All posts by Travis Birch

Travis has been helping businesses improve since 2008. His clients include Royal Bank of Canada, Scotiabank, Bank of Montreal, Bell, Telus, American Express, LoyaltyOne, Aimia, Blackberry, Dealertrack and the Government of Ontario. He is an Accredited Kanban Trainer with LeanKanban University, a Certified Scrum Professional with the Scrum Alliance and a Master of OpenAgile. Travis considers Kanban’s systematic yet humane approach to evolving organizations a key differentiator to other organizational change approaches. Travis brings curiosity, enthusiasm, respect and empathy to his work as a trainer and consultant.

Kanban: Real Scaled Agility for Your Enterprise

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Your business is an ecosystem of interdependent services, a complex adaptive system.

A bunch of organizations I know started their journey of increasing their agility with Scrum. That didn’t solve all of their problems. Kanban enables organizations to evolve their service delivery systems towards mature business agility.

As addressed in How Kanban Saved Agile, pure Scrum is extremely rare. Scrumbut (the disparaging label that spawned from so many organizations reporting that they do Scrum, but…) on the other hand, is extremely common.

In order to not be Scrumbut, you need the following:
  • Cross-functional development team of 7 +/- 2 people—ALL skills needed to ship product is present on the team—there are no dependencies external to the team;
  • One source of demand with no capacity constraints—the Product Owner is the customer AND full-time member of the team;
  • Sprints are one month or less, begin with starting new demand from the Product Owner and end with the delivery of potentially shippable Product Increments, followed by a retrospective about how to do better next Sprint;
  • “Potentially Shippable” means that the decision about whether to actually ship is purely a business decision. All the technical work is done;
  • If all of the technical work required in order to ship isn’t done, then the Sprint is a failed Sprint;
  • The Scrum Master is a servant-leader and Scrum educator to the entire organization.

How many organizations do you know of with Scrum teams that meet all of the requirements above? I don’t know one.

So, what’s the solution? Give up on Scrum? Are we still getting benefits from Scrumbut? Alright, let’s stop it with the Scrumbut already. Let’s acknowledge what’s really going with real teams in the real world and call that Scrum. Let’s refer to the above  checklist as “Ideal Scrum”.

Agile scaling methods have become a popular risk hedging tactic for all the loose ends dangling around the real teams in the real world.

Here are some of the reasons for adding layers of scaling around Agile teams:

  • Teams are not fully cross-functional;
  • Teams have external and opaque depencies;
  • Many of these dependencies are shared services with multiple sources of demand and constrained capacity—often overburdened;
  • External dependencies can be other teams—demand from other teams shows up in their backlogs, prioritized by their own Product Owners;
  • Many dependencies don’t play by the same rules at all—some reside in a different part of the organization, with different structures and political forces;
  • The Product Owners are proxies for multiple stakeholders and customers and the Product Backlogs represent an array of multiple sources of demand, with different service level expectations, strategic origins, degrees of clarity, urgency and political forces pushing them into the deliver organization;
  • The Product Backlogs are made up primarily of solutions defined by stakeholders and translated by the pseudo-Product Owners as pseudo-user stories—how they get there is opaque, the “fuzzy front end”—and somewhere in here a fuzzy delivery commitment was already made;
  • The work of a Sprint includes all of the work that the non-cross-functional teams can get done—then whatever the teams get “done” is “delivered” (handed-off) to a subsequent set of teams or process steps (usually fairly well defined at an organizational level but outside of the teams’ influence);
  • Delivery decisions are made based on constraints imposed by legacy technology, systems and their gatekeepers (for historically good reasons);
  • The teams are “done” at the end of each Sprint, yet much work is still to be done before their “done” work is potentially shippable;
  • The Scrum Master’s are held collectively accountable for the collective deliverables of the teams and their ability to cross-team coordinate and integrate—accountability by committee translates into no one is actually responsible.
  • Middle managers are scrambling to pick up the pieces because they are actually accountable for delivered results.

Generally speaking, the aim of Agile scaling methods is to apply larger Agile wrappers around clusters of Agile teams in order to re-establish some kind of hierarchical structure needed to manage the interdependencies described above. Whether its a Release Train or a Nexus, or whatever else, the idea is that there is an “Agile Team of Teams” managing the interdependencies of multiple, smaller teams. As long as the total number of people doesn’t grow beyond the Dunbar number (~150), the Dunbar-sized group is dedicated and cross-functional, there is a team managing the interdependencies within the Dunbar, there are no dependencies outside of the Dunbar and there is some cadence (1-3 months) of integrated delivery—it’s still “Agile”. All of this scaling out as far as a Dunbar (and only that far) allows the enterprise to still “be Agile”—Scaled Agile.

This is all supposed to be somehow more realistic than Ideal Scrum (with perhaps am overlay of Scrum of Scrums and a Scrum of Scrum of Scrums). It’s not. How many organizations do you know of that can afford to have ~150 people 100% dedicated to a single product? Perhaps today there is enough cash lying around, but soon enough the  economic impact will be untenable, if not unsustainable.

How does Kanban address this problem? Your business is a complex adaptive system. You introduce a Kanban system into it such that it is likely that the complex adaptive system is stimulated to improve. The Systems Thinking Approach to Introducing Kanban—STATIK—is how you can make such a transition more successful (@az1):
  1. Understand the purpose of the system—explicitly identify the services you provide, to whom you provide them and why;
  2. Understand the things about the delivery of the service that people are not happy about today—both those whose needs are addressed by the service and those doing the work of delivering the service;
  3. Define the sources of demand—what your customers care about and why;
  4. Describe the capability of your system to satisfy these demands;
  5. Map the workflow of items of customer-recognizable value (@fer_cuenca), beginning with a known customer need and ending with the need being met through stages of primary knowledge discovery (Scrum teams somewhere in the middle, towards the end)—focus on activities, not looping value streams;
  6. Discover classes of service—there are patterns to how different kinds of work flow through your system (they are not arbitrarily decided by pseudo-Product Owners), what are they? Group them, they are classes of service and knowing them enables powerful risk management;
  7. With all of the above as an input, design the Kanban system for the service;
  8. Learn how to do steps 2-7 and start applying it directly to your own context in a Kanban System Design class;
  9. Socialize and rollout (learn how by participating in a Kanban Coaching Professional Masterclass);
  10. Implement feedback-loop Cadences for continuous evolution—learn the 7 Kanban Cadences and begin applying to your own context in a Kanban Management Professional class;
  11. Repeat with all of the interdependent services in your organization—every “dependency” is a service—Kanban all of them with STATIK and begin implementing the Cadences.

