. There was no attention to the amount of work the team was already handling, and so tasks that were already in progress were constantly being interrupted. And finally, there was an expectation that all of the work should be delivered within the current sprint.

The team was constantly switching their context, suspending what they’d been working on, and starting the new idea that had just arrived. They stayed up till 5 in the morning to be able to meet the impossible deadlines. But, despite their efforts, they were always late. A huge amount of the scope of the current sprint was being moved to the next sprint, and that was happening over and over again.

And the fact is, they simply did not have the capacity to handle the constant stream of new requests. At one point, things became really tough. The managing partners were feeling super frustrated, because the team never managed to deliver the work they’d committed to on the release date, the quality of the deliverables they managed to finish was quite poor and the development cost went through the roof.

I assume it’s needless to say that the engagement and the motivation levels of these people were really low. Fingers were being pointed at each other all the time, the working environment was toxic. The team felt like their work never brought any value. There was no appreciation, trust, or respect. One after another, they started to resign.

It’s Time for a Change

Something had to change immediately! I was actively exploring the Kanban Method at the time, and it felt like exactly the approach we needed. All the concepts behind the method resonated with our current situation.

And probably the main strategy that sold me on Kanban was the data-driven approach to decision-making that it recommended.

It was time to bring some order to the chaos.

Back then, we used Trello to track our work, and there was no Kanban Analytics solution on the market that integrated with this platform. Switching to a Kanban tool was out of the question, so we decided to build our own internal solution. The star of Nave was born.

As we kept building more and more Kanban analytical charts that enabled us to analyze our performance and understand the root cause of the problem, it became obvious that the team needed to align the demand with their capacity and focus on finishing the outstanding work, rather than starting any new work.

At the very beginning, the first thing we did was discard all the aborted work items on the board. This was incredibly difficult for the team; they’d spent their nights and weekends working on these tasks, and it felt like all their effort was for nothing. But, they knew it was the start of a new beginning, so they were still really open-minded about the change.

The next step was to limit the work in progress and implement a Kanban pull system. Let’s dive deeper into the message our data communicated, and how it enabled us to head in the right direction.

Your Data Is Talking to You, Listen

To optimize your workflow performance, you need to break down your delivery times and evaluate the improvement opportunities. This is best achieved by using the Cycle Time Breakdown Chart.

The chart displays the cycle times of your completed tasks split by process state. By analyzing the different sections on the bars, you can assess how the time spent in each state affects the overall time needed to finish your work.

For example, here the data is split by week. In the week Jul 16th – Jul 22nd, tasks spent a significant amount of time in the light pink zone, corresponding to the Code Review (Done) state.

Following on from this analysis, there were two main objectives for the team that came up.

1. They knew that a tremendous amount of the time their work spent in Development was actually caused by suspending what they’ve started. They decided to stop multitasking and only start new work when they had finished their old work.

2. There was an obvious bottleneck in the Testing state. 1 QA was definitely not enough to handle the work produced by 4 developers. As a result, the work was piling up in front of the Testing state – which explained the huge amount of time the work items spent in Code Review (Done).

They decided to allocate multiple developers to the testing phase, so that they could handle the demand and enable the work to move forward. The focus was set on finishing the work, rather than starting new work. And from the beginning of August, you can see the results – these sections were reduced by about 4 times. And that was a huge motivation boost for the team.

You can see what the Aging Chart of the team looked like back in June 2018. There was a high number of aging work items either being blocked by an external dependency or simply being neglected, due to another high-priority item.

And this is what it looked like after we started managing the flow of work effectively. Essentially, what we did was keep track of how much time each work item spent in progress to monitor their WIP age and then follow up on anything that went above the 70th percentile.

The results? A steady flow of work and no more aborted tasks in the middle of the workflow. The entire workload was positioned below the orange zone, which meant our current work took less time than 85% of our previous work.

I want you to take a moment and acknowledge how the ultimate performance improvement that we’d managed to achieve actually had nothing to do with how individuals were performing. It was all about resolving the obstacles that prevented us from delivering on our commitments.

Our throughput became much more consistent, too. Let’s analyze the Throughput Run Chart.

The chart displays the throughput of your team for a specific time frame – you can group your data by day, week, 2 weeks, or month. The horizontal axis represents a timeline, while the vertical axis shows your throughput. Each bar consists of colored sections representing the type of completed work.

Here, we see that up until the end of July, the throughput of the team was very inconsistent. For many weeks, they only managed to keep it at a high level because they were working overtime and pushing themselves beyond their limits to be able to deliver.

After they limited their work in progress and adopted a pull system, the overburden was eliminated and the team went back to normal working hours. The workload was aligned to their capacity. The total working time reduced and as a result, the development cost reduced as well. Their throughput, though, increased and it became much more consistent and predictable.

I have to tell you, at the beginning of all this, the two founders felt really uncomfortable. Knowing that everything was delayed, they felt the urge to push even more work to make up for the delays. Now, they had to put a stop which, naturally, met resistance.

They only managed to shift their mindset once they actually saw the results – the reduced delivery times, the increased throughput, and most importantly, the predictability and the consistency we managed to achieve. Finishing work in progress before starting new work was fundamental for the business.

These approaches were so successful, and they led to such an amazing improvement in the team’s performance and the organizational culture, that I decided to move on and fully dedicate my time and energy to developing the concept of Nave and providing a bespoke value proposition to the market.

You Have the Power to Make a Difference

And this is what I really want you to understand.

If you are struggling to deliver on your commitments, if you want the process of estimating to be easier, less time consuming and to actually work… If you wish to deliver results in a consistent predictable manner, without overburdening your teams…

If you’re tired of the overwhelming stream of incoming requests… If you see how your team falls into the throes of full-fledged burnout while trying to meet impossible deadlines… If you’re looking for reliable approaches to set up and manage realistic goals… It’s time for a change!

It’s time to take control of your management practices and start making reliable decisions!

If you’re willing to explore the proven roadmap to building predictable delivery workflows, I’d be thrilled to welcome you to our Sustainable Predictability program!

You have the power to make a difference. Use that power!

Start as early as tomorrow morning. Don’t delay! After all, continuous improvement builds over time, with one small win following another.

Commit to promoting evolutionary change and a culture of continuous improvement. Commit now. What is the next small step you’ll take to make a difference?

