Can the manufacturing supply chain learn resiliency from food logistics?

23 November 2021

Can the manufacturing supply chain learn resiliency from food logistics? (Spoiler alert: Yes, and no.)

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The reality of the COVID-19 pandemic hit the U.S. early in the spring of 2020, prompting a wave of panic buying. For a few weeks, grocery shoppers might find empty coolers or bare shelves where they were used to seeing arrays of yogurts, or pastas, or whatever.

The thing is, our supermarkets were restocked nearly as quickly as they’d been emptied. The food supply chain proved far more resilient than the manufacturing supply chain—which is still disrupted with no real end in sight. America’s recent obsession with supply chains encouraged Zetta to reconnect with Elliott Wolf, who was the subject of our first Zetta Bytes Live talk back in July, 2020. We asked for his recent observations in light of that 2020 conversation.

Wolf has a lot of insight into how our food supply overcame those early disruptions. He also has a BS in Math from Duke and an MS in Statistics from Stanford, but his insight into food logistics was acquired on the ground, as Vice President and Chief Data Scientist at Lineage Logistics since 2013.

Lineage is a privately held company that happens to be the largest temperature-controlled warehouse owner and operator in the world. Every year, about over 90 billion pounds of food transit Lineage’s 375+ warehouses. Lineage touches over one-third of all the refrigerated or frozen food in the U.S.

As impressive as those numbers are, a business of warehouses and forklifts and tractor-trailers coming and going might not seem like a natural hotbed of AI and data science. Yet, as Wolf pointed out: “It’s got all of the logistics complexities of an Amazon, plus you have to keep everything cold, which is an exercise in thermophysics. It’s a super science-heavy business.” Wolf was the first data scientist at Lineage Logistics, which was the first company in its industry to start up such a team. These days, Lineage’s Data Science Team has grown to about two dozen people; has an org chart packed with Stanford, Berkeley, and Harvard grads; and Lineage was #23 overall and #1 in Data Science on Fast Company’s 2019 “Most Innovative Companies” list.

Most people see trucks and warehouses. A few see gradient descents, graph theory problems, Monte Carlo experiments, and—oh, yes—bin-packing problems galore

Most logistics companies are just getting up to speed in data science and AI. Lineage’s Data Science Team was a first in the industry and it’s the largest.

In our experience at Zetta, the problems that established companies have when it comes to adopting AI are not technology problems, per se; it’s not lack of expertise or resources, either. Rather it’s building the cultural bent towards innovation. Lineage Logistics had that leaning early on, even though the company didn’t quite know what to expect; eight years ago, when the leadership recruited Wolf they said, “We hear there’s math in logistics. Will you come take a look?” (We presume that many such conversations are happening right now throughout the manufacturing supply chain.)

One of the first projects for Lineage’s nascent Data Science Team was, as computer science majors would call it, a bin-packing exercise. The goal was to cram as many different-sized pallets into a given number of racks as possible.

“What can math do for you?” he recalled. “It’ll build warehouse space.” What he meant was, we’ll create new warehouse space with math instead of construction equipment. Once built, a refrigerated warehouse is very hard to modify. But the steel racks that hold pallets of foods are relatively easy to tweak. Think of a pallet of Sabra Hummus or a 55-gallon drum of Chick-fil-A honey barbecue sauce as paying tenants, whereas empty air above them or beside them is a vacancy.

“If you can just change the pegs on your Ikea bookshelf according to what your mathematician said and achieve the same effect [as expanding a warehouse] that’s a pretty fundamental economic driver in the industry considering [that the only other way to accomplish that is] to buy land, pay $300 a square foot on refrigerator construction, and then spend millions of dollars a year keeping it at zero Fahrenheit.”

“That’s probably a good illustration of how we operate,” he told us. “The objective is crystal clear and no one can disagree with it, even if the actual mechanics of it are opaque to someone who hasn’t gone to graduate school in math or statistics.”

The job of Lineage Logistics, which is privately held, is to deploy capital to turn it into more capital; not unlike a venture fund or any other private equity fund. Take warehouse rack design: We throw a quarter of a million dollars at rejiggering the rack heights and it makes us $10 million worth of warehouse. So on a valuation basis, we’ve turned $250,000 into $10 million, which is a trade that anyone at any fund would make all day long.

When Lineage CEO Greg Lehmkuhl spoke to REIT Magazine last year, here’s how he defined the company’s strategy. “We buy these properties and optimize them by increasing occupancy and density,” Lehmkuhl said. “We go in and attack every aspect of revenue and cost to get more out of them.”