Don’t get hung up on teams, roles, your latest reorg, how people will
respond to another “change”, who’s in, who’s out, etc. These are all part of the service as it is now—your current capability. Initially, no changes are required at this level. The kanban system will operate at a higher level of scale. Through feedback-loop cadences, it will evolve to be fit for the purpose of your customers without a traumatic and expensive reorg.  Who is responsible for this? Identify this person. If you are the one thinking about this problem, there is a good chance that it’s you. Whoever it is, this is the manager of the service; take responsibility, do the work and make life better for everyone.

For more information about LeanKanban University Certified Kanban courses provided by Berteig, please go to Some spots are still available for our classes in Toronto, June 12-16.

For Agilists who have read this far and still don’t get it, start here:

14 Things Every Agilist Should Know About Kanban

The story below may be familiar to some:

Our IT group started with Scrum. Scores of people got trained. Most of our Project Managers became “Certified” Scrum Masters. Most of our Business Analysts became “Certified” Product Owners. We purged some people who we knew would never make the transition. We reorganized everyone else into cross-functional teams – mostly developers and testers. But now they are Scrum Developers. We tried to send them for “Certified” Scrum Developer training but hardly anyone of them signed up. So they are Just Scrum Developers. But we still call them developers and testers. Because that’s still how they mostly function—silos within “cross functional teams”, many tales of two cities rather than just one.

After the Scrum teams had been up and running for a while and we were able to establish some metrics to show the business how Agile we were (since they didn’t believe us based on business results), we had a really great dashboard showing us how many Scrum teams we had, how many Kanban teams, how many DevOps, how many people had been trained. We even knew the average story point velocity of each team.

The business didn’t get it. They were worried that Agile wasn’t going to solve their problem of making commitments to customers and looking bad because we still weren’t able to deliver “on time”.

As IT leadership, we were really in the hot seat. We started to talk about why the transformation wasn’t going as it should. We knew better than to bring the Agile coaches into the boardroom. They were part of the problem and needed to be kept at arms length from those of us who were making important decisions. Besides, their Zen talk about “why?” was really getting old fast. Some thought it was because we didn’t have the right technology. Others were convinced it was because we didn’t have the right people. After all, we didn’t go out and higher experienced (above-average) Scrum Masters and Product Owners, instead we just retrained our own people. Clearly that wasn’t working.

We started with improving the Scrum Masters. We went out and hired a few with impressive resumes. We developed some Scrum Master KPIs (HR jumped all over this one). Then one day we had a collective flash of brilliance—since the ScrumMasters are the servant leaders of teams, we will make them responsible for collecting and reporting team metrics and this will tell us how well the teams are doing and how they need to improve. This surely would be the key to improving the performance of IT and get us on a better footing with the business.

But we didn’t get the response we were hoping for. The ScrumMasters soon complained that the metrics of the teams were impacted by dependencies that they had no influence over. When we pushed harder and shamed them publicly for failing to produce meaningful metrics, they tried harder, but they weren’t able to do it. Some began disengage. “This is not the job I signed up for” became their new mantra. This was puzzling. We were empowering them and they were recoiling. Maybe we didn’t get the right Scrum Masters after all. Maybe we needed to go out and find people who could think and act effectively beyond the confines of their own teams. Or…

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How Kanban Saved Agile

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In reality, Kanban isn’t actually saving Agile nor is it intended to, nor is any thoughtful and responsible Kanban practitioner motivated by this agenda. What I’m really trying to convey is how human thinking about the business of professional services (including software development) has evolved since “Agile” as many of us know it was conceived around 20 or so years ago. The manifesto is the collective statement of a group of software development thought leaders that captured some of their ideas at the time about how the software industry needed to improve. Essentially, it was about the iterative and incremental delivery of high-quality software products. For 2001, this was pretty heady stuff. You could even say that it spawned a movement.

Since the publication of the manifesto in 2001, a lot of other people have had a lot of other good ideas about how the business of delivering professional services can improve. This has been well documented in well known sources too numerous to mention for the scope of this article.

Substantial contributions to the discourse have been generated by and through the LeanKanban community. The aim of Kanban is to foster environments in which knowledge workers can thrive and create innovative, valuable and viable solutions for improving the world. Kanban has three agendas: survivability (primarily but not exclusively for the business executives), service-orientation (primarily but not exclusively for managers) and sustainability (primarily but not exclusively for knowledge workers). Kanban provides pragmatic, actionable, evidence-based guidance for improving along these three agendas.

Evolutionary Theory is one of the key conceptual underpinnings of the Kanban Method, most notably the dynamic of punctuated equilibrium. Evolution is natural, perpetual and fundamental to life. Long periods of equilibrium are punctuated by relatively short periods of “transformation”—apparent total and irreversible change. An extinction event is a kind of punctuation, so too is the rapid explosion of new forms. Evolutionary theory is not only a scientifically proven body of knowledge for understanding the nature of life. It can be also applied to the way we think about ideas, methods and movements.

For example, science has more or less established that the extinction of the dinosaurs, triggered by a meteor impact and subsequent dramatic atmospheric and climate change, was in fact a key punctuation point in the evolution of birds. In other words, dinosaurs didn’t become extinct, rather they evolved into birds. That is, something along the lines of the small dinosaurs with large feathers hanging around after Armageddon learned to fly over generations in order to escape predators, find food and raise their young. Dinosaurs evolved into birds. Birds saved the dinosaurs.

There has been a lot of social media chatter and buzz lately about how Agile is dead. It is a movement that has run its course, or so the narrative goes. After all, 20 years is more or less the established pattern for the rise and fall of management fads. But too much emphasis on the rise and fall of fads can blind us to larger, broader (deeper) over-arching trends.

The agile movement historically has been about high-performing teams. More recently, market demand has lead to the profusion of “scaling” approaches and frameworks. Scaling emerged out of the reality of systemic interdependence in which most Agile teams find themselves. Most agile teams are responsible for aspects of workflows—stages of value creation—as contributors to the delivery of a service or multiple services. Agile teams capable of independently taking requests directly from and delivering directly to customers are extremely rare. For the rest, classical Agile or Scrum is not enough. The feathers just aren’t big enough. Agile teams attempting to function independently (pure Scrum) in an interdependent environment are vulnerable to the antibodies of the system, especially when such interdependencies are merely denounced as impediments to agility.

