This is the first in a series of blogs in which we will look at the challenges and advantages of applying quantitative risk analysis methods to Project Portfolios. Project portfolios are often massive, intricate beasts that demand careful handling. Their sheer scale and interconnected nature make analyzing risks a daunting task. In this post, we’ll dive into the unique challenges of conducting quantitative risk analysis on project portfolios using Monte Carlo simulations. We’ll explore why interdependencies between projects add layers of complexity and how advanced modeling techniques can help streamline the process. To illustrate these points, let’s start with a historical analogy that perfectly captures the perils of poor risk management.

The Epic Failure of Napoleon’s Grande Armée

On June 24, 1812, Napoleon Bonaparte led his Grande Armée—estimated at 450,000 to 685,000 soldiers, the largest force ever assembled in Europe at the time—across the Neman River into Russia. This colossal army surged toward Moscow with remarkable speed. By September, Napoleon’s forces had captured the city, only to find it abandoned and torched by the retreating Russians. With few supplies and no real victory to claim, Napoleon held out for a month before ordering a retreat.

The journey back was a nightmare. Harsh winter weather, starvation, disease, relentless attacks from Russian troops and locals, and mass desertions decimated the ranks. By November, only 27,000 fit soldiers remained from the original half-million, with hundreds of thousands dead or captured. The campaign officially ended on December 14, 1812, when the last French troops fled Russian soil—marking the beginning of Napoleon’s downfall.

To truly grasp the scale of this catastrophe, check out this iconic visualization. It depicts the advancing army (in gray, moving left to right) shrinking as it pushes toward Moscow, and the retreating force (in black, right to left) dwindling even further. The line’s width represents troop numbers, while the graph below tracks plummeting temperatures during the retreat.

Napoleon didn’t lose this war through major battlefield defeats; he lost because his army was woefully unprepared for a drawn-out operation across vast, unforgiving terrain in brutal winter conditions.

Drawing Parallels to Modern Project Portfolios

Think about the sheer logistics: Imagine relocating the entire population of Memphis, Tennessee, on foot or horseback to Chicago, Illinois, over rough roads in extreme weather. Factor in hostile locals, scarce resources, and a plan to “live off the land” like in previous European campaigns. In early 19th-century Russia, with its sparse rural population, food, fodder for horses, and other supplies were in short supply and couldn’t keep pace with the advancing troops.

In project management terms, a war like this is the ultimate complex portfolio. Every battle, maneuver, and supply delivery acts as a project or program, often with dependencies spanning multiple initiatives. For instance, a successful battle requires prior supply deliveries, but those deliveries hinge on a secure supply chain—which depends on controlling captured territory. This creates cascading “event chains,” where one risk triggers others across the entire portfolio.

Even in the 21st century, with advanced technology, instant communications, and sophisticated tools, managing these interconnected risks is tough. Back in 1812, it was near impossible. Sadly, any high-level risk analysis Napoleon conducted was flawed or incomplete. How do we know? If he’d done it right, he likely would have scrapped the invasion altogether and sought conquests elsewhere. Who knows how 19th-century European history might have unfolded if Napoleon had embraced proper portfolio risk analysis?

The Challenges of Portfolio Risk Analysis Today

Project portfolios mirror this historical complexity: numerous projects with tight interdependencies, where risks in one area can ripple through others. Traditional analysis falls short, which is why Monte Carlo simulations are essential—they account for uncertainty and variability to predict outcomes more accurately.

But running these simulations on large portfolios isn’t straightforward. Key challenges include:

• Scale and Interdependencies: With dozens or hundreds of projects, modeling every connection can bog down computations.

• Event Chains: Risks don’t occur in isolation; one delay or failure can trigger a domino effect.

• Resource Constraints: Just like Napoleon’s supply issues, portfolios often face limited budgets, timelines, or personnel that amplify risks.

To overcome these, advanced techniques like optimized Monte Carlo methods can improve simulation performance, ensuring faster, more reliable insights.

In essence, Napoleon’s Russian debacle serves as a timeless warning: Underestimating portfolio risks can lead to total collapse. By applying quantitative tools thoughtfully, modern project managers can avoid similar fates and steer their initiatives toward success. In the next series of blogs we will look at practical approaches for running quantitative risk analysis on project  portfolios.