Thanks for checking in and I look forward to seeing you again next week, same time and place for more managerial goodness! Bye for now.

The post This Is My Story… What’s Yours? appeared first on Nave Blog: Expert tips and guidelines for agile teams.

It’s been a busy year for us here at Nave, so it’s time to let our hair down, have some fun & celebrate the end of a successful year. But, none of it would have been possible without your trust and help. To that end, we’d like to extend a big thank you to everyone who supported us during this incredible year!

To Our Customers

We have always put our customers first, which is why we’ve worked hard to bring you a solution that helps improve your workflow and make your processes more efficient. Not only are you the driving force behind everything we do at Nave – you’re the very reason we exist. Right from the start, our goal has been to bring value to you as soon as possible. Thank you for your trust! We won’t let you down!

To Our Team

Any successful project is driven by the power to collaborate and communicate without limitations and across geographical boundaries. We would like to say special thank you to our team all around the world. Thank you for your motivation, hard work and dedication. Our success wouldn’t have been possible otherwise. Thanks guys – you’re amazing!

To Our Partners

We’d also like to thank all agile coaches who have believed in us and shared our success story over the past year. With your help, we’ve managed to transform Nave into a respected brand and a trusted provider of solutions for agile teams everywhere. Thank you for the reliable team work!

To the Teams Behind the Platforms We Integrate With

Without the people behind all the products we integrate with, our solution simply wouldn’t make any sense. That’s why we’d also like to thank you for building the platforms that our dashboards work with. Your products help agile teams stay organised and collaborate effectively on any project. Together, we provide the ultimate solution for successful teams to deliver value as quickly as possible. We are happy to work with you! Keep up the great job!

To the Kanban Community

We developed our product to drive success among software development teams across the globe. We’d like to thank the Kanban community for their insights and support and their ability to drive the trends shaping our solution. To all those who share knowledge and help us keep our sense of direction, thank you for your help!

To Our Families

Over the past year, we’ve been working hard to build our visual analytics tool to help agile teams across the world increase their productivity, improve workflow efficiency and become faster, smarter and happier. It has cost us a lot of time, energy and dedication. We’d like to say a warm thank you to our families for being so patient while we’ve worked long hours to make that possible. We love you more than anything, and we wouldn’t have been able to make it without you!

What to Expect in 2026

Our job is never done. We’re here to deliver continuous value to our customers by listening to their feedback and focusing on their needs. That’s why we’re working hard to constantly improve our product. Our roadmap for 2026 includes releasing our brand new planning & forecasting, and prioritization & dependency management modules. Stay tuned and let us know which integration you’d like to see next in the comments below!

Wishing you great success in achieving your goals and dreams in 2026!

Merry Christmas and Happy New Year!

The post A Big Thank You from Nave! appeared first on Nave Blog: Expert tips and guidelines for agile teams.

It’s that time of the year once again. As we celebrate the close of one year and look towards the beginning of a new one, we’re all inclined to make a little bit of a recap. It’s a fresh new start, filled with exciting new opportunities and the perfect chance to learn from any little slip-ups (after all, we can leave these firmly back in 2025). 

We’ve all made a promise to our business and our teams. We’ve made a commitment to continuously improving our outcomes and changing how people feel about their work. We’ve been optimizing our workflows the whole year long; we’ve been increasing our performance and decreasing delivery times to grow loyalty and building up even more trust with our customers all the while.

But first, let’s go forward by taking a step back. Do you remember why you wanted to improve in the first place? What were the drivers that fuelled your decision to take on that transformation initiative?

Often, with all the energy and effort that goes into each day, we forget to think about why we’re putting in all this work. But you know what? That’s ok! Today, with the new year just about to start, we’re going to return to our initial purpose and remember why it was so important for us.

For those of us who can’t stay still for a second, forgetting why we’ve started is easy.

Let’s try to remember why we wanted to change. Was it because there is too much work to handle at a time? Or was it due to seeing our teams falling into a full-fledged burnout while working hard to meet unrealistic expectations? Did we struggle to hit our targets? Was it because we’ve lost the trust in our team’s ability to deliver or probably we were tired of always being late? Was it because we wanted more transparency, more predictability, more time to spend with our families, more joy of doing what we’re doing…

These are meant to be your reasons for starting down this path of change. But do you still remember them?

Today, we’re going to make a commitment together to stay present and show up differently in 2026.

Let me give you some personal context. Last week, I was reviewing a new digital course and suddenly I found myself opening a bunch of browser tabs. And then, I found myself looking at LinkedIn on my phone. And then my husband Hristo came in while I’m trying to listen, he sat down on the chair right in front of me and he wanted to chat. 

Wait for a second! I was supposed to be present in that session, and in fact, my mind was going in 20 different directions instead.

At that moment, I snapped out of it. I said to myself “this is not how I’m supposed to do things”. I will not be able to achieve what I wanted if I allow this to keep happening. I need to stay present, I need to remember my why. Every time I do something that’s taking me away, I say “today, I’m going to stay present, I’m going to show up different!”. 

When I go to my phone (cause I’m addicted), pick it up and start scrolling, what I remind myself is “I’m going to show up different today”! Just today, and I’m reminding myself that every single day.

Don’t lose yourself in multitasking or forget what’s important. Start being present. Do it once and you’ll feel so proud of yourself. What’s more, you’ll have accomplished so much more by the end of the day by just snapping out of it quickly and keeping your goals at the forefront of your mind. 

And yet, remember that we’re only human, so it’s ok to behave like one. 

When I feel nervous or overwhelmed by the day and this negativity gets stuck in my head, when Hristo sees it, he’ll say to me ‘Sunny, come back here!”. Right away, he snaps me back into the present. He pulls me out of this mind trap back to the present.

What I want to say today is “Come back here!”. You could have lost yourself somewhere along the way, but this is your invitation to remember your why and show up differently today. Play full out! Show up today. That’s my commitment that I’m going to make for myself and I want you to make it for yourself as well.

Embrace your goals once more and start making them come true!

You can’t imagine how much time and energy we waste by wondering and hesitating, searching for excuses, or letting our own insecurities take over. Instead – just be brave and make a difference.

Remember your motivation. What does success mean to you?