Some organizations find themselves in a state of evolutionary punctuation (the proverbial sky is falling) that can trigger rapid adaptations and the emergence of local conditions in which independent service delivery teams can thrive. Most large, established organizations seem to be more or less in a state of equilibrium. Whether real or imagined, this is what change agents have to work with. However, more often than not, the typical Agile change agent seems adamant that the sky is always falling and that everyone accepting that the sky is falling is the first step to real and meaningful change. This is not an attitude held by Agile change agents alone. This is a standard feature of traditional 20th Century change management methods, the key selling point for change management consulting.

Naturally, most self-identifying “Agilists” see themselves as change agents. Many of them find themselves in the position of change management consultants. But the motivation for change can quickly become misaligned: Change needs to happen in order for Agile to work. If you are passionate about Agile, you will seek to bring about the environmental changes that will allow for Agile to thrive. We don’t need to follow this path too far until Agile becomes an end in itself. It is understandable then that for some, Agile appears to be a dead end, or just dead.

But if there is a larger, over-arching historical process playing out, what might that be? Perhaps it has something to do with the evolution of human organization. Perhaps we are living in a period of punctuation.

For my working definition of Kanban, please refer to my previous article 14 Things Every Agilist Should Know About Kanban (this contains links to the Kanban body of knowledge, including Essential Kanban Condensed by David J. Anderson and Andy Carmichael).

For my working definition of Agile, please refer to The Manifesto for Agile Software Development.



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CLEAR Servant Leadership

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Sun rising over field - servant leadership

I facilitated this workshop today for a senior leadership team. I mostly employ famous quotations familiar to many to provide a brief overview of Servant Leadership as well as a learning framework for systematically building capacity in others while improving the systems in which they work. The folks in the workshop seemed to really connect with Scott’s CLEAR model (not so famous but ingenious in its deceptive simplicity). I offer it as a guide for designing CLEAR acts of leadership.

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14 Things Every Agilist Should Know About Kanban

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So you are embarking on an Agile transformation. One important thing you need to figure out (and hopefully you have some consultants helping you) is how many Scrum Teams you will need for product development and how many Kanban teams you will need for operations, deployment, support, maintenance, keeping the lights on, etc. Because when you create a bunch of cross-functional teams they will have gaps in their Agile engineering practices and their definition of “done” will have to be limited by several organizational factors. The teams won’t have access to many of the environments and there won’t be enough specialized resources to assign to each team. Plus, the nature of the work coming into the ops and support teams are much more finely grained and varied than user stories in a Sprint Backlog and it’s important that the Scrum teams focus on their products and are not disrupted by every little change request from the business. So you need Kanban teams to do the work that the Scrum Teams can’t do yet as well as the stuff that you don’t want your Scrum Teams to be distracted by.

That’s more or less the popular consensus on the role of Kanban in the Agile community. Some acclaimed thought leaders have even written popular books and blog posts about it. But if one digs a little deeper, one finds that there is much more to Kanban than meets the common agilist’s eye (although clearly this is changing, evidenced by the existence of this piece of writing). So here are the 14 things that I can think of off the top of my head:

  1. The Kanban Method is not about transformation. At least not the radical, deep Satir J-Curve brand of transformation that has been attempted in many organizations. All too often with the radical approach, there is enough resistance to change and enough ensuing chaos for the leaders of the organization to lose patience with the change initiative before the system has a chance to recover.
  2. Moreover (and dangerously), many so-called champions of change are not aware that Satir was a psychotherapist and that her J-Curve method was employed to change the identity of her clients, breaking them down and building them up again. What’s important here is that Satir was a psychology professional working with willing clients to help them transform into the people they wanted to be. This is not the case with most professional knowledge workers. Knowledge workers tend to not be seeking this kind of service from managers and coaches. A more brutal version of this technique has been employed to transform teenagers into deadly soldiers.
  3. Instead of deep J-Curve transformation, the Kanban Method proposes small, rapid J-Curve experiments. This approach to change provokes less resistance, avoids extended periods of thrashing in chaos and the severe testing of leadership patience. Well-designed small J-Curve experiments produce just enough organizational stress to stimulate change without drop-kicking people into deep chaos and despair and forcing the regression of an organization to lower levels of trust and maturity.
  4. The Kanban Method is a management system for the design and evolution of the interdependent services of an organization. Such services are often composed of several Scrum Teams, shared services, managers, senior staff and specialists and are often themselves served by other services. Every service is delivered via a system of stages of knowledge discovery (rather than hand-offs). See more on this here.
  5. The design of a system determines the fitness for purpose, flow of value and quality of the service (as demonstrated by Deming’s Red Bead Experiment). It’s not about high-performance teams. It’s about the performance of the system.
  6. Transparency of the system empowers knowledge workers to self-organize around the work because they understand the system and are trusted to know what to do in order to deliver the service.
  7. Kanban managers are systems managers, not people managers and not coaches.
  8. Team-level Kanban is actually a form of proto-Kanban—still Kanban, but in an immature state, an incomplete rendering of a service delivery system.
  9. A Kanban system is a pull system. The capacity of the system is calibrated for optimum flow. New work enters the system when there is sufficient capacity to absorb the new work without overburdening the system and disrupting flow. Demand is balanced against capacity.
  10. All demand is potentially refutable. When there is capacity in the system to start new work, sources of demand collaborate to determine what is the most important work to start next and the system is replenished.
  11. Deciding what to start next is based on economics—transparent and rational risk assessment.
  12. Once the system is replenished and there is a commitment to deliver the newly-started work, risks are managed with explicit policies such as classes of service, work-in-process limits, pull-readiness criteria, feedback loops and relevant metrics (i.e. not team velocity).
  13. Average lead time from project or feature commitment to completion is a basic metric. Improving the system results in a reduction in both lead time and lead time variability. Delivery forecasts are based on historical lead time data. Deadlines are also managed with lead time data (i.e. deciding when to start something).
  14. All of the above is the responsibility of management. This should leave little management capacity for monitoring individual performance and story point velocity of teams (white bead count). A sign of a mature Kanban system is that managers have improved their behaviour and are focused on improving the system and that knowledge workers are free to self organize around the work as skilled, adult professionals.

