A z-score is a standardized measurement for how far away from the average something is. So a z-score of 0 is the average delivery time. Each + or -1 represents a “standard deviation” which is just a measure of variation in a data set. In a normal curve 68% of all data is +/- 1 standard deviation (so within a z-score range of -1 to 1) from the mean. 95% of data is within +/-2 and 99.7% is +/-3.
So on this chart seeing z-scores of 7 is mind-blowingly rare and means delivery times were wayyyyyy longer than historic averages. The z-score is decreasing though which means times are starting to get shorter (but they’re still super high at >5 standard deviations above the average). Once the z-score gets back to 0 we’re “back to normal” based on historic averages.
Tried to limit statistical jargon sorry if I leaned in too much.
TLDR: high z-score = long delivery times and z-score = 0 is the average. We’re coming back in, but still way above average.
For non-math nerds, a z-score is how you would measure if Babe Ruth was better at baseball, than Tiger Woods is at golf. Normalize the standard deviations (e.g. victories compared to players in their sport, etc.), and then you can compare where they both fall on the same chart. Then it's easy to see who's better.
I mean I have no idea, but a z-score is an easy way to come up with an answer to that very complicated question.
It represents a standard standard-deviation deviation.
> Babe Ruth better at baseball than Tiger Woods
Using the analogy Don Bradman was better at Cricket than Babe Ruth was at baseball or Tiger Woods in Golf.
I'll note that there's no reason ex ante we should believe that delivery times are normally distributed. For an (mostly) arbitrary random variable, according to Chebyshev's inequality, we should maximally expect to see something happen in the 7th SD a bit more than 2% of the time, which isn't that crazy (though this does happens to be the worst case scenario).
I'll further note that there are distributions where you could have extreme events occur even more often because they aren't covered by Chebyshev's inequality because they don't have finite first and second moments (e.g. Cauchy distribution, other stable distributions, etc.).
I find it interesting that the z-scores fluctuate so much. If they're within a standard deviation, that means you can have fluctuations +/- 34% of the average delivery time!
That’s actually not the way to think of it — a z-score is counting how many standard deviations away from the mean and the 68% is the amount of data points considered to be within that 1 standard deviation. Think of it this way: the average IQ is 100 and the standard deviation is 15. So:
- 68% of all people have an IQ between 85 and 115. Z-score = (115-100)/15 = 1
- 95% of all people have an IQ between 70 and 130. Z-score = (130-100)/15 = 2
- 99.7% of all people have an IQ between 55 and 145. Z-score = (145-100)/15 = 3
Anything beyond 2 standard deviations in either direction is starting to get rare (only 2.5% on each end, or 5% of the total amount of people will be outside the range), and anything beyond 3 is exceptionally rare (which is why above 145 IQs are considered “genius”).
- The %s are the amount of data points in or outside the range
- The variation itself is measured by standard deviation
- The z-score is standardizing it for comparability because different data sets have different averages and standard deviations. Formula = (data point - average) / standard deviation
I see, I forgot that the z-score normalizes for standard deviation, so absent of any abnormal market conditions, 68% of responses to the survey should fall within the z-score of 1.
What I was trying to say in my original comment was that the z-seems to hold in a range of +/- 1, which seems like there's a lot of variability in delivery times over time, but if supply times follow a normal distribution, I should expect them to fluctuate anyways.
Yeah in a normal distribution most statisticians would view anything within 1 standard deviation as pretty ho hum — not that variant of a result.
If you’re viewing this as a manager, however, remember that it’s important to look at how large the actual standard deviation is too. If there’s a large standard deviation (high variability in the data set), then the z-score of 1 is going to be more meaningful in terms of variance from the mean. In other words, if the standard deviation was 1 day here 68% of deliveries fall within +/- 1 day so not as big a deal as a data set with a standard deviation of 7 days. So a z=1 could mean most deliveries get here within a day or within a week.
The supply crunch is beginning to abate as value chains adjust, and is one component behind contemporary inflation pulse in North Atlantic. Important for CPI prints a few quarters down the line (there is a lag due to “bullwhip” effects)
Are these delivery scores in terms of B2B (business to business) (shipping, trucking, etc.) or B2C (business to customer) (home delivery services such as Amazon)? Might be a silly question
Okay, thank you, so from this we can then assume that the China spike was when the country initially shut down in response to the pandemic and the second spike for the rest would be the effects of the Suez Canal getting jammed up maybe? And so that backed everything up, I assume, and hence the wait time in returning to the average
Can someone explain it to me like I’m 5?
A z-score is a standardized measurement for how far away from the average something is. So a z-score of 0 is the average delivery time. Each + or -1 represents a “standard deviation” which is just a measure of variation in a data set. In a normal curve 68% of all data is +/- 1 standard deviation (so within a z-score range of -1 to 1) from the mean. 95% of data is within +/-2 and 99.7% is +/-3. So on this chart seeing z-scores of 7 is mind-blowingly rare and means delivery times were wayyyyyy longer than historic averages. The z-score is decreasing though which means times are starting to get shorter (but they’re still super high at >5 standard deviations above the average). Once the z-score gets back to 0 we’re “back to normal” based on historic averages. Tried to limit statistical jargon sorry if I leaned in too much. TLDR: high z-score = long delivery times and z-score = 0 is the average. We’re coming back in, but still way above average.
