The Hobbled Bubble Tea Supply Chain

Two things you didn't expect the world to run out of are Boba tea and Ketchup.

The reasons for the stockouts are unrelated. The Ketchup shortage is driven by the shift from dining in restaurants to relying on food delivery and take-outs.  When dining in a restaurant, one can use big bottles of Ketchup and unused packets are left for the next customer. When delivering food, restaurants tend to send more condiments than needed, and customers never return these or have a way of signal that they don’t need them.  Over time, the demand outpaces the supply, which I am sure was stable for years. 

The reason for the Boba tea shortage is quite different:

“It happened when beverage aficionados learned that tapioca, the starch used to make the sweet, round, chewy black bubbles — or pearls — that are the featured topping in the popular boba tea drink, was in short supply.”

It’s an excellent opportunity to explore why we see product shortages of what seems to be a random set of products.

Is it Just-in-Time?

If you follow the NY Times, you get the impression that we should blame the concept of Just-In-Time (JIT) operations for everything. Let's try to see whether this is the case.  JIT originated in Japan with the Toyota Production System. The idea behind JIT is to reduce “waste” in the system by synchronizing all flows. In particular, items only move through the system when downstream nodes need them.  JIT does not mean carrying zero inventory, but it means carrying less inventory, so if customers change their preferences, the system is much more responsive, given the limited stock it carries. 

The notion of JIT has become widely popular across almost every sector as firms started copying Toyota. 

When looking at the data from the Federal Reserve, we can see that indeed the metric of  “inventory to sales ratio” has been declining quite steadily since the 90s where the method gained popularity. Why am I not looking at raw data of inventory? Since if inventory grew, albeit, at a slower pace than sales, the implication is that inventory spends less time on the shelf (where it loses money). And indeed this is what we see until 2012. 

But then we see a reversal to this trend around 2012, and relative inventory levels begin to build up. 

This post does not aim to explain this reversal, but just to say that if you blame JIT for the shortages we see, you are probably making the wrong argument. You are making a 2012 argument. 

So what can be the reason?

“It’s all being held up at the docks,” said Arianna Hansen, a sales representative for Fanale Drinks, which is based in Hayward, Calif., and supplies boba to thousands of stores around the country. Ms. Hansen said that shipments had been backed up for several months and that the company’s existing stockpile of tapioca was running dangerously low.”

Shipments of Tapioka, usually brought from Taiwan are delayed at the ports. Let's try to delve deeper and understand the root cause of these delays and their impact.

First: if we take the same data from the Federal Reserve and zoom in on the time from 2016, we see a reasonably constant inventory to sales ratio, all the way until January 2020. Supply chains like predictability. 

Then at the beginning of the COVID pandemic, a significant shock to the ratio. What drives it? Store closure and uncertainty on economic outcomes.  As people stopped buying and stores closed temporarily due to lockdown, items spent more time on the shelves.  

The ratio is then starkly reversed around July. A combination of two different trends: on the one hand, people started buying more: spending more time in front of their laptops while getting checks from the government. On the supply side, issues began to emerge as many countries were still under lockdowns.  The inventory to sales ratio went down by 30%, demonstrating a significant reduction in relative inventory levels. 

In and of itself, the fact that relative inventory levels went down is not an issue. But it does limit the flexibility of retailers to absorb additional shocks. 

And the additional shock came in the shape of delay at the ports.  

Congestions at the Ports

The fact that ports are more congested is clear.  But to understand why we see such a significant impact, we have to dig deeper. 

“Marine terminals in Los Angeles–Long Beach appear to be operating at 80 to 85 percent utilization, “which means on peak days they are operating at 105 percent of capacity,” said Dan Smith, principal at the Tioga Group. “It’s killing everybody up and down the supply chain.” 

If you remember your time in OPS class, queues behave in a very non-linear way as a function of their utilization. Without getting into the math, I will say that waiting time grows as a factor of the metric “utilization / (1- utilization)”. This means that if you are around 80% utilization. A 10% shock to either demand or supply will increase your waiting time by a factor of 2.25X. More than double your waiting time.  

And that’s indeed what we see: 

“Under normal conditions, container ships rarely anchor,” said Kip Louttit, executive director of the Marine Exchange of Southern California. On Feb. 1 at noon, 40 were anchored offshore.”

If you don’t have enough buffer inventory and the ports do not have sufficient buffer capacity, you will not have products: Boba tea, or Lumber, or Kettlebells.  Buffer or suffer.  

Different products have different reasons for their shortage. But they all hint that supply chains are increasingly complex and rely on many decisions makers along the chain.  If you care about your product availability, there are no shortcuts but mapping the network, identifying points of failure, and hedging or mitigating these.