Is it more money? That’s fine, I’ve got nothing against money! Maybe it’s to manage happy and engaged people, maybe it’s to make realistic commitments and meet them every time, maybe it’s to do the right things the right way, or to leave the company culture a little bit of a better place than how you found it.

Think about what success means to you, and continue asking yourself that question throughout the year. Although your answer may change over time, do yourself a favor – whatever your answer is, don’t do anything that will jeopardize your goals. 

For the start of the new year, our message to you is this: 

Be present and always remember your why! 

From all of us at Nave, we wish you great success in achieving your goals and dreams in 2026

Merry Christmas and Happy New Year!

The post Our Christmas Wish For You! appeared first on Nave Blog: Expert tips and guidelines for agile teams.

It’s astonishing to see in practice how a simple pull strategy can improve the predictability of your delivery system so significantly, even after just a couple of months.

The Wizards are a development team, working for a start-up digital signage company, which delivers the first streaming service for public displays. Developing a cutting-edge digital signage solution that aims to disrupt the status quo is a big deal, especially if you’re just starting out with your entrepreneurial initiative.

The problem with the current value propositions on the market is that they don’t provide the possibility to present real-time content. The Wizards’ company recognized this opportunity and they were very well positioned to make a huge impact in the digital signage industry.

The Wizards are a cross-functional Scrum team consisting of 10 people, delivering batches of work every 14 days to their stakeholders. The stakeholders review the deliverables and provide feedback to adjust the course of action accordingly. The Wizards were proficient in implementing the Scrum framework and they were widely considered successful because they delivered a consistent velocity on a sprint-to-sprint basis.

However, from a flow perspective, there were inefficiencies in the Wizards’ development process. The fact that their delivery workflow didn’t operate effectively hindered both their delivery times and their predictability and the Scrum velocity metric would not have exposed this.

The stakeholders wanted to have high confidence that the team would be able to achieve their goals. And it was a matter of applying a simple pull strategy that led to a tremendous improvement in their predictability, all in just 2 months.

Analyzing the Predictability of the Wizards’ Delivery System

One of the most suitable approaches to demonstrating the predictability of a delivery system is using the Cycle Time Scatterplot.

The dotted horizontal lines stretching across the graph are called percentile lines. They represent the time taken for past work to be completed. In the Wizard’s diagram, the 50th percentile points to 10 days. This means that half of the work has been completed in less than 10 days. The other half though has taken up to 130 days to be finished. In 95% of the cases, the team needed up to 76 days to deliver the work they had started.

If you’re running 14-day sprints, these numbers are quite bad. If in 50% of the cases the work was finished within 10 days, this means that stories that started at the beginning of a sprint only had about a 50% chance of being completed within that same sprint. This is not a predictable system.

If you look closer at the values that come with each percentile, you’ll notice that there is a significant gap between them:

The closer the cycle times on your percentiles are, the more predictable your system is. So, the main goal of the Wizards was to reduce these gaps.

Looking into their Scatterplot, the team started to dig deeper into the root cause behind their long delivery times. It turned out that they were pulling the smaller, quicker work items first, while the bigger, more complex items artificially aged within the process. There was no pull strategy in place that would have enabled them to prevent that behavior.

Applying a Simple Pull Strategy Tremendously Improved the Wizards Delivery Times (in Less Than 2 Months)

The team decided to introduce a pull policy that dictates the order in which work items in progress should move through the workflow so that their stories don’t age indefinitely. The focus switched to analyzing the age of each item in progress and progressing with those with the highest age first.

The Aging Chart came in handy for that purpose.

The dots on the top move forward first. By following up with the items with the highest WIP age first, the Wizards can ensure that there is no abandoned or delayed work left in the process. Sometimes, this would mean that the items that were started later had to wait until the older items were completed first, but this is perfectly fine from a predictability perspective.

The team used this pull strategy to give priority to the items that spent the most time in their workflow in order to improve their predictability.

The results? After two months, their Cycle Time Scatterplot looked like this:

The team was able to get 95% of their stories completed in 13 days or less. Their delivery times at the 95th percentile were reduced by over 80%. This is what their cycle times looked like:

Can you see that the gap between the 85th percentile and the 95th percentile is only 2 days? This means that now the Wizards can deliver any item in 13 days or less and there is a 95% chance that they will meet that commitment. The predictability of their system, first and foremost, was dependent on how effectively they were managing the flow of work. 

If you’re interested in learning about how to build a stable predictable delivery system and meet your commitments by delivering on time, I’d be thrilled to welcome you to our Sustainable Predictability program.

DOWNLOAD THE WIZARDS USE CASE IN PDF FORMAT

Use this PDF as a reference when assessing your explicit policies and evaluating the predictability of your delivery system.

LEARN MORE

Keep in mind that the Wizards didn’t change how much time they spent working, how many people were working in the team, or the nature of the work itself. Nothing else changed. Applying this simple pull strategy of handling the item with the highest age first resulted in a tremendous improvement in the predictability of their system.

The Wizards have exceeded their stakeholders’ expectations ever since, delivering off-the-charts solutions to their customers in a stable, predictable manner.

Alright, my friend. Thanks for tuning in. I’ll see you again next week, same time and place for more managerial insights. Have a wonderful day!

The post How the Wizards Scrum Team Immensely Improved Their Predictability in Just 2 Months (all by Applying a Simple Pull Strategy) appeared first on Nave Blog: Expert tips and guidelines for agile teams.

Managing blockers effectively is the first step towards revealing the obstacles that cause delays and hinder your delivery speed. One of the practices that drive continuous improvement is blocker clustering. The blocker clustering technique enables you to track the impact that blockers have on your performance and prioritize those that affect your delivery times the most.

The blocker clustering analysis consists of the following steps – identify your blockers, capture your total blockers’ time, and determine the root causes of the most impactful blockers. Let’s go through each of the steps in more detail.

Identify Your Blockers

The first step towards evaluating the obstacles that block your work from moving further in your workflow is to identify your blockers.

At the moment a work item is getting blocked, think about the reason behind it and identify what is causing the work to get stuck.

Collect all the blockers and organize them into groups. The groups might be External Dependency, Internal Dependency or Expedite Request, just to name a few.

Create a custom field on your board i.e. Blocked Reason and add the groups you have identified as options to your new custom field. Now, when a new item gets blocked, you can associate it with any of your blockers. You can also modify the custom field to include new blocker groups to keep it up to date.

