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A team with 3-day reviews ships half as much as one with same-day reviews. Same people, same skills. Calculate what your review wait time is really costing.

## What the math actually says
Let's make it concrete. A team of 5, 2-week sprints, 2 days base dev work:
- **With 3-day reviews:** 260 stories/year
- **With same-day reviews:** 520 stories/year

Same people. Same skills. **Double the output.**

_"But that's theoretical,"_ you might say. _"Real teams have overlap, parallel work, varying review complexity."_ Here's the thing: if you reduce cycle time from 5 days to 2.5 days, throughput doubles. That's not a theory - that's Little's Law - that's math. The overlap and parallel work you're describing? They're already baked into cycle time. However your team works, **doubling the time from start to done means half as much gets done**.



## Need management buy-in? Here's what it costs.

Fixing slow reviews isn't free - it takes process changes, maybe tooling, definitely prioritization. If you need to make the case for investing in this, here are the numbers:

**The throughput gap:**
To match a faster team's output, you'd need to **hire an entire second team**. That's a $500k+ decision vs a process change.

**The hidden costs:**
At $75/hour loaded developer cost, 3-day reviews create:
- **37.9 days/year** lost to context switching
- **24.3 days/year** lost to merge conflict rework
- **$37,275/year** per team in hidden costs

That's 497 hours of developer time. Gone. Not to building features - _to waiting and recovering from waiting._


Plug in your numbers. See what the math says for your team.

What does your typical review wait time cost?

## The question worth asking
Team dynamics vary. Workloads differ. What works for one team won't work for another. But here's a question worth asking: **Do you actually know how long your reviews take?**

Not a guess. Not "a day or two, usually." The actual median time from PR opened to review complete. Most teams have never measured it. Which means they've never questioned whether it could be better.

Once you can see where your issues actually spend their time - stage by stage - you can decide what's worth fixing. And once you fix the wait states, the throughput gains from Little's Law stop being theoretical.

The hidden queue: what 3-day reviews actually cost

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Every missed deadline has a cause. Every blown estimate has a reason. 

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How Many Sprints Before You Spot the Pattern?

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You Know It's a Bad Estimate. But Can You Prove It?

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Same Story Points. Different Outcomes. Why?

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You Run Planning Poker Every Sprint. What Happens to That Data?

Stakeholders add urgent work mid-sprint. You know it hurts delivery—but without concrete data, you can't quantify the damage or push back effectively.

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How Much Did That 'Quick Request' Actually Cost?

Story points tell you relative size. They don't tell you when something will ship. The gap between estimation and prediction is where promises break.

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Your Team Estimates. But Can You Actually Predict?

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Where Does Your Team's Time Actually Go?

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It's Been In QA For How Long?

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Turn Status History Into Process Insights

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The hidden queue: what 3-day reviews actually cost

Time In Status

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