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Writer's pictureFernando Cuenca

Why collecting Lead Time data is a good idea

"How long will that take?"

Is that a question your stakeholders expect you to answer? If so, is your first instinct to think about estimating EFFORT


Does it happen, then, that the "ACTUAL" doesn't match the "ESTIMATE", triggering calls for "WE HAVE TO IMPROVE ESTIMATION!", and the cycle starts all over again? 


👉 We've learnt a big deal about the "FORCES OF NATURE" that cause lead time to be what it is. 


EFFORT is certainly a variable, but even when we know exactly what the effort involved is, some days we get done more than others: there's RANDOM VARIABILITY in our raw ability to do work. It can feel like we're "ROLLING DICE" each day (and if you've played the GetKanban simulation, then you know what I'm referring to here 🎲 🎲 )


But there are other factors affecting Lead Time, which are RANDOM in nature:


  • 🚧 DELAYS produced by external factors 

  • ✋ WAIT STATES in the design of your process 

  • ⏰ Decisions to PRIORITIZE other WORK 

  • 😓 TASK SWITCHING between several equally important tasks. 


Effort is only a (small) portion of Lead Time. 


By collecting and analyzing, Lead Time data you can understand what else goes into the "how long will it take?" question, and then use those insights to introduce improvements it to achieve higher SPEED and PREDICTABILITY.


While it is true that Lead Time data can be used for producing forecasts, estimates, and other predictions, the main value of collecting this metric is because it can teach you a lot about your own process.


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