I know it’s got something to do with demand and supply chain, but can’t seem to get it.
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It is how small changes in demand signals at one end of the supply chain (say at a phone assembly factory) becomes amplified as the signal percolates through the supply chain (through chips etc etc). For example: demand change of 5% at the top end results 50-60% demand changes lower in the supply chain. The wrong idea of the supply chain is to think of it as an inflexible rope where pulling on one end results in the same amount of pull at the other end. Rather this figurative “rope” is better seen as an elastic band where changes at one end propagate as waves of increasing magnitude. The reasons for this are many, ranging from economic lot sizing, order lead time, buffer inventories, transport time, ordering policies and lag time. (Presumably you have some background in Operations Research, Supply Chain, Manufacturing Planning Systems etc or much of this answer might not be informative). In signal or control theory, this effect is reminiscent of resonance and underdamping.
[https://en.wikipedia.org/wiki/Bullwhip_effect#:~:text=The%20bullwhip%20effect%20is%20a,further%20up%20the%20supply%20chain](https://en.wikipedia.org/wiki/Bullwhip_effect#:~:text=The%20bullwhip%20effect%20is%20a,further%20up%20the%20supply%20chain).
It’s the supply chain version of a basic theory in control law that delays make systems more unstable. In short, it means that the farther up the supply chain you are (the farther away from the end user), the wilder swings in orders you see. This happens because each intermediate step in the chain tends to aggregate orders and work on a longer lead time, so their buffers and fear of running out of stock get larger.
Imagine a three-stage supply chain for beer: brewery, distributor, retailer. Lets assume the retailer sells cases, and orders from the distributor every day and the distributor delivers it the following day, and the distributor does the same with the brewery only they order pallets of cases.
A major sporting event is coming up on Sunday, the retailer places a larger than normal order on Thursday to stock up for Friday because they think people will start prepping for the “big game” (pick whatever event you like). All other retailers do the same, for the same reason. The distributor gets hit by several large orders at once…they fill what they can, backorder the rest from the brewery, *and order more so that this doesn’t happen again on Friday*. But there’s more than one distributor too, and they’re all doing the same thing, so the brewery gets slammed with a *giant* pile of orders, which they can’t fill either, so they fill as much as they can, backorder the rest, and *start brewing even more beer* so that this doesn’t happen to them again on Saturday. When the dust all settles on Monday, the retailer is back to their normal stock but pissed off some customers because they ran out of beer over the weekend, the distributor has a huge overstock because the Monday orders are back to normal, and the brewery is sitting on a giant pile of beer with no customers.
This is an overly simplified example, obviously real supply chains try to forecast and adapt, but the basic system dynamics are remarkably robust. If you use orders as your demand proxy and use that to make production decisions, you’re always “behind the curve”. MIT runs an academic game (“The Beer Game”) to simulate this and has been doing it for decades, they have a really robust dataset and the ability for people to screw this up even in a completely transparent setting where everyone knows everything is pretty amazing.
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