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Plans are Nothing, Planning is Everything - Dwight Eisenhower
While swimming in more data than ever before, uncertainty remains one of the greatest challenges facing businesses. This is particularly true in global trade and logistics, where complex networks of interdependence mean that disruptions can cascade through the system in surprising ways.
As Kenneth Arrow, the Nobel laureate economist, discovered during his time as a weather forecaster for the US Air Force in World War II, sometimes predictions are valued even when they're not accurate. When Arrow suggested discontinuing long-range weather forecasts because they were effectively useless, the reply came back: "The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes."
This anecdote from Peter Bernstein's landmark book "Against the Gods: The Remarkable Story of Risk" highlights a fundamental truth: businesses and economies depend on being able to plan for the future, even when that future is inherently uncertain.
From Divine Will to Statistical Probability
Our modern approach to uncertainty is relatively new. For most of human history, unpredictable outcomes were attributed to the will of gods or fate. It wasn't until the development of probability theory in the 17th century that humans began to think systematically about risk and uncertainty.
Bernstein traces this shift to mathematicians like Blaise Pascal and Pierre de Fermat, who corresponded about gambling problems and developed early probability theory. Their work laid the foundation for a revolution in how we perceive and manage risk.
This intellectual breakthrough had profound practical implications. As Bernstein notes, it was only after these mathematical advances that Lloyd's of London evolved from a coffee house where merchants exchanged gossip into a sophisticated insurance market. By quantifying risk, businesses could now plan in ways that were previously impossible.
The Time Revolution: When Minutes Mattered
Another crucial development in reducing uncertainty was the standardization of time. Before the 1880s, the United States operated under at least 68 different time standards. As railroads expanded, this created massive coordination problems.
Consider that passengers in Buffalo, NY faced three different station clocks: one set to New York City time for the New York Central Railroad, another showing Columbus time for the Lake Shore & Michigan Southern passengers, and a third displaying local Buffalo time. In Pittsburgh, the situation was even worse, with six different time standards used by different railroads.
On November 18, 1883, the American Railroad Association established four U.S. time zones, bringing order to this chaos. This standardization wasn't just a convenience—it was a prerequisite for complex coordination across vast distances. Suddenly, businesses could schedule with precision, reducing a major source of uncertainty.
The Growth Explosion
The ability to manage risk and reduce uncertainty correlates remarkably with economic growth. Historical GDP data shows that for nearly 3,000 years until 1750, economic growth averaged only 0.01% per year. Living standards were essentially flat.
Then came the Industrial Revolution, bringing not just new technologies but new ways of organizing and planning economic activity. Since 1750, GDP growth per person has averaged 1.5% annually—a rate that allows the economy to double every 50 years rather than every 6,000 years as before.
This growth was enabled by three phases of innovation: the Industrial Revolution (1750-1830), mass industrialization (1870-1910), and the IT revolution (1950-present). Each phase introduced technologies that reduced uncertainty and improved planning capabilities. One could argue another has emerged in the 21st century as the internet has come into its own with social media and artificial intelligence.
Today's global trade networks are marvels of coordination, but they also contain hidden vulnerabilities. As the NBER working paper "Hidden Exposure: Measuring US Supply Chain Reliance" reveals, the true dependencies in supply chains are often much deeper than they appear.
Using input-output analysis, researchers have found that US exposure to foreign suppliers—especially China—is much larger than conventional trade data suggest. While the United States sources about 80% of its manufacturing inputs domestically, its "look-through" exposure (accounting for inputs to inputs) shows significant hidden dependencies.
For example, the Vehicle sector's exposure to Chinese industrial inputs is four times higher when measured on a look-through basis versus the face value measure. This hidden exposure creates vulnerabilities that many businesses don't fully appreciate.
The China Factor: The OPEC of Industrial Inputs
China's rise as a manufacturing powerhouse has fundamentally altered global supply chains. As recently as 1995, more than 70% of all intermediate goods were made in developed countries. By the 2010s, China's production of intermediate goods surpassed one-quarter of the world's total—almost twice as large as the next most important supplier (the US).
In 2018, China accounted for approximately 42% of world manufactured intermediates production, earning it the informal title of "the OPEC of industrial inputs." This concentration creates systemic risks that go beyond any individual business relationship.
The dominance extends to trade equipment: China produces approximately 70% of cranes, 86% of chassis, and 95% of containers used in global shipping. Under peaceful conditions, this concentration reduces costs; under stress or conflict, it becomes a vulnerability.