Once the card is unblocked, remove the custom field option from it. The goal behind adding and removing a blocked reason to the card is to track the time that each card spends assigned with a certain blocker to be able to evaluate the impact blocked time has on your overall delivery time.

Capture Your Total Blocked Time

The next step of the blocker clustering analysis is to prioritize the most impactful blocker that causes delays in your delivery. The trick here is to count the total days your work spent blocked and split that data into each of the blocked reasons you have identified.

Our Cycle Time Breakdown Chart collects that information automatically, in order to help you identify the blocker that is increasing your delivery times the most.

The above Cycle Time Breakdown shows that 38% of the blocked time is being caused by Unclear Requirements and a further 32% by Expedite Requests. These two groups are the most impactful groups of blockers that cause delivery delays.

Determine the Root Causes of the Most Impactful Blockers

What possible solutions can we suggest to resolve these impediments? A very effective technique that you can use to figure out the root cause of the problems is the 5 Why’s approach. You ask ‘why’ multiple times over, and each answer leads you to the next question in the chain.

Blocked reason: Unclear Requirements 

Why are the requirements supposedly unclear? Because a description of what is expected to be delivered has not been provided.

Why is that? Because there are no clear requirements on how a card should be structured before it is pulled into the system.

Why? Because we haven’t adopted concepts like Definition of Done, Definition of Ready or Acceptance Criteria.

What we have to focus on now is defining a clear DoD, DoR and AC, which would guarantee that the team knows exactly what they need to do to deliver results in a timely manner.

By eliminating this blocker, this team effectively reduced their delivery times by almost 40%. Drastically improving their overall performance all came down to a matter of introducing a new management practice.

Blocked reason: Expedite Requests

Why do expedite requests block work to such an extent? Because there are too many expedites in the system, which are constantly interrupting the work we have started.

Why is that? Because there is no clear definition of what an Expedite is, and most of the items just got assigned with this CoS based on intuition.

Why? Because we don’t prioritize our work based on the Cost of Delay and properly consider the risks that we have to manage.

Most likely, you need to look into the practices you have in place to better prioritize your work. Think about building a dynamic prioritization system based on the value your work brings to your customers, instead. In our Sustainable Predictability digital course, we explore the strategy of sequencing your work items, in accordance with the benefit they bring to your business, right from the moment you add them to your backlog. We’ve cut out the need to spend time reordering to-do lists.

By looking into the approach of prioritizing work based on the cost of delay and the market opportunities it realizes, this team has the potential to reduce their delivery times by a further 30%.

Manage Defects the Same Way You Manage Blockers

Although teams often track defects differently from blockers, defects can be clustered in exactly the same way as blockers, so you can investigate their root causes and work on their prompt resolution by going through these same steps.

Software defects are often the main obstacle to revealing the full potential of our solution to the market. Defects are expensive, especially when they make their way to production. The challenges behind developing quality software solutions, like constant market demand and lack of time, will remain. The cost of defects is likely to rise if we don’t commit to continuously improving our development practices.

To perform defect clustering, you can use the exact same approach that we described in the blocker clustering analysis, and use the Cycle Time Breakdown Chart to perform an analysis of the most impactful defect causes.

If you’re interested in building a delivery system that enables you to deliver on your commitments consistently, I’d be thrilled to welcome you to our Sustainable Predictability program.

Improvement is not a one-off task – it happens over time, with each new improvement building on the one before. Very often, small tweaks in your management practices will lead to a significant improvement in your performance.

Prioritizing blockers and continuously working upon the root causes that prevent your work from moving smoothly through your process is a highly effective approach to take towards reducing delivery times and fostering an environment of continuous improvement.

Thanks for checking in and I look forward to seeing you again next week, same time and place for more managerial goodness! Bye for now.

The post How to Reduce the Impact of Blockers on Your Delivery Times by Using Blocker Clustering appeared first on Nave Blog: Expert tips and guidelines for agile teams.

“What is the industry standard of good flow efficiency?”, great question!

Rather than just giving you an answer straight away, first, we need to spend some time going through the ins and outs of calculating flow efficiency and the outcomes that come with it.

Following the industry standards is a great approach to setting goals and thresholds, however, first, you need to make sure that your approach to measuring flow efficiency compiles to the industry standards as well.

So, let’s start with the basics. What is flow efficiency?

What is Flow Efficiency?

Simply put, as your work items are flowing through your process, they are in one of two states – they are either actively being worked on or they are not actively being worked on. Flow efficiency of a work item is the ratio of the active time that it’s being worked on against the total time that it took to get this work item done.

If the flow efficiency of a work item is 10%, this means that for 90% of the time it has just been stuck in your workflow, either blocked or waiting to be pulled to the next process state. For 90% of the time, no one was actually working on it.

Flow efficiency is used to measure the performance of your process. As a tool, the flow efficiency metric itself is extremely helpful for understanding the delay in your delivery system.

Now, let’s look into the prerequisites to accurately measure your flow efficiency.

The 3 Fundamentals to an Accurate Flow Efficiency Calculation

Even though it seems like a simple concept, accurately measuring flow efficiency is not a trivial task. To come up with a reliable number, there are three points you have to check off. Let’s go through each of them.

1. Enable a Kanban Pull System

The first step to measuring flow efficiency is enabling the concept of a Kanban pull system. The process states in your delivery workflow should be split into In Progress and Done (queue) states.

Once the work in an In Progress state is completed, it is moved to the queue state signaling to the next state that it is ready to proceed. When people finish what they’ve started, they pull work from the queue states.

Apart from having the system design in place, you also need explicit process policies that define when an item should move forward to the next step in the process. The rules have to be clearly defined to ensure consistent outcomes and so allow you to measure flow efficiency accurately.

2. Manage Blockers Effectively

Furthermore, you need an established approach to managing blocked work effectively.

When a work item in an In Progress state on your board gets blocked, this time is also considered as inactive time and when you’re making your flow efficiency calculation, it needs to be taken into account.

Again, you need process policies that outline what blocked work means, how you mark work items as blocked, what you should do when an item gets blocked, and so on. These rules have to be explicit so that everyone knows what needs to happen in this scenario.

This approach not only enables you to track flow efficiency accurately but also provides a transparent decision-making framework for how the team should handle different cases, without requiring your constant involvement. It creates alignment between everyone involved and sets the foundation for further process improvement.