If you are interested in the history of the Kanban Method, start here.

Best starter book: Essential Kanban.

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The Scrum Team Assessment – Official Launch

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Hi Everyone,  I don’t do announcements on here too often, but I wanted to let everyone know about the official launch of our new product: the Scrum Team Assessment – an online tool for your team to get a report on how well they are using the Scrum framework… and most importantly, helpful recommendations on how to improve!  This is a fully automated Scrum maturity assessment tool!

The Scrum Team Assessment is based on the years that I and two other coaches (Paul Heidema and Travis Birch) have been working with Scrum and Agile teams to improve business and technical results.  The Scrum Team Assessment is a joint effort that has resulted in a fully automated virtual coach for your team.

The analysis is both statistical and expert-system based.  This means that the report has basic information about how the team is following Scrum, and, more importantly, clear how-to advice to get your team to make improvements.  There are “quick wins” which are easy but will have a significant impact as well as long-term recommendations that are often harder, but will drive your team to a high-performance state.

The Scrum Team Assessment includes a survey of about 100 questions that focus on seven broad categories:

  • The team’s environment
  • The basic Scrum process
  • The Product Backlog
  • Team Membership
  • ScrumMastering
  • Product Ownership
  • and Agile best practices

Every team member fills in the survey to help us generate a valid set of recommendations.

The Scrum Team Assessment is $496/team/use (that’s Canadian dollars).  If you have several teams or wish to obtain an enterprise license, please contact us at or +1-905-868-9995.

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The Real Daily Scrum

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On many occasions, I have observed “Scrum Masters” and even “Product Owners” attempting to drive what they understand to be the Daily Scrum. Just this morning, I witnessed a “Daily Scrum” in which a “Product Owner” gave the team a bunch of program updates and made sure that each team member had tasks to work on for the day. Then, the PO “wrapped up” the meeting and left the team to get to the work. I then stayed and observed what the team did next. They actually stayed together to discuss the work and figure out how they were going to organize themselves for the day. I then went over to the Product Owner and whispered in her ear that the team was now doing the real Daily Scrum. She said “Oh,” and promptly walked over to find out what was going on. I then observed her from a distance nodding her head several times while appearing to understand what the team was talking about. I’m not sure if she understood or not, but that’s irrelevant. The point is that the Daily Scrum is for the Development Team to inspect and adapt its progress towards the Sprint Goal and decide how it will self-organize for the coming day. If the Development Team decides as a result of the Daily Scrum that it needs to re-negotiate any previously forcasted functionality with the Product Owner, then that conversation can certainly happen at that time.

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Nexus Reifies

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Nexus Scrum

I had the privilege of attending‘s 2-day seminar on Scaled Professional Scrum. The Nexusa connection or series of connections linking two or more things (direct translation from Latin a binding together), is the recommended scaling framework. The purpose of the Nexus is to manage dependencies between 3-9 Scrum Teams towards “reification”, to make an abstract idea real or concrete. This is ensured mostly through a single Product Owner, single Product Backlog, integrated (Nexus) Sprint Planning, Review and Retrospective and the addition of a Nexus Integration Team whose membership is made up mostly of Scrum team members internal to the Nexus, but often also includes other support personnel. The structure is very similar to LeSS, but perhaps even less prescriptive and is certainly much less prescriptive than SAFe. This is probably my favourite thing about the Nexus – the fact that it has just enough structure to be a model for scaling Scrum, but is light and flexible enough to accommodate all of the nuances that “just depend” on your situation. Like the other two above-mentioned scaling models, it places emphasis on the need for strong technical practices, continuous integration and the synchronization of events to facilitate integration. There is flexibility around synchronization, in that if the Nexus Sprint is 4 weeks in duration and teams within the Nexus want to do 2 or even 1 week Sprints, the model accommodates – as long as all of the teams’ work is combined into a fully integrated (reified) increment of potentially shippable product by the end of the Nexus Sprint.


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All Team Quality Standards Should Be Part of the Definition of “Done”

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The other day a technology leader was asking questions as if he didn’t agree that things like pair programming and code review should be part of the Definition of “Done” because they are activities that don’t show up in a tangible way in the end product. But if these things are part of your quality standards, they should be included in the definition of “Done” because they inform the “right way” of getting things done. In other words, the Definition of “Done” is not merely a description of the “Done” state but also the way(s) of getting to “Done” – the “how” in terms of quality standards. In fact, if you look at most of any team’s definition of “Done”, a lot of it is QA activity, carried out either as a practice or as an operation that is automated. Every agile engineering practice is essentially a quality standard and as they become part of a team’s practice, should be included as part of the definition of “Done”. The leader’s question was “if we’re done and we didn’t do pair programming and pair programming is part of our definition of “Done”, then does that mean we’re not done?” Which is sort of a backwards question because if you are saying you’re done and you haven’t done pair programming, then by definition pair programming isn’t part of your definition of done. But there are teams out there who would never imagine themselves to be done without pair programming because pair programming is a standard that they see as being essential to delivering quality product.

Everything that a Scrum Development Team does to ensure quality should be part of their definition of “Done”. The definition of “Done” isn’t just a description of the final “Done” state of an increment of product. In fact, If that were true, then we should be asking why anything is part of the definition of “Done”. This is the whole problem that this artifact solves. If this were the case, the team could just say that they are done whenever they say they are done and never actually identify better ways of getting to done and establishing better standards. We could just say (and we did and still do), “there’s the software, it’s done,” the software itself being its own definition of “Done”.

On the contrary the definition of “Done” is what it means for something to be done properly. In other words, it is the artifact that captures the “better ways of developing software” that the team has uncovered and established as practice because their practices reflect their belief that “Continuous attention to technical excellence and good design enhances agility” (Manifesto for Agile Software Development). The definition of “Done” is essentially about integrity—what is done every Sprint in order to be Agile and get things done better. When we say that testing is part of our definition of “Done”, that is our way of saying that as a team we have a shared understanding that it is better to test something before we say that it is done than to say that it’s done without testing it because without testing it we are not confident that it is done to our standards of quality. Otherwise, we would be content in just writing a bunch of code, seeing that it “works” on a workstation or in the development environment and pushing it into production as a “Done” feature with a high chance that there are a bunch of bugs or that it may even break the build.