One of several ways Lineage increases yield is via Data Science. Once the team had proven its value-add, other math problems presented themselves. Optimizing shipments between hundreds of warehouses and thousands of local and regional distribution centers was classic graph theory; modeling the performance of new blast freezers called for computational fluid dynamics.

“You could call [it] AI, you could call [it] data science, it’s actually applied physics but whatever—it’s a giant high dimensional gradient descent,” he said of the freezer model. “If you know the mathematical methods to do AI/ML, then you also know how to do stuff like this.”

Another thermophysics project involved load balancing power demand. There’s tremendous thermal mass in a 200,000 square-foot warehouse filled with 50,000,000 pounds of frozen food. So Lineage schedules power consumption to avoid peak rates. “It’s as if you put a behind-the-meter battery system on your cold storage warehouse. Now you’re actually intentionally thermally cycling the food supply,” Wolf told us. “We added two zeros on the sensor counts so that we knew exactly what was going on, not because we were concerned about losing control in a particular area or that we were going to thaw something accidentally, but because if you’re going to do that, you need to have a precise idea of whether what you think is going to happen is actually going to happen. How close are your models to your sensor data?”

Lineage Logistics was forced to learn fast when Covid hit

“The food supply chain has existed for millennia,” he reminded us. “The entire history of humanity has been geared towards figuring out how to feed ourselves. And the modern incarnation of that is a whole bunch of pooled infrastructure whereby almost no one who manufacturers food is responsible for its handling and distribution all the way down to the consumer.”

In the spring of 2020, a combination of panic buying and disruptions at packing plants and warehouses resulted in some bare shelves at supermarkets. Lineage quickly pivoted to a different distribution strategy during the brief period when the food logistics system was disrupted.

“We intentionally simplified the orders,” Wolf explained—describing a quick reaction by thousands of people spread across hundreds of warehouses and tens of support teams. “The objective of the supply chain under normal circumstances is to guarantee the availability of a cornucopia of yogurts. You can stand in the grocery store and gawk, thinking ‘I want gluten-free boysenberry,’ or whatever. Lineage decided, ain’t nobody got time for that ** and, in conjunction with our customers, we said, ‘OK, we used to ship five items; now we’re shipping two. You used to want half a pallet; now you’re getting a full pallet.’ It worked out well because they had the demand to use that up downstream. But the critical thing is that when you have a crisis like COVID, the work that you do before it matters more than the work that you do during it.”

One example of work done before the pandemic was a model Lineage built to predict turn rates on pallets entering the warehouse. By putting pallets expected to leave the warehouse quickly in the most accessible spots, Lineage effectively increased labor productivity. It also monitored operational performance to determine how to triage facilities as different areas of the country saw surges in COVID and/or panic-buying at different times.

But labor shortages continue to plague supply chains, even as the direct impact of COVID appears to be winding down.

The new normal for the supply chain is one that’s less dependent on humans. So that’s a robotics thesis; I want to build this robotic warehouse. But what does it need to do? That’s a statistical question. Okay. I have this candidate layout. Is it capable of doing that?

It’s an extra exercise in scientific computing because we don’t know what the future supply chain is going to look like. We get to build this building once and modifying it is going to be prohibitively expensive. So now you’re going into this stochastic exercise of, what will the future look like and how future-proof am I? That involves doing a whole bunch of Monte Carlo simulations, to break your warehouse before you build it.

The U.S. food supply chain has recovered its equilibrium, but the manufacturing supply chain hasn’t. In a recent email, Wolf pointed out some of the differences.

  • The US is the largest agricultural producer and the largest agricultural exporter in the world. Common products that do come in from abroad (e.g. cheese) have domestic substitutes. Very little is manufactured wholly within the US with zero foreign dependencies, but agricultural commodities are. The plurality of the food that the US does import comes from Mexico, and thus does not need to transit a container port.
  • The feedstocks to any food product are necessarily simpler than something like a car. There are fewer individual components and fewer different suppliers of those components. If one has 10 necessary components vs. 10,000, the system with 10 will prove more resilient.
  • Manufacturing operations get to choose when they produce. They often make the choice to minimize inventory or working capital, maximize plant utilization, or otherwise achieve some financial objective. We in agriculture don’t get to choose. We produce at the times and in the amounts that Mother Nature allows.
  • Since Mother Nature doesn’t care about working capital, we have to stockpile for times when we don’t produce, leading to much higher inventories. Even if we wanted to produce and ship just-in-time, we can’t. We instead do what humans have done since the dawn of time — stockpile for the (literal or proverbial) winter.