3. Keep Your Board up to Date

Last but definitely not least, you need to make sure that the cards move through the board as the work moves through the workflow. Your board should represent reality and it should be updated in real-time.

To preserve the consistency of your management practices (and thus the outcomes associated with them), the team must follow the process policies.

Everyone needs to be aligned around the understanding that the board is a powerful communication mechanism and should be perceived as the single source of truth.

At the moment your cards no longer represent the actual state the work is currently in or if the work item is blocked and not marked as such, you will have already compromised the accuracy of your flow efficiency.

Only once you’ve checked off all the above can you say that you use a reliable approach to measuring flow efficiency.

The industry standard says that anything between 15% and 40% flow efficiency is good. Flow efficiency above 40% is considered exceptional.

Keep in mind that the flow efficiency % always includes non-working time. Weekends, holidays, leaves, all these days off should be included when performing the calculation.

One of the main benefits of tracking flow efficiency is that you can define a threshold upon which to focus your improvement efforts.

As a rule of thumb, if your flow efficiency is below 40%, to improve your delivery times you will need to focus on reducing the waiting time in your system. If it is above 40% and you’re still not happy with your delivery performance, you’d probably have to expand your system design, either upstream or downstream, to gain a better understanding of how that active time is actually spent.

How to Use Flow Efficiency to Trigger Opportunities for Improvement

Use the Flow Efficiency chart to measure your average flow efficiency and see how the trends move over time. To specify which states on your board you consider to be inactive, select them on the right sidebar.

A word of caution here! What I often see is teams trying to make delivery predictions by estimating the effort of their work. This approach is particularly risky in a low flow efficiency environment.

In a low flow efficiency delivery system, your active time (effort time) will represent a very small % of your total delivery time. If this is the case, keeping the focus on evaluating the active time in the workflow will land you in some hot water.

Using the low flow efficiency % to emphasize that estimating your work using hours (or story points) is not a reliable approach for making delivery commitments is a very powerful strategy.

If your delivery system doesn’t produce the results you are hoping for and you’d like to explore the proven roadmap to optimize your workflows for predictability, I’d be thrilled to welcome you to our Sustainable Predictability program!

You should put your efforts into identifying the items with the lowest flow efficiency (those on the left side of the chart) and analyzing them accordingly. Any item with a flow efficiency lower than 15% is a good candidate for examination.

Reveal the reasons behind the delays and take action accordingly. Is the fact that you are managing a highly specialized team slowing you down as the work is waiting for the subject matter expert to become available? Or are you constantly being blocked by a third-party dependency? Perhaps your quality assurance specialists can’t handle the workload at the pace your developers produce it?

Whatever the reason is, the best way to improve the efficiency of your flow is to dig down to the root cause of your delays and act accordingly.

Keep track of how the trends build over time and use flow efficiency to identify the weak spots in your management practices, as this strategy will immediately trigger opportunities for improvement. As long as you keep your approach to measuring it consistent, you’ll be in a good place.

That’s all for now. I’ll see you again next week, same time and place for more managerial goodness. Bye!

The post The 3 Fundamentals to an Accurate Flow Efficiency Calculation appeared first on Nave Blog: Expert tips and guidelines for agile teams.

What is the ultimate goal behind implementing WIP limits?

Usually, teams only start thinking about introducing this practice the moment they realize that their work has become unmanageable.

Their boards are brimming with tasks, customer requests keep coming in, and they are constantly switching the context trying to cater to everyone’s demand. The question “when will this be done” becomes impossible to answer without compromising their credibility.

If this sounds familiar, applying a limit on the amount of work that your team handles at any given time will be a game-changer. This practice enables you to move the focus from starting more new work to finishing outstanding work. It gives you all the means to significantly improve your delivery speed.

But how would you measure the impact of implementing WIP limits in the first place? When you’re just getting started, how do you know whether or not it’s working?

During the Transition, Your Positive Results Might Be Hidden

Once you put this practice in place, the ultimate goal is to decrease your delivery time. This result is also backed up by the relationship between the main flow metrics, as exposed by Little’s Law. The law states that limiting WIP will most certainly reduce your cycle time.

And the Cycle Time Scatterplot plays a crucial role here.

This chart visualizes all your completed tasks as dots scattered on a plot. Each task comes with the finished date and the time it has taken to complete.

Here is the thing, the traditional visualizations of the scatterplot use your items finished date as the value on the X-axis.

Why is this important?

When you apply WIP limits for the first time while your board is still overloaded with work items, the immediate effect of implementing WIP limits won’t be visible until you stabilize your system and release all the aging work.

Let’s look into the following example. This is the Cycle Time Scatterplot of a development team who were struggling to deliver on their commitments.

At the end of June, they introduced the practice of WIP limits and when they analyzed their charts a few weeks later, they were not delighted by the results.

In the period between the end of June (when they first implemented WIP limits), up until the end of July, their cycle times actually remained very inconsistent and even went up.

Here is why this happens. The items that appear on the chart with a very high cycle time are the aging items they have released. These items had already been started at the moment they adopted the practice.

They were not a result of implementing the practice, they were an after-effect of the unmanageable amount of items the team had already been handling by the time they initiated their change management initiative.

And with the traditional Cycle Time Scatterplot that visualizes your completed work items by end date, the immediate effect of implementing WIP limits will remain hidden.

So, how can you expose the positive results of this practice, to spark motivation and engagement?

How to Reveal the Immediate Impact of Implementing WIP Limits

There is a very simple trick that you can apply to be able to reveal how limiting your work in progress immediately improves your performance.

Instead of visualizing your data by end date, organize it by start date.

If you’re using Nave, you can just select “Start date” on the X-axis configuration on the right sidebar of your chart.

Looking into the Cycle Time Scatterplot organized by “Start date”, it became crystal clear that their delivery system was actually behaving in a very predictable and consistent manner right after the implementation of WIP limits.

You can see that the items that have been started right after the team introduced this practice have been finished in a much more consistent and predictable manner.

Before the change, their cycle time varied significantly. Now, once they focused on finishing their work before starting new work, the percentiles moved very close to each other, which is an ultimate indicator of good predictability.

Furthermore, their completed items were positioned below the 70th percentile which means that they can now commit to delivering any type of work within 14 days or less with very high confidence.