This is similar to saying a building is “Done” without an inspection (activity/practice) that it meets certain safety standards or for a surgeon to say that he or she has done a good enough job of performing a surgical operation without monitoring the vital signs of the patient (partly automated, partly a human activity). Of course, this is false. The same logic holds true when we add other activities (automated or otherwise) that reflect more stringent quality standards around our products. The definition of “Done”,therefore, is partly made up of a set of activities that make up the standard quality practices of a team.

Professions have standards. For example, it is a standard practice for a surgeon to wash his or her hands between performing surgical operations. At one time it wasn’t. Much like TDD or pair programming, it was discovered as a better way to get a job done. In this day and age, we would not say that a surgeon had done a good job if he or she failed to carry out this standard practice. It would be considered preposterous for someone to say that they don’t care whether or not surgeons wash their hands between operations as long as the results are good. If a dying patient said to a surgeon, “don’t waste time washing your hands just cut me open and get right to it,” of course this would be dismissed as an untenable request. Regardless of whether or not the results of the surgery were satisfactory to the patient, we would consider it preposterous that a surgeon would not wash his or her hands because we know that this is statistically extremely risky, even criminal behaviour. We just know better. Hand washing was discovered, recognized as a better way of working, formalized as a standard and is now understood by even the least knowledgable members of society as an obvious part of the definition of “Done” of surgery. Similarly, there are some teams that would not push anything to production without TDD and automated tests. This is a quality standard and is therefore part of their definition of “Done”, because they understand that manual testing alone is extremely risky. And then there are some teams with standards that would make it unthinkable for them to push a feature that has not been developed with pair programming. For these teams, pair programming is a quality standard practice and therefore part of their definition of “Done”.

“As Scrum Teams mature,” reads the Scrum Guide, “it is expected that their definitions of “Done” will expand to include more stringent criteria for higher quality.” What else is pair programming, or any other agile engineering practice, if it is not a part of a team’s criteria for higher quality? Is pair programming not a more stringent criteria than, say, traditional code review? Therefore, any standard, be it a practice or an automated operation, that exists as part of the criteria for higher quality should be included as part of the definition of “Done”. If it’s not part of what it means for an increment of product to be “Done”—that is “done right”—then why are you doing it?

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Summary of User Stories: The Three “C”s and INVEST

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User Stories

Learning Objectives

Becoming familiar with the “User Story” approach to formulating Product Backlog Items and how it can be implemented to improve the communication of user value and the overall quality of the product by facilitating a user-centric approach to development.

Consider the following

User stories trace their origins to eXtreme Programming, another Agile method with many similarities to Scrum. Scrum teams often employ aspects of eXtreme Programming, including user stories as well as engineering practices such as refactoring, test-driven development (TDD) and pair programming to name a few. In future modules of this program, you will have the opportunity to become familiar enough with some of these practices in order to understand their importance in delivering quality products and how you can encourage your team to develop them. For now, we will concentrate on the capability of writing good user stories.

The Three ‘C’si

A User Story has three primary components, each of which begin with the letter ‘C’:


  • The Card, or written text of the User Story is best understood as an invitation to conversation. This is an important concept, as it fosters the understanding that in Scrum, you don’t have to have all of the Product Backlog Items written out perfectly “up front”, before you bring them to the team. It acknowledges that the customer and the team will be discovering the underlying business/system needed as they are working on it. This discovery occurs through conversation and collaboration around user stories.
  • The Card is usually follows the format similar to the one below

As a <user role> of the product,

I can <action>

So that <benefit>.

  • In other words, the written text of the story, the invitation to a conversation, must address the “who”, “what” and “why” of the story.
  • Note that there are two schools of thought on who the <benefit> should be for. Interactive design specialists (like Alan Cooper) tell us that everything needs to be geared towards not only the user but a user Persona with a name, photo, bio, etc. Other experts who are more focused on the testability of the business solution (like Gojko Adzic) say that the benefit should directly address an explicit business goal. Imagine if you could do both at once! You can, and this will be discussed further in more advanced modules.


  • The collaborative conversation facilitated by the Product Owner which involves all stakeholders and the team.
  • As much as possible, this is an in-person conversation.
  • The conversation is where the real value of the story lies and the written Card should be adjusted to reflect the current shared understanding of this conversation.
  • This conversation is mostly verbal but most often supported by documentation and ideally automated tests of various sorts (e.g. Acceptance Tests).


  • The Product Owner must confirm that the story is complete before it can be considered “done”
  • The team and the Product Owner check the “doneness” of each story in light of the Team’s current definition of “done”
  • Specific acceptance criteria that is different from the current definition of “done” can be established for individual stories, but the current criteria must be well understood and agreed to by the Team.  All associated acceptance tests should be in a passing state.


The test for determining whether or not a story is well understood and ready for the team to begin working on it is the INVEST acronym:

I – Independent

  • The solution can be implemented by the team independently of other stories.  The team should be expected to break technical dependencies as often as possible – this may take some creative thinking and problem solving as well as the Agile technical practices such as refactoring.

N – Negotiable

  • The scope of work should have some flex and not be pinned down like a traditional requirements specification.  As well, the solution for the story is not prescribed by the story and is open to discussion and collaboration, with the final decision for technical implementation being reserved for the Development Team.

V – Valuable

  • The business value of the story, the “why”, should be clearly understood by all. Note that the “why” does not necessarily need to be from the perspective of the user. “Why” can address a business need of the customer without necessarily providing a direct, valuable result to the end user. All stories should be connected to clear business goals.  This does not mean that a single user story needs to be a marketable feature on its own.

E – Estimable

  • The team should understand the story well enough to be able estimate the complexity of the work and the effort required to deliver the story as a potentially shippable increment of functionality.  This does not mean that the team needs to understand all the details of implementation in order to estimate the user story.

S – Small

  • The item should be small enough that the team can deliver a potentially shippable increment of functionality within a single Sprint. In fact, this should be considered as the maximum size allowable for any Product Backlog Item as it gets close to the top of the Product Backlog.  This is part of the concept of Product Backlog refinement that is an ongoing aspect of the work of the Scrum Team.

T – Testable

  • Everyone should understand and agree on how the completion of the story will be verified. The definition of “done” is one way of establishing this. If everyone agrees that the story can be implemented in a way that satisfies the current definition of “done” in a single Sprint and this definition of “done” includes some kind of user acceptance test, then the story can be considered testable.

