The value of 1 day of factory cycle time

Thank you everyone who participated in the last poll. Participation was significantly down beside the fact, that there were plenty of post viewers. My interpretation is that the readers are not too sure about the actual value of 1 day of cycle time. This observation is also in line with my personal experiences from working in semiconductor wafer FABs. It seems like that everybody acknowledges that fast cycle time is a good thing and it would be valuable to work on that – but what the actual value is – there is no clear understanding. The results of the poll itself look accordingly:

the same data sorted by the $ value:

The majority of voters pointed towards a few hundred thousand dollars but 33% said it is a million dollars or more !


I think one of the reasons why the real value of cycle time is not clearly defined is the missing of an accepted and standardized model how to calculate or at least how to estimate. I have seen a few different approaches from very simple to very complex – and what is worse – different models will generate different results, which does not really help to build confidence in the numbers.


One very simple model is the following:
If we look at our factory operating curve and assume we are running at our voters favorite operating point:

800 wafer starts per day at a X factor of 3 (or 60 days cycle time)

we can extrapolate the value of 1 day of cycle time by the following logic:

  • 800 wafer starts per = 800 x 365 = 292,000 wafers per year
  • 292,000 wafers per year times $1,150 selling price = $335.8 million revenue

If we now use the factories operating curve and “look” to the left and right of the current operating point we can do a very simply estimation of the value of 1 day of cycle time:

Since the operating curve is non linear there is a difference if we look towards lower or higher utilization – but if we assume only small changes around the current point – we can ignore this.

Towards higher utilization:
plus 50 wafers starts will lead to 20 days more cycle time or in a simple ratio:
2.5 wafers per 1 days of cycle time.
2.5 wafers x 365 days x $1,150 = ~ $1 million revenue

Towards lower utilization:
minus 100 wafers starts will lead to 20 days less cycle time or in a simple ratio:
5 wafers per 1 days of cycle time.
5 wafers x 365 days x $1,150 = ~ $2 million revenue


This is a big difference between the 2 numbers – but even if we use the smaller one to be on the safe side – $1 million is a serious number. Keep in mind, all the other benefits of faster cycle time are ignored in this simple model.


Another model – significantly more complex – which takes into account:

  • revenue gain due to faster cycle time versus falling selling price
  • revenue gain due to faster yield learning

It was developed by professor Robert Leachman. He teaches this method at the University of California, Berkeley. The complete coursework can be found here: LINK


I will not dig in more into the “fascinating world” of models to calculate the value of cycle time – instead will discuss a bit more the practical application of the value of speed.

Clearly the value of speed depends also on the overall market situation. In very high demand situation customers might be willing to tolerate higher cycle times, if they just can get enough supply. Factories tend to start more wafers in these conditions and simply “cash in”.

Still if the engineering team could implement measures to reduce the factory cycle time by lets say 1 day – management could “use” this gained 1 day of FAB capability to start a few more wafers – driving the FAB back to the previous speed, but deliver more wafers = more revenue.

In this scenario the question is:
1 day of cycle time is worth $1 million. How much will be the engineering team allowed to spent to enable this 1 day of cycle time reduction ?

This comes down to how ROI is handled in the company – but there is a path to calculate this. It will enable dollar spending for cycle time and this is based on a model – which will support decision making – if a measure or change is worth implementing or not.

In my next post I will start discussing a few more details around the operating curve and most important – what can be done to improve.


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