When you’re just starting out with your change initiatives, visualizing your analytical charts based on end date could be misleading.

Instead, switching to start date can give you the insights you need to understand whether your improvement efforts are actually paying off. Then, use that visualization to spark a conversation around the behavior of your delivery system.

And if you’re struggling to make reliable delivery commitments and hit your targets consistently, I’d be thrilled to welcome you to our Sustainable Predictability program.

Always remember that, without the context behind them, the charts are just pictures. The most important part of the process is to understand the meaning behind your data. Own it, embrace it and learn from it.

* The implementation of this feature was inspired by the work of the one and only Ivo Gueorguiev

The post How to Reveal the Immediate Impact of Implementing WIP Limits appeared first on Nave Blog: Expert tips and guidelines for agile teams.

Getting started with Monte Carlo simulations as an alternative approach to making delivery forecasts can be challenging, especially if you’ve been stuck estimating your work using story points (or hours) for quite some time.

Over the past few months, I’ve received a lot of questions from our audience about this topic and so I thought it would be useful to bundle the 10 most frequently asked ones together and answer them for you.

The Monte Carlo Simulation Explained: How to Leverage the Most Reliable Approach to Forecasting

Today, we’ll explore the top 10 frequently asked questions (and answers) about Monte Carlo. Let’s dive in!

What is Monte Carlo simulation?

In project management, the Monte Carlo method or Monte Carlo simulation is a mathematical technique used for forecasting which takes into account risk, uncertainty and variability.

It runs a large number of random trials using your past throughput data to predict the throughput for a future time frame.

You define the start date and the number of tasks, and the simulation provides a range of delivery dates and the probability that comes with each date. For any date in the future, it uses the throughput of a random day in the past to simulate how many work items are likely to get done.

For example, say on February 14th you’ve had a throughput of 3 tasks. The simulation takes this number and assumes that this is how many tickets will be completed on February 22th. To project the probable throughput of February 23th, it takes the throughput of another random day in the past and so forth.

Тhe simulation is repeated tens of thousands of times before the results are presented in the form of a probability distribution with percentiles increasing from left to right. It provides a range of delivery dates and the probability that comes with each of them. The Monte Carlo simulation produces a probabilistic forecast based on your past performance data.

When to Use Monte Carlo Simulations?

With regard to forecasting, Monte Carlo simulations come in two forms: calculating the delivery date of a number of items to be completed, or the amount of tasks to be finished in a given period.

We use this technique to answer the two most challenging questions in project management:

• “When can we finish X number of tasks?”. Monte Carlo will give you the delivery date of your project and the level of certainty that this will happen. Let’s say that you know (at your best) that the scope of the project is about 100 tasks. You can use the Monte Carlo simulation to give your client a probable delivery date and the confidence level to hit that target.
• “How many tasks can we finish in X number of days?”. With Monte Carlo, you will be able to decide how many items can be completed within a certain timeframe. For example, say you know your next release is planned for June 15th and you want to know how many new features will be ready by then. You input your start date and end date and the simulation will give you a range of outcomes and the probability that comes with each of them.

No guesswork or subjective estimating is involved – just data-driven probability-based future predictions calculated using your own historical performance.

How to Interpret Monte Carlo Simulation Results?

Monte Carlo uses a computational algorithm to simulate the process thousands or even millions of times. The result is a histogram showing all the possible outcomes and the likelihood that each outcome will occur.

So, how do you read that histogram?

In this example, we set a backlog of 40 tasks and we want to start working on it on March 1st. The simulation tells us that there is an 85% probability that we can finish all the backlog items by July 5th. The further you go in time, the greater the certainty of completing all the tasks.

Are we saying that these exact 40 tasks in our backlog will be delivered by July 5th? No, we aren’t. What we are saying is that we can deliver any 40 work items by July 5th and there is an 85% chance that we can meet that goal.

Probabilistic forecasting enables you to make reliable delivery commitments using your own past performance data. The question, “When will this be done?” is not that interesting anymore. The charts already provide that answer. The question now becomes, “How much risk are you willing to take?”.

Are you willing to commit to the delivery date that comes with the 50th percentile (which, by the way, comes with the same confidence level as flipping a coin)? Or, would you prefer to make a commitment with more confidence and go with the 85th, even the 95th percentile so that you increase the probability of delivering on time?

With Monte Carlo, you don’t have to estimate the relative complexity of your work anymore! The only thing you have to do is decide upon the level of risk you’re willing to manage.

Do You Need to Slice Your Items into Even Sizes for Monte Carlo to Work?

Story sizing into even pieces is a widely-spread activity, which is often considered to be a prerequisite to making reliable future predictions. This is one of the biggest Monte Carlo misconceptions out there.

The size of your work items doesn’t affect the reliability of your forecast because your historical data (the basis of your forecasts) contains work items of different sizes.

The main prerequisite to making accurate delivery forecasts lies in maintaining a predictable workflow.

In a predictable system, we only choose the level of confidence we want to work with. If you know that the nature of the work is complex, there are plenty of unknowns and you have never done this kind of work before, then commit to a higher percentile (95%, 98%). That way, you have a high probability of meeting your goal.

If the work is easy, your delivery workflow is stable and you don’t expect any obstacles along the way, go with a lower percentile (70%). Remember, forecasting is all about managing risks effectively and it is up to you to decide what’s the level of risk you’re willing to live with.

The size of your work can only be a prioritization criterion and it doesn’t impact the accuracy of your forecast in any way.

Does Monte Carlo Account For Story Splitting Altogether?

Let’s take a step back. Just because you have 100 stories in your backlog, this doesn’t mean that these exact 100 stories will be delivered on the date you’ve committed.

That’s not what the Monte Carlo simulation is telling you. What the simulation is telling you is “If you have 100 items, they will be done by date X and there is Y% certainty that you’ll hit that target”.

You’ll probably split your stories, some of them will drop off, more will be added, you’ll discover defects and additional work will inevitably come in between. You can take any 100 items you want, the result of the simulation will still be valid.

Story splitting is about determining whether something is more complex than we initially assumed. If you split your initial story into 4 other stories, that doesn’t necessarily mean that you’ll work on all 4 new stories.

What Monte Carlo is telling you is that you have 100 free slots to deliver on your commitment. Now, it’s up to you to decide, in a continuous manner, how to best fill these slots to meet your customer’s expectations.