Note: The INVEST criteria can be applied to any Product Backlog Item, even those that aren’t written as User Stories.

Splitting Stories:

Sometimes a user story is too big to fit into a Sprint. Some ways of splitting a story include:

  • Split by process step
  • Split by I/O channel
  • Split by user options
  • Split by role/persona
  • Split by data range

WARNING: Do not split stories by system, component, architectural layer or development process as this will conflict with the teams definition of “done” and undermine the ability of the team to deliver potentially shippable software every Sprint.


Like User Stories, Personas are a tool for interactive design. The purpose of personas is to develop a precise description of our user and so that we can develop stories that describe what he wishes to accomplish. In other words, a persona is a much more developed and specific “who” for our stories. The more specific we make our personas, the more effective they are as design tools.iii

Each of our fictional but specific users should have the following information:

  • Name
  • Occupation
  • Relationship to product
  • Interest & personality
  • Photo

Only one persona should be the primary persona and we should always build for the primary persona. User story cards using personas replace the user role with the persona:


can <action>

so that <benefit>.


i The Card, Conversation, Confirmation model was first proposed by Ron Jeffries in 2001.

ii INVEST in Good Stories, and SMART Tasks. Bill Wake.

iii The Inmates are Running the Asylum. Alan Cooper. Sams Publishing. 1999. pp. 123-128.

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An Evolution of the Scrum of Scrums

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This is the story of how the Scrum of Scrums has evolved for a large program I’m helping out with at one of our clients.

Scrum of Scrum photo Continue reading An Evolution of the Scrum of Scrums

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Product Backlog Refinement

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The ultimate purpose of Product Backlog refinement is to ensure an ongoing conversation that increases transparency of the Product Backlog and therefore the Product itself – to orient everyone on the team to breaking out of their waterfall silos and focus on delivering business value, period.

On mature teams, a lot of the refinement work happens as ad hoc conversations while they are sitting around and thinking together about how to build something great because they are just motivated by that and it becomes part of their mode of operation.

The objective of the refinement work of any given Sprint (that often needs to be repeated over and over like a mantra with new, immature teams) is to ensure that the items at the top of the Backlog are transparent enough that the Development Team considers them ready to pull and get “Done” in the next Sprint.  This is where the concept of the Definition of “Ready” (DoR) comes from – the Scrum Team defines the DoR and spends up to 10% of its capacity refining enough items at the top of the Backlog so that it can provide estimates (if required) and have a reasonable degree of confidence that it can deliver the items in the next Sprint.

Refinement is NOT solutioning – I think this is the big trap that a lot of teams fall into because there is a false assumption that technical solutions need to be hashed out before estimates can be made (part of the carried-over lack of trust and communication between the business and IT) – I would almost rather throw out estimates in cases where this is not improving – The Planning Game exercise, when facilitated well, lends itself more to increasing transparency rather than solutioning.

The fact that teams are telling us that they need to solution before they can estimate is also an indication of weak Agile Engineering practices such as refactoring, test-driven development and continuous integration (XP).  The best refinement sessions are those in which the team is able to focus on the “what” – the business benefit results that the Product Owner really wants – rather than the “how” (solution).  Strong teams emerge in an environment in which they are trusted by the business and management to find the right solution as a team.  They don’t need to have it all figured out before giving an estimate because they are not afraid to give a bad estimate and fail.  Also, if the team is struggling to give estimates, this is often a sign that the Product Backlog Items are too big.  Most likely the team also needs to expand the Definition of “Done” to include testing against acceptance criteria within the Sprint so that they can estimate based on that criteria.

The “how” (solution) should be mapped out by the Development Team at a high level in the 2nd part of Sprint Planning (partly why the time box is bigger than they often think they need) and more detailed architecture, requirements and design work as part of the Sprint Backlog

But this level of maturity is very hard to do and it will take a while to get there, perhaps even years.

It also depends on your interpretation of “detail”, the word used in the Scrum Guide to describe what the team does in Product Backlog refinement. To me, it means understanding in more detail what the Product Owner really wants and needs. What does it mean to you?

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Scrum “Inputs”

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The Product Backlog is often described as the primary input to Scrum.  The Sprint starts with Sprint Planning and Sprint Planning starts with the Product Owner and the Product Backlog.  In principle, this makes perfect sense and hopefully it is enough for most teams and organizations to just start with the Product Backlog.  And if you don’t have a Product Backlog, then just start without one, get some stuff done that the team thinks is important, invite some people to the Sprint Review and most likely one of those people will end up becoming the Product Owner and gradually take on the responsbilities of that role.  I believe in just starting if you can.  I even wrote a blog post about this a while back and I stand by it.

I have served as a Scrum Master and coach for a number of teams and I have identified some patterns that I think are worth addressing.  Newly-formed teams tend to ask for (and need) a little more help than this in order to feel ready to start.  And I have learned from experience that it is usually more effective for the adoption of Scrum and team development for the team to feel ready enough to just start.

The Scrum Guide recognizes the following inputs to Sprint Planning:

  • Product Backlog
  • Latest product increment (not applicable to first Sprint)
  • Projected capacity of the Development Team during the Sprint
  • Past performance of the Development Team (not applicable to first Sprint)
  • Definition of “Done” (implicitly)

A newly-formed team often needs to address the following before the first Sprint:

  • Product Backlog
  • Projected capacity of the Development Team during the Sprint
  • Definition of “Done”

If these are not addressed before the first Sprint, then they will likely need to be addressed during Sprint Planning, which can place a lot pressure on a new team (especially in environments where it is difficult to build shared understanding of the work).

Product Backlog

Keep it simple.  It’s an ordered list of all the features, functions, enhancements and fixes that might be needed in the end product.  Get the Product Owner to blow these things out into a list.  It doesn’t need to be a complete list.  Just the most important things right now.  A good test is to give the Product Owner 5 minutes.  Whatever the Product Owner can think of in 5 minutes is important enough for the team to start working on.  There are all kinds of techniques that can be used to order the Product Backlog.  The simplest way is to just have the Product Owner eyeball it.  If people are uncomfortable with this, then introduce the other ways.  It doesn’t need to be perfect.  It will get better and become refined and adapted as you go.

