As industry leaders rebuild their supply chains it’s time to redesign them as well.
By Max Wessel, SAP Chief Learning Officer
Modern supply chains were built like cathedrals: massive logistical marvels. But then in February, the walls came tumbling down. Now that industry leaders are scrambling to rebuild their supply chains, it’s time to redesign them as well. The supply chains of the future need to be agile, distributed, and stress-tested. Although it’s tempting to optimize logistics for the best possible outcome, in the wake of COVID-19, it has become evident that leaders have to plan for a range of worst-case scenarios.
For a sense of what’s possible, consider the shortage of ventilators. Ventilators are deceptively complex devices—a recent model has more than 700 individual parts.[i] As one columnist observed, “The constituents of a ventilator come from dozens of countries and through as many as nine layers of suppliers. At the same time, some specialty components… are made by only a few precision manufacturers—meaning that any attempt to affect a rapid expansion in global production is constrained by the ability of these companies to ramp up.”[ii] Constrained for now, simulation makes it possible to plan for scenarios in which any one of those 700 parts is suddenly unavailable, or if demand surges. It is a failure of foresight—and responsibility—to rebuild these networks without seizing the opportunity to make them more agile, more diversified, and more resilient than before.
By the same turn, companies across sectors now have an opportunity to make their supply chains more agile and more modular. The incentives are clear: in good times, a more agile supply chain will offer a faster track from design to market for new products. And when the next crisis comes, it will mean a swifter rebound, too.
Increasing agility begins with harvesting operational data, and then deriving insights that supply chain managers can act on. For instance, amidst the confusion when the demand for essential items surged earlier this year, we helped suppliers, shippers, and buyers connect on a single, transparent network. Even as many of the most robust supply chains were breaking down, our network helped nurses procure personal protective equipment in 24 hours. When a temporary hospital in New York City needed 500 beds, the same network connected them with a source in 30 minutes.
Getting actionable information in a fast-moving crisis like COVID-19 is one thing, but supply chain managers will also need early detection systems to help them brace for slower, longer-term variables like drought,[iii] flood patterns, labor strikes, and geopolitical volatility. Achieving that level of awareness—let alone operationalizing it—would overwhelm even the most experienced supply chain teams, but advanced computing makes it possible.
Unfortunately, some of the qualities that make our supply chains efficient also make them fragile. By eliminating bottlenecks and maximizing profit margins, many companies have inadvertently integrated their supply chains so tightly that one shock to the system—say, a shortage of skilled workers, or a surge in demand—sets off a cascade of failures.
One of the most common missteps is overreliance on one supplier. Now that companies around the world are suffering the consequences of concentrating too much of their supply chain abroad, some political leaders are agitating to shift entirely to local suppliers. But however one feels about globalization, attempting to reverse or overregulate it isn’t just reactive—it’s unrealistic.
A smarter, more sustainable approach is to use this opportunity to do just the opposite: build far more distributed supply networks. When a sailboat hits choppy water, crewmembers spread out to help stabilize the boat through the waves. Likewise, in a highly uncertain supply chain environment, the geographic distribution of raw materials and labor can make the difference between slowing production and stopping it altogether.
Simulate your worst-case scenarios.
Simulation can help quantify the risks and rewards of infinite geographic distributions, giving today’s executives a level of operational awareness that simply wasn’t available to their predecessors. A textbook cautionary tale describes how, two decades ago, the telecommunications giant Ericsson streamlined their mobile phone operation by switching to a single-source supplier for some of their core components. Shortly thereafter, a small fire at a semiconductor plant in New Mexico brought the entire assembly line to a halt, costing $1.7 billion in losses and eventually forcing Ericsson out of mobile phone production.
By and large, supply chain managers use analytics to help them track the flow of data and goods throughout the lifecycle of their business. With that information, they solve for the highest-margin way to operate. But these days, supply chain managers face much harder questions like, What do we do if demand surges and 10% of our workers take emergency medical leave?[iv] Or, Say that 30 ports shut down.[v] Which products should we drop in order to maximize our balance sheet?
No amount of business acumen can substitute for the level of computing necessary to solve these complex, multivariable optimization problems. If supply chain leaders want to answer these questions while they’re still hypothetical—and prepare for when they aren’t—then they’ll need to augment their teams with data scientists.
Conventional wisdom says that competitive advantage comes from efficiency and scale. But in the post-COVID era, that formula feels incomplete. If we accept the reality that supply chain disturbance is inevitable, then companies will need powerful new tools and skillsets to help build and manage their operations. Fundamentally redesigning a supply chain is challenging, no doubt, but necessary to weather the storms ahead.
Max Wessel is SAP Chief Learning Officer.
[iv] Consider, for instance, the effect of COVID-19 sweeping through many of the world’s largest meatpacking plants.
[v] As they did in 2015, when a labor dispute concerning 14,000 longshoremen in California held up the entire U.S. economy for months, costing some $2 billion a day. See: