Forecasting with historical data: replace confidence with evidence

Most delivery teams are stuck in a horrible reality of being pushed to provide estimates which are then turned into commitments. The estimates are an expression of confidence at best.

Dates are chosen because they feel reasonable, or because someone senior is comfortable with them, or because a plan demands certainty. The organisation then rallies around that date, progress is reported optimistically, and reality is politely ignored until the moment it can no longer be hidden.

​When the estimates inevitably turn out to be wrong, we treat that as a failure of execution rather than what it is: a failure of thinking.

Kanban acknowledges the probabilistic nature of our work and takes a fundamentally different approach to forecasting. It does not try to pretend we can have complete certainty. It accepts uncertainty and then manages it explicitly.

Why deterministic deadlines keep letting you down

Traditional delivery forecasting assumes that if you plan hard enough, break work down far enough, and monitor closely enough, outcomes will become predictable.

That assumption simply does not hold in knowledge work.

Software, product development, change initiatives, and most modern delivery contexts are full of:

•     hidden complexity
•     unknown dependencies
•     learning that cannot happen up front
•     interruptions that cannot be scheduled
•     variability in work size that never fully disappears

Estimates attempt to compress all of that into a single number. Forecasts built on estimates inherit all of that optimism and then amplify it into pure fantasy.

This is why organisations end up with detailed plans that are consistently wrong in exactly the same direction.

The main kanban insight about forecasting

Kanban starts from a much less flattering but far more useful premise:

Nothing is as predictable as you want it to be.

Instead of asking “How long will this take?”, in Kanban, we ask:

“How long has similar work actually taken in the past?”

This is not about mistrusting teams. It is about trusting actual data over intention.

Historical delivery data already contains:

•     the real world impact of dependencies
•     the cost of waiting
•     the effect of interruptions
•     the consequences of starting too much work
•     the variability you keep pretending isn’t there

Ignoring that data in favour of fresh guesses is simply wasteful.

Cycle time is the raw material of forecasting

All Kanban forecasting relies on historical cycle time data.

Cycle time measures how long work took from start to finish, as defined by your workflow. Very import to note that, we capture elapsed time, not effort.

Elapsed time then includes:

•     waiting
•     reviews
•     queues
•     rework
•     handoffs
•     external delays

In other words, it reflects the entire system, not just the team.

If your cycle time data is clean and your workflow definition is honest and transparent, you already have everything you need to forecast. No estimation workshops required.

Why Monte Carlo forecasting is a big deal (even if you never say the name)

Monte Carlo forecasting often sounds intimidating, but the underlying idea is actually quite simple.

Instead of producing a single answer (e.g. a date), it produces a range of possible outcomes based on real historical behaviour.

It asks questions like:

•     “If we complete work at roughly the same rate as before, what might happen?”
•     “What’s the likelihood of finishing by a given date?”
•     “How much risk increases if we start more work?”

The output is not a deadline, a commitment or a promise. It is a probability distribution.

This is a major shift for most leaders, because it reframes delivery conversations away from certainty and towards risk.

Why leaders struggle with probabilistic forecasts

Many managers or delivery leaders are uncomfortable with probabilistic forecasts because they don’t provide a single answer.

That discomfort tells a story.

What people often want is not accuracy but reassurance. As such a date feels reassuring, even when it’s wrong while a probability feels vague, even when it’s honest.

But delivery leadership is not about reassurance. It’s about making informed trade-offs under uncertainty to navigate towards the goal.

Probabilistic forecasts make those trade-offs transparent. And that’s often uncomfortable.

Forecasting as a decision support tool

One of the most damaging and irrational habits organisations have is treating forecasts as commitments that must be met at any cost.

Once a date is spoken out loud:

•     scope gets bent to protect it
•     risk gets hidden
•     teams get pressured
•     reality gets negotiated

Kanban forecasting is designed to do the exact opposite.

Forecasts exist to inform decisions such as:

•     Should we start this now or wait?
•     What happens if we delay something else?
•     How much certainty do we need before starting?
•     What risk are we knowingly accepting?

If a forecast cannot change a decision, then it’s purely academic.

Why forecasting without WIP control is meaningless

Here’s the part many organisations miss.

Forecasting assumes some level of system stability. If you continuously change priorities, inject new work, and overload the system, then the work variability rate goes through the roof and your historical data quickly becomes irrelevant.

This is why Kanban insists on WIP control.

Without WIP control:

•     throughput fluctuates wildly
•     cycle time distributions degrade
•     forecasts become meaningless
•     risk compounds invisibly

Forecasting requires discipline and that’s a common failure for many teams.

If leaders want better forecasts, they must first stop starting more work than the system can handle.

What good forecasts reveal (that gantt charts hide)

Used properly, historical forecasting reveals things that planning meetings never do:

•     how sensitive delivery is to extra WIP
•     how quickly risk escalates when work ages
•     how much variability really exists
•     how fragile “stretch goals” actually are

These insights are often uncomfortable. They challenge assumptions about capacity, productivity, and urgency.

That discomfort is a feature, not a flaw.

Confidence is cheap, evidence is rare

Many organisations confuse confidence with competence.

Confident forecasts feel decisive. Evidence-based forecasts feel tentative. But only one of them improves predictability over time.

Kanban does not promise certainty. It offers something far more useful: situational awareness.

It allows leaders to say:

•     “Here’s what the data suggests”
•     “Here’s the risk we’re taking”
•     “Here are the options available to us”

That is what responsible delivery leadership looks like, even if it feels tentative.

If your forecast never changes, it was never useful

Here is a simple test for your forecast.

A good forecast should evolve as:

•     WIP changes
•     throughput changes
•     work ages
•     conditions shift

If your forecast is fixed, unchallenged, and rarely revisited, then it’s unlikely to be helping with decisions. Perhaps it’s defending your initial estimate?

Kanban forecasting replaces any deterministic narrative with evidence.

In the next post, we’ll look at Ageing Risk Highlights, and how combining age, percentiles, and SLEs helps you spot “silent blockers” before they turn into visible failures.