Does the Method Consider Your Current Work in Progress?

Yes, absolutely. Monte Carlo doesn’t explicitly specify whether or not your 100 stories have been started yet. These 100 stories may include the work that’s currently in progress as well.

Remember, the Monte Carlo method doesn’t tell you which items will be delivered. It is up to you to prioritize the work effectively, ideally based on cost of delay.

Often, I see teams who work towards a release date use Monte Carlo to figure out how many work items in progress will make it by the deadline. They use this analysis to prioritize the work that brings the highest value and they deprioritize tasks that won’t be delivered in the current release anyway.

The number of items you add to the simulation accounts for both items already in progress and items that haven’t been started yet.

What Data Is Needed for Monte Carlo Simulation to Produce Reliable Outcomes?

The fact that probabilistic forecasts are based on your past performance doesn’t mean that you need a ton of data in order to come up with reliable delivery predictions. Whether you have been collecting data from the very beginning of your board creation, or you are just getting started with new teams, this is beside the point.

If your delivery system is optimized for predictability, then you won’t actually need any more than 20 or 30 completed items to come up with accurate results. It’s not about quantity – it’s all about taking control of your management practices and ensuring you deliver results in a consistent manner.

More importantly, you need to use data from the past that reflects your current conditions. If you’ve recently changed your system design, introduced new process policies, or if there are new people joining or leaving the team, then you have to observe how these changes affect your performance.

And if the impact is significant, then it would be better to only work with the data collected after the changes have been implemented.

How Does the Scaling Factor in Monte Carlo Work?

Now, what if you don’t have data that reflects your future conditions? Let’s say that you need to forecast the delivery date of a project that takes place in December when everyone is taking time off for the holidays, and you don’t have data that accounts for that situation.

If that’s the case, the best thing that you can do is scale down your daily throughput accordingly, so that the simulation accounts for the changes in your performance.

This is where the scale factor in Monte Carlo comes into play.

The scale factor is used for high uncertainty scenarios where you expect drastic changes in the performance of your team, but you don’t have data to account for that.

A 0.5 scale will mean that you anticipate your daily throughput to be twice lower, 2.0 means twice as much as the typical throughput rate.

In the example above, we expect the throughput to decrease by 20%, so we set the scale factor to 0.8. The simulation now tells us that if we have a scope of 20 tasks and we initiate our project on December 1st, there is an 85% chance of delivering on February 7th.

Remember, only use the scale factor if you don’t have the data to work out your scenario. If you do and it represents your current setup, by all means, use that data instead.

Should We Filter Out Certain Data When Running the Simulation?

Here is a $700/hour consulting answer. It depends.

Let me explain. The main goal of the Monte Carlo simulation is to answer the question: “When will this be done?”. This is a customer’s question. In order to provide a reliable answer, you need to think about your work from a customer’s perspective.

How do you define the work that you’ll deliver to your client? If you only have tasks in your system, and that’s how you define the concept of customer value, then you shouldn’t filter out any data.

However, if your scope is defined in terms of stories, and your customer expects a commitment on a story level, you should filter your data by stories to generate the forecast and leave out all other work item types.

What Are the Assumptions Required to Be Made in Monte Carlo Simulations?

The size, nature or complexity of your work doesn’t affect the accuracy of your forecast. The amount of data you have collected is not a factor determining the dependability of the Monte Carlo method.

The only prerequisite to producing reliable delivery forecasts is to optimize your workflow for predictability.

The sole requirement for Monte Carlo (and any other approach to forecasting) to work and give you reliable answers is to use the data produced by a predictable delivery system.

It’s all about taking control of your management practices to ensure you deliver customer value in a consistent manner.

If your delivery system doesn’t produce the results you are hoping for and you’d like to explore the proven roadmap to optimize your workflows for predictability, I’d be thrilled to welcome you to our Sustainable Predictability program!

So, these were the questions that I’ve been asked about Monte Carlo simulations the most often. I hope the answers will help you leverage this fantastic approach to making reliable delivery commitments.

And if you have any others, don’t hesitate to post them below. I’ll address each and every one of them. I wish you a productive day ahead!

The post Monte Carlo Simulation Explained: Everything You Need to Know to Make Accurate Delivery Forecasts appeared first on Nave Blog: Expert tips and guidelines for agile teams.

There is a simple way to eliminate the stress in your workplace and create a new narrative around how you manage your work.

Recently, I’ve been thinking about the easy pitfall of taking on more and more and more work until eventually everyone burns out. Many of us have that inner hustle, driving us to say yes to every request. We constantly try to cater to everyone’s demands and make everyone happy. It’s that feeling that you and your team, you’ve got to do it all. Not only that but you’ve got to do it quickly.

And my hope is that you’ll walk away from this article willing to shift how you show up for work – both for your teams and for yourself.

Workplace Stress: The Silent Killer of Your Well-Being

Back in the day, in my early years as a product manager, I believed that every single thing that was being asked of us was important. Customer requests were constantly coming in. We were overpromising and underdelivering over and over again.

I felt like we were always behind. I felt like I was always letting everyone down. That’s how badly overwhelm manifested for me during those times. Eventually, we burned out trying to manage unrealistic expectations. One after another, my teams started to fall apart.

Our customers began looking for alternatives on the market which created the need for additional demand in order to stay cost-effective. This vicious cycle landed us in very hot waters.

Things didn’t look good from a personal perspective, either. That constant pressure that there were so many commitments to deliver and they were all important, was killing me. At the same time, we couldn’t really finish any of it because we were constantly interrupting what we were doing to start the new request that had just arrived.

I felt paralyzed by overwhelm. It seemed that I was just going to stay stuck in this feeling. I felt like there was literally no way out, there was no way to move forward.

Did you know that when you have this feeling of overwhelm when you feel like you can’t move, like you are frozen, that is a real response of your nervous system to fear and anxiety?

While, for some, this can mean physical symptoms like shaking or sweaty palms, for others, this can show itself as a mental shutdown. This is what the stress in your workplace is causing you.

When you’re not being able to sleep properly, if your heart is racing, if you can’t take deep breaths, or you’re getting headaches, these are the physical symptoms that signify that you are under severe pressure. This is always a sign that there’s something off, and that you need to make a shift to release that pressure.

