What We’re Learning from the NoEstimates Game

NoEstimates workshop at the 2018 LeanAgileUS conference

Having facilitated the NoEstimates game for more than a year, in many places around the world with differing groups — most recently at the outstanding LeanAgileUS conference — I’ve observed some patterns for success. Though these “winning strategies” may at first appear to be useful only if you want to play the boardgame, I believe that they likely translate into real-world success in intangible-goods (e.g,. software) delivery processes.

(Spoiler alert: If you haven’t played the game yet but plan to, you may not want to read the following — unless, of course, you want to cheat your way to victory!)

To remind you of some context: The game is a simulation of a group of interdependent work teams, each with an identical backlog of 25 work items. The teams play in simulated days, and, depending on how long the session is, usually play between 15 and 30 days. Teams earn “money” based on how much value they deliver, according to the following table:

Delivery Time (Days) Value ($)
1-2 days $700
3-5 days $400
6+ days $300
Urgent -$100 per day

Using data that I’ve collected from the teams over several sessions, I’m seeing that the teams who earn the most money per day are also the ones that are most predictable. That is, while they can’t do anything about some of the variation (e.g., the essential effort required to do the work), they either consciously or unconsciously follow common policies that reduce other kinds of variation. This appears to support Dan Vacanti’s idea that “doing predictability” is a rewarding business strategy.

Teams typically earn the most value per day and deliver most predictably by following these policies:

  • Limit work in progress: We generally know that this is a helpful policy. The learning for me with the game is that the optimal work-in-progress levels are even lower than one might expect, typically half (or fewer than) the number of people on the team. Even four or five-person teams who follow a single-piece flow policy don’t trade off much, if any, throughput. For small teams, the difference between having three-to-four WIP and one-to-two WIP can yield twice as much revenue per day in the game!
  • First-in, first-out: It’s easier to do this when you’ve got low WIP levels, of course. And single-piece flow is the natural extension of this policy. The game includes a few random “urgent” work items, which cost the team $100 each day they’re in progress, so they’re highly incentivized to “jump the queue” with these cards. Even so, the teams that have low WIP (a conWIP of one or two) are able to continue to honor their FIFO policy, which creates better predictability, throughput and value delivered. (Dan Vacanti has written about this.)
  • Cross-functional collaboration: Probably because the game makes both the scarcity of effort available and the work highly visible, players almost naturally “focus on the work, not the worker.” Rather than optimize in their specialty areas, players on successful teams instead work outside their specialties, where they get only half credit for their effort. (This appears to support the research that Dimitar Bakardzhiev has done.)
  • Flow over utilization: Winning teams generally don’t mind not fully utilizing all of their capacity, preferring to leave some effort on the table (literally, in the form of effort cubes) rather than pulling in enough cards for everyone to “stay busy.” One of the event cards attempts to entice teams to improve utilization, but nearly every team chooses not to.
RPS_Image-289 cropped
This team executes a strategy of limiting WIP to fewer than half the number of team members at the 2018 LeanAgileUS conference.

Although these lessons are from simulations, I think that, to the extent that the game emulates real work, the lessons can be extended into our actual work environments. In general, these gameplay experiences — because they are rooted in the incentive to optimize value — tend to manifest the mantra “Value trumps flow, flow trumps waste reduction.” So why to teams playing the game seem to know these lessons almost intuitively? The reasons aren’t necessarily anything that can’t also be done in real life: Connect more directly to the value feedback loop (John Yorke’s recent post on verifying value of user stories helps with this) and use flow metrics (e.g., delivery time depicted on a scatter plot) to make your process more predictable. “Keeping score” — of things that matter, anyway — doesn’t need to be limited to games, after all.

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