The Tariff Effect: Uncertainty Multipliers
Trade policies, particularly tariffs, can significantly amplify uncertainty. According to research by Furceri, Hannan, Ostry, and Rose, tariff increases lead to economically significant declines in domestic output and productivity. A one percentage point increase in tariffs leads to a decline in output of about 0.12% and in productivity of about 0.23% after five years.
These effects tend to be magnified when tariffs rise during economic expansions—precisely the condition we've seen in recent years. For advanced economies, the decline in output after tariff increases is larger than the baseline, reaching about 1% after four years.
Perhaps surprisingly, tariffs have only small effects on trade balances. This is partly because they induce offsetting exchange rate appreciations, failing to address the trade imbalances they're often intended to correct while still creating significant economic harm.
Robustness vs. Resilience: Two Approaches to Uncertainty
In responding to uncertainty, businesses must balance two distinct approaches: robustness and resilience.
A robust supply chain continues to operate despite shocks, typically through fail-safes, redundancies, and geo-diversified supply sources. Think of a hospital with backup generators—the goal is to maintain operations without interruption.
A resilient supply chain, by contrast, focuses on quick recovery after disruptions. This might involve the ability to swiftly switch suppliers, adjust production schedules, or modify products as needed.
Both approaches have costs. Robustness often requires maintaining multiple suppliers or larger inventories, increasing immediate operational expenses. Resilience may require deeper relationships with suppliers or investments in flexibility that don't pay off during normal operations.
The transformation of the US manufacturing sector illustrates how businesses adapt to changing uncertainty. Since 2001, manufacturing jobs have declined by 24%, while transportation and warehousing employment has increased by 48%.
This shift reflects not just offshoring but a fundamental change in how value is created. Today's logistics networks aren't just moving goods—they're managing time and uncertainty. As one analysis notes, "The logistics industry is helping house then discount then destroy inventory all along the way."
The US transportation industry was valued at $1.7 trillion in 2022, with manufacturing at $2.3 trillion. Together with in-house logistics contributions of approximately $300 billion, this represents a massive investment in managing the flow of goods through time and space.
Conclusion: Embracing Uncertainty
Uncertainty is not new, but our ability to measure and manage it is. From the development of probability theory to the standardization of time, from industrial revolutions to digital transformations, the story of economic progress is partly a story of taming uncertainty.
Yet, as Bernstein reminds us, the assumption that "the future will be like the past" underlies most models and is the root of many problems in statistical modeling. His friend Leibniz warned, "Nature has established patterns originating in the return of events, but only for the most part."
In today's interconnected world, uncertainty is inevitable. What matters is how we respond to it—whether we build systems that are robust enough to withstand shocks, resilient enough to recover quickly, or ideally, both.
As Professor Graham Medley noted in a recent lecture on pandemic modeling, "a half-good answer given before the decision is made is infinitely more helpful than a perfect answer given after the decision is made."
In business as in life, we must make decisions before all the information is available. The most successful organizations won't be those that eliminate uncertainty—an impossible task—but those that develop the tools, culture, and strategies to navigate through it.
References
Books
Bernstein, Peter L. (1996). Against the Gods: The Remarkable Story of Risk. John Wiley & Sons.
Academic Papers and Reports
Baldwin, Richard, Freeman, Rebecca, & Theodorakopoulos, Angelos. (2023). "Hidden Exposure: Measuring US Supply Chain Reliance." NBER Working Paper No. 31820, National Bureau of Economic Research.
Furceri, Davide, Hannan, Swarnali A., Ostry, Jonathan D., & Rose, Andrew K. (2018). "Macroeconomic Consequences of Tariffs." NBER Working Paper No. 25402, National Bureau of Economic Research.
Johnson, Robert C. (2014). "Five Facts about Value-Added Exports and Implications for Macroeconomics and Trade Research." Journal of Economic Perspectives, 28(2), 119-142.
Government and Institutional Sources
Bank of England. (2019). "How has growth changed over time?" Explainers, last updated January 10, 2019.
Bureau of Labor Statistics. (2022). Data on manufacturing and transportation employment trends, 2001-present.
Articles and Other Sources
King, Beau. (2025). "Where did all the manufacturing jobs go?" The Running Signal, January 29, 2025.
King, Beau. (2025). "Ying and Yankee: How the U.S. finds itself shipless in a shipping war." The Running Signal, March 6, 2025.
"Standardized time in the US." (2004). From the book Faces of Railroading published by Kalmbach.
Data Sources
OECD Inter-Country Input-Output (ICIO) tables (2021)
World Input-Output Database (WIOD)
Bureau of Transportation Statistics, Merchant Fleet data 1960-2019
US Census Bureau trade statistics