Projected capacity of the Development Team during the Sprint

Multiply the number of working days in the Sprint (total days minus Sprint Planning, Sprint Review and Sprint Retrospective, rounding down) by the number of Development Team members by the average percentage team member dedication (hopefully 100%).  If you have weird things going on with team member allocation (not 100%) then you may find it helpful to refer to this blog post.  According to what the Scrum Guide says about Development Team size and Sprint duration, this number could theoretically be smaller (Sprint less than one week), but in most cases no less than 12 (3-member Development Team in a one-week Sprint) and no more than 207 (9-member Development Team in a one-month Sprint with 23 days – the maximum number of weekdays in a month).

Definition of “Done”

This is a list of all of the activities that will go into the intended Increment of the first Sprint in order for it to be done.  The team needs to know this before it can estimate the items in the Product Backlog as a team.  Estimation is not a requirement of Scrum, but is often very helpful in refining the Product Backlog, tracking velocity and making projections into the future based on historical actuals.

Planning with the Product Backlog, projected capacity and Definition of “Done”

In the first part of Sprint Planning, the team looks at the items at the top of the Product Backlog in order to determine what can be done in the Sprint and the Sprint Goal, keeping in mind that it will need to complete the items according to its Definition of “Done”.  Once the team has set a Sprint Goal, it can then create a set of tasks that represent how the work will get done.  All of the tasks should fulfill a specific attribute of the Definition of “Done” or be about the technical parts of the system that need to be built.  The team should try to create a set of tasks each of which are a one-person day effort or less.  Count the number of tasks.  If the number of tasks are close to the number of days of the team’s capacity, the team can be confident that it has a decent Sprint Backlog.  If not, then the the Sprint Backlog and likely the Sprint Goal will need to be adapted.

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Theoretical Team Member Allocation Adjustment for Team Capacity Adaptation Projections Game: Adaptive Planning for Adjusted Team Capacity in Scrum

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Author’s caveat:

Lots of smart people have already come up with lots of ways of doing adaptive planning, and chances are someone has already come up with some variation of this particular approach. I have not yet had the benefit of reading everything that everyone else has already written about Agile and planning, so this has been generated by my own experiential learning on the ground as an Agile coach.  Sometimes, as a ScrumMaster/Agile Coach, you are called upon to be a two-trick pony.  This is my other trick.

 Requirements for team estimation (and planning):

  • Product Owner
  • The whole Development Team (i.e. everyone who will be involved in doing the work)
  • Product Backlog
  • Definition of “Done”

 When team membership changes:

A Scrum Team that is estimating effort against Product Backlog items for project planning and timeline projections and changes team membership for one or more Sprints must also re-estimate the remaining items (or at least the items that will be part of the Sprints in which the different/additional team members are expected to participate) regardless of estimation method (Agile Planning Poker or otherwise). The people involved in doing the work (Development Team members/Sprint) must also be involved in providing team estimates. The Development Team is responsible for all estimates as a whole team and therefore should provide estimates as a whole team. The Planning Poker game is widely understood by Agile experts and successful Agile teams as the best tool for facilitating team estimation. Part of what makes Planning Poker so effective is that it does not only provide accurate timelines, but it also facilitates knowledge-sharing among team members as everyone on the team is required to endeavor to understand the degree of complexity of the work of all other team members in order to deliver each item according to the team’s Definition of “Done”.

When team member allocation is adjusted:

Sometimes, the Development team will have people partially dedicated to the team. After one or two Sprints, it becomes apparent that full dedication of all Development Team members is required for optimal team performance. As result, management can be assisted to reconsider allocation of team members towards 100% dedication to the work of a single Scrum Team. Increased (or decreased) dedication of team members can also be expected to have a corresponding impact on velocity (effort points completed per Sprint). However, the Scrum Master needs to help the team (and their managers) to be careful to avoid planning against the unknown. Scrum allows a team to adapt based on actual historical data. Therefore, planning against minimum historical velocity is strongly recommended as a general best practice. At the same time, if a team starts off with, say, 50% allocation of team members and management decides to bump it up to 100%, it is fairly safe to assume that you will actually get somewhat more out of the team. How much more is never possible to know, as human beings are reliably incapable of predicting the future. The moderate way to approach this is to plan the next Sprint based on previous velocity, finish the planned work early in the Sprint, get a bunch of “extra” stuff done, then calculate velocity of the new and improved team and plan against the new and improved velocity. This allows the team to adapt to actuals and not be blind-sided by unforeseen impediments/bottlenecks.

Sometimes, there is a need for management to get a sense of how much more velocity the team will get from increased team member allocation in order to feel that an informed decision has been made. There is a simple (though not risk-free) method for doing this that I have whipped up after being put on the spot on several occasions. I have decided to call this the Theoretical Team Member Allocation Adjustment for Team Capacity Adaptation Projections Game.


The purpose of this exercise is to provide decision-makers with a sense of how much they are going to get out of adjusted allocation of team members to Scrum Teams. Scrum Teams perform optimally when all team members are 100% dedicated to the team. This game should be used with caution and as a means to help organizations move closer to 100% dedication of all Scrum Team members (at least all Development Team members) and, therefore, eliminate the need for this game. Great care should be taken to not encourage perpetuation of dysfunctional Waterfall habits such as “we will now go twice as fast and get done twice as early with twice the allocation of resources because we have this shiny new crystal ball called Theoretical Team Member Allocation Adjustment for Team Capacity Adaptation Projections Game that tells us so.” As long as no one believes that this is magic, it is likely safe enough to proceed to Step 1.

Step 1 – What is our current velocity?

After the first Sprint, the team should be able to count up the number of Product Backlog items completed and add up the corresponding number of “Effort Points” established during its initial estimation (Planning Poker) sessions. Let’s say for our example that the number completed for Sprint 1 is 21 Effort Points. Therefore, the current velocity of the team is 21. Let’s say that this is not a comfortable realization for the team because at some point in the past it had been estimated that this project would take the team about 5 Sprints to complete. Now, the team has done 21 points in the first Sprint and the total number of Effort Points on the Product Backlog estimated by the team is just under 210. Uh oh… 10 Sprints! Whoops! Now what do we do?! Are the new estimation values wrong? Should we stick to the 5 weeks and just have everyone work overtime on this project? Should we take this to management? Let’s say that this team decides to take it to management. But what if management needs more information than “team velocity = 21, Product Backlog = 210, therefore it’s going to take us 10 Sprints instead of 5”? Never fear, Theoretical Team Member Allocation Adjustment for Team Capacity Adaptation Projections Game is here!

