- #1
Last_Exile
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I'm trying to understand this problem and could do with some help.
A small business is trying to measure their delivery performance in terms of "on-time, in-full & error-free".
It sent out product to 4 customers (A,B,C,D) and notes down what the performance was: 0 = not ok, 1 = ok.
(Apologies for not being able to do a table)
__________A_B_C_D
on time____1_0_1_1
in full______1_1_1_0
error free___0_1_1_1
Overall_____0_0_1_0
It is clear that only 1 out of 4 (25%) actually received goods on-time, in-full & error-free.
But then, a customer calls Sales and asks "What are the chances of my order being on-time in full and error-free?
The Sales guy thinks to himself:
Well, 3 out 4 (75%) were on time, 75% were in full & 75% were error-free so the chances are:
0.75 x 0.75 x 0.75 = 0.42
and tells the customer "There is a 42% chance your order will be on-time, in-full & error-free"
Can anybody explain why there is such a difference between the actual performance and the probability?
Thanks in advance.
A small business is trying to measure their delivery performance in terms of "on-time, in-full & error-free".
It sent out product to 4 customers (A,B,C,D) and notes down what the performance was: 0 = not ok, 1 = ok.
(Apologies for not being able to do a table)
__________A_B_C_D
on time____1_0_1_1
in full______1_1_1_0
error free___0_1_1_1
Overall_____0_0_1_0
It is clear that only 1 out of 4 (25%) actually received goods on-time, in-full & error-free.
But then, a customer calls Sales and asks "What are the chances of my order being on-time in full and error-free?
The Sales guy thinks to himself:
Well, 3 out 4 (75%) were on time, 75% were in full & 75% were error-free so the chances are:
0.75 x 0.75 x 0.75 = 0.42
and tells the customer "There is a 42% chance your order will be on-time, in-full & error-free"
Can anybody explain why there is such a difference between the actual performance and the probability?
Thanks in advance.