Bottlenecks, part 3

Merry Christmas and Happy Holidays !

I hope everybody is having a good time with friends and family and after a lot of good food is ready sit down and discuss more details about factory bottlenecks. In today’s post I will start zooming in on the 3 not grayed out metrics from the poll results picture below:

To disclose my personal opinion upfront: I think that “highest average lot wait time” (or metrics that are derived from this) is the most objective way to measure and define what is the true factory bottleneck. But lets discuss all 3 of the metrics a bit.

highest miss of daily moves vs. target

I think every factory in the world is measuring and reporting in some way the number of “Moves” – the number of wafers which were processed/completed on a step in a day, a shift, an hour, for the whole FAB or departments and down to individual process flow steps and grouped by equipment or equipment groups.

“Moves” is a very attractive and popular metric for a lot of reasons:

  • Moves can be easily measured and aggregated in all kind of reporting dimensions
  • based on the numbers of steps in a process flow (route) it is clear, how many Moves a wafer needs to complete, to be ready to be shipped
  • Moves is a somewhat intuitive metric – humans like to count
  • target setting seems to be pretty straight forward – “more is better”

I personally think, measuring a FAB via “Moves” as the universal speedometer can be very mis-leading and might drive behaviors – which are actually counter productive – for the overall FAB performance. At the very least a well thought through and dynamic target setting is needed to steer a factory which is mainly measured by the number of Moves. The danger of Moves as the key metric might be less in fully automated factories, since the actual decision making is done by algorithms which usually incorporate a lot of other metrics and targets and therefore Moves are more an outcome of the applied logic, less an overarching input and driver.

In manually operated factories, where operators and technicians make the decisions, which lot to run next and on what equipment, a purely Moves driven mindset can do more harm then good – to the overall FAB performance.

I think a lot has been written and published on this topic and there are strong and different schools of thought out there, but I’m fully on board with James P. Ignizio’s view in his book

In chapter 8 of his book – titled

“Factory Performance Metrics: The Good, The Bad, and The Ugly”

“Moves” get a nice talk – in the “Bad and Ugly” department – for the very reason, that Moves can drive counter productive behavior. If you are interested in this topic – I strongly recommend reading the book.

Before I jump to the next metric – I just wanted to say – that I think that Moves are important to understand and is a useful indicator if used within the right context, but not “blindly” as the most important indicator, which drives all decision making.

highest amount of WIP behind a tool group

Almost one third of the voters picked this metric. Similar to Moves there are a lot of advantages to measure WIP:

  • WIP can be easily measured and aggregated in all kind of reporting dimensions
  • using “Little’s law” it is easy to define WIP targets
  • WIP is a very intuitive metric, especially in manual factories – is my WIP shelf full or empty ?

In general – for daily operations – having a lot of WIP is seen as problematic, since it might lead to lots not moving, starvation of downstream steps and tools, long lot wait times before they can be processed. So high WIP is not a desirable status and very high WIP must be for sure a problem. I think here as well – it depends. For example it depends on what is the target WIP for the given context (like a tool group) to just try to lower the WIP as much as possible (“at all cost”) might lead to generating WIP waves in the factory and to underutilization and lost capacity.

Why do I not 100% subscribe to the highest WIP = the bottleneck ? It is simply, that the tool group with the highest WIP not necessarily has the worst impact on the FAB performance. Here are some data points for this:

Let’s assume we have a very small factory running a very short route – with only 30 steps. If we plot a chart showing the WIP (in lots) per step for each step and sort the steps in the order of the process flow – meaning lot start on the very left and lot ship on the very right – we get what is typically called a line profile chart.

In the picture below our factory is perfectly balanced ( if we define balanced as lots per step – another great topic to talk about) because on each step there are currently 3 lots waiting – or processing.

If we look a bit closer, different steps are of course processed on different tool groups, if we add this detail, the same factory profile looks like this:

For example tool group 2 has 2 steps in the flow and tool group 9 has 3 steps. Our bottleneck metric is the aggregation of the WIP by tool group (“highest WIP behind a tool group”). To find out, which tool group this is, we simply aggregate the same data from the line profile by tool group instead per step:

Tool group number 1 has the highest WIP of all tool groups in this FAB – it clearly must be the number 1 bottleneck – I do not think so. As discussed earlier, there is more content needed. For example, if tool group 1 is a scrubber process, which is typically in the flow a lot of times and it is an uncomplicated very fast process, having the overall highest number of lots there is not necessarily the biggest problem of the factory. Yes, one can argue, still it would be nice to have less WIP sitting at a scrubber tool set, but this is already part of the missing context, I mentioned earlier.

Measuring and reporting WIP is an absolute must in a semiconductor factory, but interpreting WIP levels and assigning them attributes like “high”, “normal” or “low” needs a very good reference or target value. Setting WIP targets should be done via math and science, to reflect what is the overall factory desired WIP distribution – in order to achieve the best possible FAB performance.

Before I close this topic for today – let me say: my simple “perfect balanced” line from the pictures above might not be balanced at all, if we incorporate things:

  • different steps / different tool groups have very likely different capacities
  • different raw processing times
  • might be batch or single wafer tools
  • might sit inside a nested time link (queue time) chain

At this point I will pause and hope that I could stimulate some thinking and of course would love to hear feedback from the readers out there. The next post will be fully dedicated to the last open metric …


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