Simple Tricks to Manage Stress in Your Workplace Effectively

Nowadays, when I’m consulting managers and business owners, one of the things I hear a lot, especially from my super ambitious clients, is that this is just the way it is.

So, the expectation is that we’re going to be available twenty-four hours a day. We’re going to start working on whatever is asked of us immediately, and we’ll ideally deliver it that same day.

We are going to be there for every meeting and say yes to all the fancy requests. And, what is it that we are aiming for? We want to maintain our level of success, we want to reach the level of recognition that we aspire to.

What we don’t really realize is that, we also have to make a sacrifice. And that sacrifice is usually our physical well-being, our mental well-being, our emotional well-being, our spiritual well-being, and our relationships. Both ours and those of our teams.

And it only takes a certain number of years of going down this road to realize that this is not going to be the path to long-lasting success.

Eventually, your health falls apart, or your marriage falls apart, or your relationship with your kids falls apart, or you just find yourself shaking on the floor at 3am and you can’t function properly.

And I think that we don’t talk about this enough. These things happen a lot, and we simply aren’t talking about it as much as we should.

We’ve been brainwashed to believe that our value is based on how busy we are. It’s not, and it has never been so.

Do we have to go through these really tough times before we’re able to wake up and say, “Wait for a second, something needs to change. Something feels off here”?

I want to encourage you to take a stand. Instead of trying to do all the things all at once, really, just say “No”. Or at least say, “Yes, but later”. Respectfully but firmly.

If you need permission to just stop saying “Yes”, I’m happy to be that person. You can just stop now.

And if you are a people-pleaser or a recovering people-pleaser (like me), if you’re someone who doesn’t like to get into confrontations or difficult conversations, there are two very simple things that can help.

Firstly, play a little trick on your mind. Just say, “If I continue to say yes to everything, if I continue to pretend that we have it all together when we don’t, eventually I and everyone else in my team won’t be able to show up for the people we love the most.”

So, every time you say “Yes, we’ll handle this, but later. Our process is fully utilized at the moment” or even “No, we won’t handle this at all because it doesn’t fit within our business vision”, if you can then reframe each of those occurrences as a little investment in your well-being, you’ll be in a great place.

Just think about how these small actions lead to a relief in team overburden, take you a step closer to a good work-life balance and how they enable you to be present for the people you love. With this approach, you can actually trick yourself into getting better at saying “No”. Most of us need that when we’re just starting out with this transition.

Secondly, provide the evidence. Start measuring your team’s capability and your demand. Then, show your customers the results.

No one can argue with numbers.

When your demand outstrips your capacity and you’re able to demonstrate that pushing new work actually leads to delayed deliveries, poor predictability and exhausted and overburdened teams, the conversation takes a totally different perspective.

Here’s your action item: If you haven’t connected Nave to your product management tool, now is that time. Start your trial here, it’s free for 14 days, no strings attached →

When we worship at the altar of success, when that is the only defining measurement of our worth, things can get really messy. I hate that I’m talking from experience, but it’s true. I hope this story inspires you to slow down not only to relieve the stress in your workplace but also to help you show up for your teams and yourself.

And as always, if you love this post, could you do me a favor and share it with your colleagues and friends? I strongly believe that this is a vital message, and I think it’s essential that we get it out into the world.

All right, my friend. Thank you so very much for tuning in. I’ll see you next week, same time, same place. Have a wonderful day!

The post I Had to Say “No” to Relieve the Stress in My Workplace appeared first on Nave Blog: Expert tips and guidelines for agile teams.

There are certainly business cases where predictability doesn’t actually matter. If you are working upon release dates that take place twice per year and you always meet these deadlines, or there are no deadlines at all and you deliver results whenever you deliver results, why would you need predictability?

Well, let’s look at this question from a slightly different perspective.

Did you know that Serena Williams earned her first Grand Slam singles title at the U.S. Open in 1999 and won a string of five Grand Slam singles title wins: the 2002 French Open, Wimbledon, and U.S. Open titles and the 2003 Australian Open and her second Wimbledon title in 2003, all by defeating her older sister Venus in the finals.

To date, she has won 23 Grand Slam singles titles, the most of any player in the Open Era, and the second-most of all time.

And if you’ve watched any of her documentaries, you’ll know that even though Serena Williams is one of the greatest female tennis players of all time, she still shows up for a training session every day striving to improve her game.

She brings her A-game every single time with the fundamental belief that she can keep getting better. And this is one of the top key qualities that sports legends have in common. It is their constant willingness to improve. One small step at a time, every single time. This is not just a habit, it’s a lifestyle.

The fact is that the principles and practices that enable you to achieve sustainable predictability are the exact same principles and practices that lead to tremendous improvement in your delivery workflows.

By adopting the strategies suggested by the Sustainable Predictability roadmap, you are by all means continuously improving your processes which ultimately leads to better business outcomes across the board. Achieving predictability is really a side effect of your efforts.

The data that you collect and analyze to provide predictable delivery results is going to give you invaluable insights, actionable insights. Think of it as your guide, constantly directing your attention to the most effective areas for you to drive your improvement efforts.

The data you track when working towards achieving better predictability is there to give you visibility into your current situation. It is there to reveal problems that wouldn’t otherwise be visible and help you inform your decisions.

Here’s the thing, I know you’re talented, you’re efficient, you’re good at your job, but how will you grow into it if you are not consistently focusing on improving your management practices?

No matter how skilled or productive you and your teams are, there’s always, and I mean always, a possibility to do things better than before.

Strive to identify improvement opportunities in every activity you do. Get the data-driven insights and take action. One small step at a time.

And even if you don’t care about predictability, I really hope that you at least care about continuous improvement.

If you just focus on getting 1% better in improving the way you manage your work each day, you’ll eventually reach a certain point where you’ll not only achieve sustainable predictability but also have set up a system that self-optimizes for better business outcomes. And that’s a goal worth striving for.

If you’re interested in exploring the proven roadmap to optimize your workflows for predictability and enabling an environment of continuous improvement, I’d be thrilled to welcome you to our Sustainable Predictability program!

Alright, my friend. Thanks for tuning in. I’ll see you again next week, same time and place for more managerial insights. Have a wonderful day!

The post Do You Really Need Predictability? appeared first on Nave Blog: Expert tips and guidelines for agile teams.