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- Nominal cycle time and flat availability make the map too optimistic.<\/li>\n
- Micro-stops are missing from almost every data box, yet they cost a lot of capacity.<\/li>\n
- Measured values can move the bottleneck to a different process step.<\/li>\n
- Every value is labeled measured, calculated or estimated.<\/li>\n<\/ul>\n<\/div>\n
What Balane writes about value stream mapping: label where every number comes from<\/h2>\n
Jonas H\u00f6ttler of Balane GmbH, the Munich company that develops the FlowVisual process modeling and simulation software, has published Wertstromanalyse: Schritt-f\u00fcr-Schritt-Anleitung mit Beispiel (inkl. Software-Vergleich)<\/a> (in German). The article walks through seven steps from choosing the product family to the future state, works through an example based on a loan application process and explains the key metrics, from takt time to lead time and flow efficiency. In the section on moving from a paper VSM to a robust simulation model, it sets a rule that many plants lack: “Jede Zykluszeit, jede FPY-Quote, jedes Volumen sollte markiert sein als gesch\u00e4tzt, berechnet oder gemessen.” (“Every cycle time, every FPY rate, every volume should be marked as estimated, calculated or measured.”)<\/p><\/blockquote>\n
The Balane article provides the method and the path to simulation. This article starts from the “measured” category: how to get measured values for the steps of a production line, which data box fields benefit most, and why priorities can shift as a result. The method stays the same; only the data underneath becomes more solid.<\/p>\n
Why estimated values distort the map<\/h2>\n
A value stream map is often built in a few days: a gemba walk, talks with shift leaders, a look at the routing, a few stopwatch readings. That is enough for material and information flow. For the machine data boxes, four biases keep coming back:<\/p>\n
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- Cycle time comes from the routing.<\/strong> The nominal cycle time goes on the map. The real cycle time drifts with tool wear, material or reduced speed after an incident, and that drift is not in any master data.<\/li>\n
- Availability is a flat rate.<\/strong> The same percentage is often applied to every machine. A machine that regularly waits for material and one that hardly ever stops then look the same on the map.<\/li>\n
- Changeover time comes from memory.<\/strong> People quote a good day, without the ramp up to the first good part. We explain how to capture the real changeover in Changeover time: how to really measure it, ramp-up included<\/a>.<\/li>\n
- Micro-stops are invisible.<\/strong> Interruptions of a few seconds to a few minutes never reach an incident log. Added up, they can cost more time than major breakdowns, yet they appear nowhere on the map.<\/li>\n<\/ul>\n
Then there is the sampling effect: on the day of the walk, the line runs under observation, often on the morning shift with the most experienced crew. The night shift and the difficult variants are missing from the sample.<\/p>\n
![\"Four](\"https:\/\/teeptrak.com\/wp-content\/uploads\/2026\/11\/teeptrak-wertstrom-messdaten-en.png\")
<\/figure>\nWhich values go into which data boxes, and where they come from<\/h2>\n
For every process step with a machine, it pays to measure over two to four representative weeks, across all shifts. The table shows which fields benefit:<\/p>\n
- Availability is a flat rate.<\/strong> The same percentage is often applied to every machine. A machine that regularly waits for material and one that hardly ever stops then look the same on the map.<\/li>\n
- Cycle time comes from the routing.<\/strong> The nominal cycle time goes on the map. The real cycle time drifts with tool wear, material or reduced speed after an incident, and that drift is not in any master data.<\/li>\n