Value stream mapping — current-state analysis

A value stream map traces a process from its start to the point a customer receives value, timing each step and the wait between steps. The current-state analysis here does the arithmetic a team usually does by hand on a whiteboard: total lead time, process (value-added) time, flow efficiency, which step caps throughput, and where wait time is piling up — each one computed directly from what you measured, not estimated. It doesn't replace walking the process yourself; it replaces the error-prone parts of doing the math afterward.

Scope

State what's in and out of this value stream in a sentence or two — which product/service family, which start and end point. A value stream with no stated boundary quietly turns into a map of everything, which maps nothing well.

Process steps

List each processing step in order with its process time (the time actually spent working on the item) and the wait time after it (queue time before the next step starts). Add operators, uptime, defect rate, or WIP count only where you actually observed them — leave them out rather than guess, since the waste calculations only quantify what has real data behind them.

Lead time · Process time · Flow efficiency

Lead time, process time, and flow efficiency are computed for you from the step data — you shouldn't need to touch these by hand. If a number looks wrong, the fix is almost always a step's process or wait time, not the formula.

Waste — the eight wastes

Waiting, inventory, and defects are quantified directly from your step data wherever you captured it. The other five wastes stay marked as needing direct observation until your notes give them something concrete to point to — that's not a gap in the tool, it's the tool refusing to invent a number it can't support.

Where the wait concentrates

Ranked automatically by wait-time contribution, showing where flow is choked worst — override the axis to cost or impact if dollar value or customer effect matters more than raw wait time for this specific value stream.

Countermeasures

Rank by impact and effort (FR-M3-6), and name the specific finding each one addresses — the largest wait contributor, the capacity constraint, or a named waste. A countermeasure that doesn't trace to a finding is a good idea with no evidence behind it.

The example below is a full current-state analysis: step-by-step timing data, a lead time and flow efficiency figure with the arithmetic shown, the step that caps throughput and the step where wait time is worst, waste findings that are honest about which are measured and which are still qualitative, and countermeasures that each name the finding they address.

• Flow efficiency is estimated instead of computed from real step data.

  A guessed 30% and a computed 14% lead to very different conversations. The whole point of doing this analytically is to replace the guess.

• Every step gets flagged as a problem.

  If everything is flagged, nothing is prioritised — the vital-few discipline (master-plan §1.3) means naming the one or two steps actually worth acting on first, not cataloguing every step with nonzero wait.

• A waste category gets a number with no real data behind it.

  Overproduction, motion, and transportation usually can't be read off step-timing data. Forcing a number there is worse than leaving it qualitative — it looks rigorous and isn't.

• The value stream's scope keeps growing mid-analysis.

  A value stream mapped without a stated start/end boundary drifts to cover every variant and exception, and the resulting numbers describe nothing in particular.

• Countermeasures target the step that's easiest to change, not the one the data points to.

  The capacity constraint or the largest wait contributor is where the value stream actually loses time. An easy fix elsewhere feels productive and moves nothing.