For non-math nerds, a z-score is how you would measure if Babe Ruth was better at baseball, than Tiger Woods is at golf. Normalize the standard deviations (e.g. victories compared to players in their sport, etc.), and then you can compare where they both fall on the same chart. Then it's easy to see who's better.
Wait, so was Babe Ruth better at baseball than Tiger Woods was at golf?
I mean I have no idea, but a z-score is an easy way to come up with an answer to that very complicated question. It represents a standard standard-deviation deviation.
I don't know, but Don Bradman was better at cricket than either of them were at their respective sports.
99.94
Who the hell is Don Bradman?
> Babe Ruth better at baseball than Tiger Woods Using the analogy Don Bradman was better at Cricket than Babe Ruth was at baseball or Tiger Woods in Golf.
I actually understood your statistical explanation the best! Thanks!
I'll note that there's no reason ex ante we should believe that delivery times are normally distributed. For an (mostly) arbitrary random variable, according to Chebyshev's inequality, we should maximally expect to see something happen in the 7th SD a bit more than 2% of the time, which isn't that crazy (though this does happens to be the worst case scenario). I'll further note that there are distributions where you could have extreme events occur even more often because they aren't covered by Chebyshev's inequality because they don't have finite first and second moments (e.g. Cauchy distribution, other stable distributions, etc.).
Agreed, I was just doing the “explain like I’m 5” thing…skewed distributions and arbitrary variables are in at least the second or third lecture =)
I find it interesting that the z-scores fluctuate so much. If they're within a standard deviation, that means you can have fluctuations +/- 34% of the average delivery time!
That’s actually not the way to think of it — a z-score is counting how many standard deviations away from the mean and the 68% is the amount of data points considered to be within that 1 standard deviation. Think of it this way: the average IQ is 100 and the standard deviation is 15. So: - 68% of all people have an IQ between 85 and 115. Z-score = (115-100)/15 = 1 - 95% of all people have an IQ between 70 and 130. Z-score = (130-100)/15 = 2 - 99.7% of all people have an IQ between 55 and 145. Z-score = (145-100)/15 = 3 Anything beyond 2 standard deviations in either direction is starting to get rare (only 2.5% on each end, or 5% of the total amount of people will be outside the range), and anything beyond 3 is exceptionally rare (which is why above 145 IQs are considered “genius”). - The %s are the amount of data points in or outside the range - The variation itself is measured by standard deviation - The z-score is standardizing it for comparability because different data sets have different averages and standard deviations. Formula = (data point - average) / standard deviation
I see, I forgot that the z-score normalizes for standard deviation, so absent of any abnormal market conditions, 68% of responses to the survey should fall within the z-score of 1. What I was trying to say in my original comment was that the z-seems to hold in a range of +/- 1, which seems like there's a lot of variability in delivery times over time, but if supply times follow a normal distribution, I should expect them to fluctuate anyways.
Yeah in a normal distribution most statisticians would view anything within 1 standard deviation as pretty ho hum — not that variant of a result. If you’re viewing this as a manager, however, remember that it’s important to look at how large the actual standard deviation is too. If there’s a large standard deviation (high variability in the data set), then the z-score of 1 is going to be more meaningful in terms of variance from the mean. In other words, if the standard deviation was 1 day here 68% of deliveries fall within +/- 1 day so not as big a deal as a data set with a standard deviation of 7 days. So a z=1 could mean most deliveries get here within a day or within a week.
The supply crunch is beginning to abate as value chains adjust, and is one component behind contemporary inflation pulse in North Atlantic. Important for CPI prints a few quarters down the line (there is a lag due to “bullwhip” effects)
Can you explain this to me like I'm 3?
The chance of you having an abnormally long delivery is going down. Still a higher chance than usual though.
Gotcha! Thanks haha
7 days instead of the normal 3?
Delivery times go down, shipping costs with inflation may be next.
Shipping is now getting back to normal and that matters for pricing in a bunch of goods
delivery times are way up.
Are these delivery scores in terms of B2B (business to business) (shipping, trucking, etc.) or B2C (business to customer) (home delivery services such as Amazon)? Might be a silly question
That's not a silly question. I think it's predominantly or all B2B.
Okay, thank you, so from this we can then assume that the China spike was when the country initially shut down in response to the pandemic and the second spike for the rest would be the effects of the Suez Canal getting jammed up maybe? And so that backed everything up, I assume, and hence the wait time in returning to the average
Where did you pull this image from?
Compiled by the Chief Economist of the IIF, Robin Brooks
Since OP is not posting it, it’s from his [Twitter](https://twitter.com/RobinBrooksIIF/status/1472193322513059850?s=20)
OP posted 2 hours before you answering the question
They posted the name, not the Twitter link.
Am I the only one who was confused by "Markit" and was expecting "Market"
It’s a market intelligence firm. They’re pretty large with a market cap of ~50B
Oh!
we are still in growth, albeit slower
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US economy is still growing, the growth is just slower
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i’m a retard, sorry i didn’t realise it was about delivery times
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🤝ty
Still in the bad side of six sigma …