The most international part of the U.S. food supply chain is seafood. Unlike the rest of the food we eat, most of our seafood is harvested outside U.S. waters, and even fish like salmon that are farmed or harvested here are often sent to offshore plants for processing. Harvests are notoriously unpredictable; the supply chain pivots to alternative suppliers in dizzying fashion, but few consumers even notice.

Shrimp is a very heavily imported commodity into the United States. The Gulf is the only provider of shrimp in the U.S. and gulf shrimp is primarily distributed regionally. Most of the shrimp that we consume comes from Asia; historically Thailand was our largest supplier.

But the rankings have been wildly gyrating for several years. Thailand’s shrimp were hit with an animal pandemic; something called yellow head virus—it’s a distant cousin of the coronavirus. Suddenly their exports to the U.S. are down 80%. Meanwhile Ecuador has jumped from number seven to number two.

We’ve had huge global warming-induced squid shortages. Global warming has had catastrophic effects on certain fisheries; we see it in our data. So in 2013 you had 118,000 tons of calamari come in to California and in 2019 we had less than 10% of that.

These are things that happen when you are dependent on mother nature for all of your production; El Nino is going to take out a bunch of fruit, or some fishery is going to fail, or some pork virus is going to take out 60% of the Chinese pork population. There’s a working assumption in our industry that you’re just going to get kicked in the head.

Advice for startups seeking their first clients

Lineage Logistics is one of the companies that’s willing to engage startups. Since Zetta works with founders, we were curious about Wolf’s insights vis-à-vis engaging startups. He told us that the ones that get contracts are the ones who have taken the time to get to know Lineage’s business.

People use the word ‘empathy’. A lot of startups who make sales pitches at us are not terribly empathetic… To them [their startup] is the center of the universe. And so they’re interested in this one particular application, and good for them.

The startup’s application is hyper-focused. It could be computer vision for X; it could be this particular type of inventory management; a freight execution platform, or whatever.

But imagine in the context of COVID and our problem set in general; we’re fighting fires in all different directions. It’s usually not that successful to come at us directly like, ‘Here’s my product and you need to buy it because I think it’s important to your business.’

That may be true but Lineage has problems with six zeros on them; we have problems with seven zeros on them; problems with eight zeros on them; sometimes we’ve got problems with nine zeros on them. So, which one is it?

We might have a two-year-long sales cycle where they come to understand our business and we understand their capabilities. And then suddenly we’ve got a nine-digit problem and we can make their day.

So what about that troubled manufacturing supply chain? Can it learn from food logistics, or not?

In spite of the many differences between the two supply chains, it’s still natural to wonder whether the manufactured goods chain could learn some useful lessons in resilience from food logistics. In a recent email exchange, we asked Wolf for any last thoughts on the ongoing challenges facing the manufacturing supply chain. He referred us to an online talk that he and Dr. Danel Wintz, a Principal Data Scientist at Lineage, had participated in. The talk was sponsored by Duke University’s John Hope Franklin Center; it took place just 6 weeks after that brief period of disruption in the food supply chain. In that conversation, Wolf pointed out that business has embraced the notion of just-in-time inventories to reduce the amount of capital tied up in inventory and improve cash flow.

“That’s what your activist hedge fund wants you to do,” Wolf said. “They want you to free up cash in inventory, so you can return it to shareholders.”

“The statistics of rare events is difficult,” Dr. Wintz interjected. “It’s clear that resiliency is a way to deal with these rare events, and a lot of companies haven’t done that expected value calculation for their supply chain.” By ‘resiliency’, Dr. Wintz meant some of the things that are baked into food logistics: Keeping critical feedstock production onshore; keeping enough inventory on hand to weather a disruption.

Wolf picked up that thread. “I’m proud of the fact that of all the supply chains implicated in this, the only one that really didn’t fall down was perishable food. I wish I could claim some kind of moral high ground in that, but in reality we’ve been trained; the system has trained us not to rely on agricultural production; history has trained us. The history of humanity is, in large measure, the history of agricultural disruptions.”

“And so the [food supply] system regularly deals with things that the rest of the economy is shocked to learn are a factor,” he concluded. “That’s gotta change.”