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- Many missed dates start at the handoff between sales and planning.<\/li>\n
- Planning systems often use rated capacity, not measured availability.<\/li>\n
- Downtime, micro-stops and real changeovers are missing from master data.<\/li>\n
- Measured plannable hours make delivery promises more reliable.<\/li>\n<\/ul>\n<\/div>\n
What Bernd Vollm\u00fcller writes about the handoff between sales and planning<\/h2>\n
Bernd Vollm\u00fcller of Vollm\u00fcller & Reese, a German consultancy for production and maintenance, makes a point in his article Die Wahrheit \u00fcber Produktionsoptimierung: Es geht nicht nur um die Maschinen<\/a> (The truth about production optimization: it is not only about the machines) that many plant managers know from experience: the largest losses often do not occur at the machine itself, but at the handoffs between departments. He gives a typical interface as an example: “Wenn die Liefertermine des Vertriebs in der Planung landen, hierbei aber u. a. die tats\u00e4chlichen Durchlaufzeiten nicht ber\u00fccksichtigt wurden” (When sales delivery dates land in planning, but among other things the actual throughput times have not been taken into account.)<\/p><\/blockquote>\n
He describes three levers for a holistic approach: data, process interfaces and clear ownership of the overall result. He also stresses that collecting raw data is not enough: it has to become information you can act on. His article provides the organizational view. This article adds the measurement side: which availability figure for a machine belongs in the plan, where does it come from, and how do you keep it current?<\/p>\n
Theoretical capacity and measured availability: two different numbers<\/h2>\n
In most planning systems, a machine’s capacity is master data: shift time multiplied by rated output, sometimes with a flat allowance for breakdowns that was set years ago. The figure is convenient because it rarely changes. That is exactly the problem. On the shop floor, availability changes all the time.<\/p>\n
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- Unplanned downtime:<\/strong> breakdowns, missing material, operators waiting. None of it appears in a master data record.<\/li>\n
- Micro-stops:<\/strong> stops lasting a few seconds to a few minutes are rarely logged by hand, yet they add up over a shift.<\/li>\n
- Changeovers:<\/strong> the standard time in the system rarely matches the real changeover, ramp-up included.<\/li>\n
- Speed losses:<\/strong> a machine running below its rated speed reports no stop, but delivers fewer parts.<\/li>\n<\/ul>\n
An illustrative example: planning assumes two full shifts at rated speed for a press. Measurement shows that, on average, the press produces at rated speed for noticeably fewer hours, because micro-stops and long changeovers use up part of every shift. Every order scheduled on that press therefore starts with a delay that only becomes visible once the date is already at risk. Sales confirmed the date correctly according to the plan, production worked correctly, and the delivery is still late.<\/p>\n
![\"Four-step](\"https:\/\/teeptrak.com\/wp-content\/uploads\/2026\/11\/teeptrak-vollmueller-en.png\")
<\/figure>\nWhere the gap hits the delivery date<\/h2>\n
The table below shows typical planning assumptions, what automatic measurement reveals, and the consequence for the promised date.<\/p>\n
- Micro-stops:<\/strong> stops lasting a few seconds to a few minutes are rarely logged by hand, yet they add up over a shift.<\/li>\n
- Unplanned downtime:<\/strong> breakdowns, missing material, operators waiting. None of it appears in a master data record.<\/li>\n