Step 2 – What is our current capacity?

As part of Sprint Planning, the team needs to have a sense of its capacity in order to create the Sprint Goal and Sprint Backlog. Therefore, the team should already have a sense of its own capacity. Let’s say for our example that the (fictional) Development Team had the following projected allocation for the first Sprint:

50%        Chris P. Codemuncher

50%        Larry Legassifulunch

25%        Beth Breaksidal

40%        Gertrude Gamesthadox

40%        Dana Deadlinedryver

The team is doing 2-week Sprints. After calculating the time that the team has allocated for Scrum Events, the remaining time for doing the work of the Sprint is about 8.5 days. Therefore, we can calculate the total allocated days per team member as such:

8.5 x 50% = 4.25 days    Chris P. Codemuncher

8.5 x 50% = 4.25 days    Larry Legassifulunch

8.5 x 25% = 2.13 days    Beth Breaksidal

8.5 x 40% = 3.4 days      Gertrude Gamesthadox

8.5 x 40% = 3.4 days      Dana Deadlinedryver

17.43                              Total combined available days per Sprint

Let’s round that down to 17. That’s the number used by the team to understand its capacity for Sprint Planning. This is a powerful number for other reasons than what we are trying to get at here, but they are worth pointing out nonetheless. For generating the Sprint Backlog in Sprint Planning, this is particularly useful if each task in the Sprint Backlog is a maximum of a one-person-day. Therefore, this team should have a minimum of 17 tasks in the Sprint Backlog and these tasks should all be a one-person-day or less amount of effort. If the team has more than 17 tasks which are all about a one-person-day of effort, chances are the team has overcommitted and will fail to deliver the Sprint Goal. This should trigger the adaptation of the Sprint Goal. In any case, it provides the team with simple transparency that can easily be inspected and adapted throughout the Sprint. For example, with one-person day tasks, each team member should be able to move at least one task into the “Done” position every day and point to that movement every day during the Daily Scrum. Also, this team should be burning down at least 5 tasks every day. If either of these fails to occur, this is a clear signal for the team to inspect and adapt.

Now, let’s get back to our Theoretical Team Member Allocation Adjustment for Team Capacity Adaptation Projections Game. As a result of Steps 1 & 2 we now know that the team’s velocity is 21 Effort Points and that the team’s capacity is 17 person-days per Sprint. For short, we can say:

21            Velocity

17            Capacity

21/17       V/C

(WARNING: This number is dangerous when in the wrong hands and used as a management metric for team performance)

 Step 3 – How much capacity do we hope to have in the next Sprint?

Let’s say a friendly manager comes along and says “you know what, I want to help you guys get closer to your original wishful thinking of 5 Sprints. Therefore, I’m deciding to allocate more of certain team members’ time to this project. Unfortunately, I can only help you with the ‘developers’, because everyone else reports to other managers. I’m concerned that Beth is going to become a bottleneck, so someone should also speak with her manager. But for now, let’s bump Chris up to 100% and Larry up to 75% and see what that does for you. We’re also going to throw in another ‘specialist developer’ that you need for some stuff in your Product Backlog at 100%. How much more velocity can I get for that?”

Okay. So…more allocation = more capacity = more velocity, right? If we acknowledge that this is highly theoretical, and remember the initial WARNING of the game, we can proceed with caution…

But just as we get started on calculating the adjusted allocation of team members, we find out that Beth was actually more like 50% allocated, Dana was more like 15% allocated and Gertrude was more like 30% allocated. We need to recalculate our actuals for Sprint 1:

8.5 x 50% = 4.25 days    Chris P. Codemuncher

8.5 x 50% = 4.25 days    Larry Legassifulunch

8.5 x 50% = 4.25 days     Beth Breaksidal

8.5 x 30% = 2.55 days     Gertrude Gamesthadox

8.5 x 15% = 1.28 days     Dana Deadlinedryver

16.58                               Total combined ACTUAL available days in Sprint 1

16                                    Actual capacity (rounded-down)

21/16                               Actual V/C

As a side note, Beth had to work on a Saturday in order to increase her capacity but she spoke with her manager and thinks that from now on she will probably be able to maintain this degree of dedication to the team without having to work any more overtime.

Now the team can calculate its hoped-for capacity for Sprint 2 and beyond:

8.5 x 100% = 8.5 days     Sally Supaspeshalis

8.5 x 100% = 8.5 days     Chris P. Codemuncher

8.5 x 75% = 6.38 days     Larry Legassifulunch

8.5 x 50% = 4.25 days     Beth Breaksidal

8.5 x 30% = 2.55 days     Gertrude Gamesthadox

8.5 x 0% = 0 days            Dana Deadlinedryver

(Note: Dana is also the Scrum Master with plenty of other work to do for the team)

30.18                               Total combined hoped-for available days in Sprint 1

30                                     Hoped-for capacity (rounded-down)

Step 4 – How much velocity do we hope to have in the next Sprint?

21            Actual Historical Velocity

16            Actual Historical Capacity

30            Hoped-For Future Capacity

x              Hoped-For Future Velocity

Some simple math, loaded with assumptions:

Actual Historical Velocity/Actual Historical Capacity = Hoped-For Future Velocity/Hoped-For Future Capacity

Therefore if 21/16 = x/30, then x = 21 x 30/16 = 39.375

39            Hoped-For Future Velocity

Step 5 – How do we adapt our planning in light of what we now know (assuming we now know something substantial enough to inform our planning)?

Hopefully, not much. The best thing for the team to do at this point is to plan against its actual historical velocity of 21. If team members finish their work in the Sprint Backlog early, they should help out with other tasks until the Sprint Goal is delivered. Then, if the team achieves the Sprint Goal early and has extra time left before the end of the Sprint, then the team can pull additional items to work on from the Product Backlog. If the velocity of the team actually increases as a result of actual increased capacity, then the team can safely plan against its increased velocity beginning in Sprint 3. However, Hoped-For Future Velocity is often way too tantalizing for a team that already strongly (and to some extent, logically) believes that it can get more done with more capacity. So, most teams will usually plan to do more in light of this knowledge and that’s fine. Scrum allows them to inspect and adapt this plan at least every day. The team will figure it out.

Thank you for playing Theoretical Team Member Allocation Adjustment for Team Capacity Adaptation Projections Game. I hope it was as fun to play as it was to create!

See you next time,


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