I am going to show more details now, because it is interesting to follow what happens closely. (Well, it is if you are a management nerd, like me.) Remember that our original estimate, based on the average roll of the die, was that we'd get a throughput of 35 beads in an iteration. (An iteration consists of 10 sequences of 8 die rolls.) That prediction failed. The average throughput was only 28.4.
The second try to predict the end of the project used another method. I used the average flow rate, measured over the first five iterations. This prediction indicated that 1.6 more iterations would be needed. 5+1.6, rounded up, is a total of 7.
Let's see how the flow based prediction holds up. Here is state of the system two sequences into iteration 6:
After four sequences in iteration 6, and rolling a highly unlikely series of fours and fives, sequence 3 yielded 5 beads. Sequence four yielded only 1 though, so it evens out:
At the end of iteration 6, the model looked like this:
This is the system four sequences into iteration seven:
- 21/1=21 ==> 188/21= 8.9
- (21+32)/2= 26.5 ==> 188/26.5= 7.1
- (21+32+28)/3= 27 ==> 188/27= 7.0
- (21+32+28+36)/4= 29.25 ==> 188/29.5= 6.4
- (21+32+28+36+25)/5= 28.4 ==> 188/28.4= 6.6
- (21+32+28+36+25+31)/6= 28.83 ==> 188/28.83= 6.6
In other words, measuring flow is better than estimating time!
One thing to note is that the model used here was balanced, i.e. the capacity was the same at each stage. In reality that is not the case. such differences in capacity would make traditional time estimates even more unreliable. I'll look into that, and more sophisticated methods of calculating project durationin a future blog entry. First I'll write myself a little simulation software. I'm getting tired of rolling the die.
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