@lentesta First as you have said obviously something changed to make your modeling (which is wonderful work and a great job that you have done) off this January and February. Now getting down to cause obviously gets tricky as there are a number of inputs.
So you have shown that capacity was throttled down before 2/21 as compared to after that date of this year. I think the point
@mikejs78 has made is the big point and really the determining point on how this throttling of capacity is interpreted. Without historical context, sure it looks like Disney has throttled down a bunch and of course this could explain the differences. However, I would humbly say though that this data (a one year look pre and post 2/21) is shaky when trying to figure out why your projections were off as it has no comparison to previous years, and I think you would admit as much when you say the numbers should be viewed with skepticism. Perhaps Disney has always decreased capacity during these times by similar percentages or maybe they haven't. That is the missing piece of data, and I am not sure if you have capacity (actual exit counts) for the same time periods in years past or not. That is huge and has huge effects, especially depending on the magnitude of any decrease in capacity from prior years.
On the flip side another input is attendance and that is something that we don't know and is much harder to measure. As you have mentioned the economy is better, delayed trips due to hurricanes, and also this is the third year of tiered pricing (??? or around there if I remember correctly). So behavior is ripe to start changing. And here is the other only issue I see in the analysis, that wait times will increase linearly with attendance. I think the issue here is complete saturation of the capacity for the day.
@MisterPenguin has laid this out very well above.
I will attempt to bolster this in another way with the area of study I know which are drug metabolism, kinetics, the Michaelis-Menten equation, and saturation kinetics. This is an area where loads of modeling and equations exist and great science. Actually, it is great parallel to theme park ride capacity and attendance modeling. I will not go into all the math and equations etc here to save everyone from that, but it is all available if anyone wants it and wants to hear more. Most basically, for this I will say drug molecules are similar to people visiting a theme park and that rides/attractions are the enzyme that eats them up so to speak for the metaphor. So a ride/attraction has a certain throughput that it can do, of course which is dependent on staffing and the amount of vehicles used, which is the same as the velocity of an enzymatic reaction, and even in nature it is not constant. And as people queue up and wait for a ride (and then, even though not a direct relation as we know, ride wait time goes up) this is equivalent to a drug concentration or number of people waiting for a ride. So if a theoretical ride capacity is 1,000 people per hour or an enzyme can take care of 1,000 drug molecules an hour, the actual rate of people going through the ride will increase until the capacity is reached or in kinetics the enzyme is saturated. Then the actual rate of people going through the ride for a certain time period will not change, but you continue to have more and more line up.
If we look at one ride and assume the ride on this day has a 1,000 pph capacity we can see what happens if the the number of people entering the attraction per hour is higher (assumed with higher attendance) and then goes over the capacity. The actual rate of people going through the attraction increases until capacity is hit. Then as you have said yes it is true that the additional number of people in line has a linear relation to the number of people entering per hour, but there is an exponential relationship between the total number people entering the attraction over a certain time period and the number still in line after that time period. Of course this simplifies things and takes out things like human behavior when a ride line gets so long and avoiding it etc.
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So now some pictures for those who are visual. I have just changed the graph axis labels to illustrate the point and the originals and a discussion of the said graphs can be found:
http://www.derangedphysiology.com/m...ero-order-and-non-linear-elimination-kinetics
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So as can be seen when considering saturation if attendance increases from A to B, and we are not near complete saturation or near complete park capacity at a macro level or at a single attraction at a more micro level there is almost an equal rise from a to b in people in lines or wait times. However if expected attendance C is closer to the saturation point and is further increased to D there is a much larger rise from c to d in people in line and wait times. As saturation or full capacity is reached there will be greater increases in people in line and wait times then expected. Works in nature and works here as well.
To your point Disney can control how that curve looks to some extent with staffing and capacity. I will look at capacity in a macro whole park sense here. Obviously it is far more complex and would be a fun problem to model, but this is most simplistic for explanation.
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So three things could have happened here:
On the one hand Disney could have moved capacity from the high capacity curve to the medium capacity curve (the middle curve) or even from medium to low. For explanation and illustration lets say from the high capacity curve to the medium capacity curve. They could have done this and you and your team model you are expecting attendance level A with the high capacity curve and thus wait times and people in line of a1, however as the capacity has changed then you end up in a big difference from wait times and people in line that is a2. This is all just Disney trying to improve margins and save money and decrease capacity and staffing.
Option 2 is that if Disney has always been riding the slow season with the low capacity curve and has been close to the saturation point for that slow season. However, if there was even a small percentage attendance bump from C1 to C2 which could be related to the economy, buying behavior with tiered pricing, the hurricanes, etc then as Disney was always playing close to maximum during this season this modest attendance bump (if unexpected and capacity not increased) created a c1 to c2 increase in people in line and wait times, which is not linear to the attendance increase.
And then the most likely senario would be a bit of both. Disney has had a few years of analytics and data and are feeling confident and looking at margins and have a directive to cut operating and capital expenditures. During the slow season they always believed they had extra margin for attendance level A and they believe that they can move say from the high capacity curve above to the medium capacity curve and have a marginal effect (to them) on wait times and people in line (a1 to a2). Now they have a steeper curve which is much closer to saturation and if that is coupled with a more then expected increase in attendance from A to B and we end up with a b2 level of wait times and people in line. Now if you (Len and team) are modeling this and are expecting the high capacity curve then the attendance bump would have been an a1 to b1 bump which is probably within your error and isn't a big deal. Also if this kind of attendance variability had existed for years Disney had been able to absorb it at the high capacity curve. But now you are seeing with the combination of the increase in attendance and the decrease of capacity to close to the saturation of capacity levels that there is a big swing from a1 to b2 which is way off. Disney is also surprised as they are expecting a more modest a2 level of people in line and wait times but are seeing a b2 kind of level. However, this is after Disney has let go of a bunch of seasonal employees, have lower college program numbers, etc. and turning that ship is hard. So perfect storm. This seems the most likely and while Disney is playing too close to having saturated capacity and are having the good news of better then expected business, it isn't just some nefarious lets make the park seem busy so we can charge more tin hat theory. Also means Disney will see a drop in satisfaction scores and attractions per guest scores and will hopefully adjust for next year.
The big problem is all of the above scenarios depending on the actual curve shapes can all look very much the same in the outcome of increase in wait times and people in line and can be very hard to parse out. It all comes back to if we had the actual capacity and exit counts historically and could trend that. Not that you need any advice from a random internet guy who is bored on a Sunday, but it seems like that would be great data to have especially in the historically slower seasons to have best predictions and real time updating of your modeling.