Value Stream Analysis: The honest mirror.

Many teams jump from kick-off straight into value-stream design — and skip the analysis. The result: a pretty target state that misses reality. The Value Stream Analysis is the tool that prevents this break. It's the honest mirror of the current state.

Value Stream Analysis doesn't start in the meeting room

It starts with pencil, stopwatch and a blank sheet — at the place where value is created. Anyone who first draws the value stream and then "verifies" it has already started wrong. The order is always: walk, observe, measure — then sketch.

"ERP data isn't wrong — it's just incomplete. What really happens, you only see at the process."

Seven dimensions per process step

For each step in the value stream, seven dimensions are captured — not estimated, but measured or at least sampled:

Cycle time
How long does one piece / one operation take?

Changeover
Switching between variants / orders.

Availability
Planned vs. actual machine uptime.

Batch size
How large are the production batches?

Inventory
Measured in days — not units.

Quality
First-pass yield or rework ratio.

People & shift model
Who works when, with what qualification?

The central KPI: VAR

From the data, the Value-Added Ratio (VAR) emerges: value-adding time divided by lead time. Realistic values in manufacturing companies almost always sit between 1 and 5 percent. Sounds shocking — but it's normal. And that's exactly where the biggest potential lives.

Anyone who reacts "shocked" to VAR has missed the point: the gap between value-adding time and lead time isn't a problem — it's the improvement budget for the next 18 months.

Don't forget the information flow

Material flow is obvious. Information flow is invisible — and usually the bigger brake. When is planning done? Which department gets which information at which moment? Where do queues form because an approval is missing?

In 8 out of 10 projects we find more leverage on the information level than on the material level. Anyone who omits the information flow in analysis only optimises half the system.

Three common mistakes

Estimating instead of measuring. "It takes about…" isn't enough. Three real data points beat ten estimated ones.
Observing only one shift. Value streams often look completely different in late shift versus early shift.
Forgetting the customer. What is value-add — from the customer's perspective? Not from the plant's.

What the analysis delivers

At the end of the analysis you have three things: a clean current-state map, a quantitative overview (LT, VAR, inventory in days) — and most importantly, a shared reality in the team. Only on that basis does Value Stream Design pay off. Before that, it's wishful thinking.