Today, we’re digging into the fact that projects in a portfolio aren’t islands. As any seasoned risk analyst or project manager knows projects are often tangled up with one another in ways that have real effects on overall results. We’ll look at why analysing projects by themselves can fall short, and how running a full portfolio Monte Carlo simulation can give you a clearer, more useful view. We’ll also touch on what happens when things get too complicated.

Interdependencies in Project Portfolios: Why Isolation Doesn’t Work

Imagine you’re managing several projects at once, each with its own schedule, budget, and risks. They might look separate on paper, but in reality, they’re connected:

• Timing Dependencies: Starting or finishing a task in one project might rely on hitting milestones in another. For example, if Project A’s supplier is late, Project B could get held up too.
• Resource Sharing: People, money, and equipment are often spread across projects, so if a key resource gets stretched too thin, costs can climb and timelines can slip.
• Risk Ripple Effects: Risks can spread from one project to another—a supply chain issue might impact several projects, not just one.
  Because of these links, looking at projects one by one isn’t enough. To really understand what’s going on, you need a simulation that takes the whole portfolio into account.

The Upsides and Downsides of Full Portfolio Simulations

Running a Monte Carlo simulation for your entire portfolio brings big benefits. You get a view that covers all the important connections, and you can see the odds for costs, schedules, and risks. But, as portfolios get bigger and more complex, some challenges pop up:

• Heavy Computation: These simulations can eat up lots of computer power and time—sometimes hours or days, even on fast machines.
• Information Overload: Too much data isn’t always helpful. You might get swamped with outputs like correlations and scenarios, making it tough to find the real takeaways for decision-making.
  Let’s look back at Napoleon’s campaign. If he’d had access to modern simulation tools, he’d probably see that the chances of capturing Moscow were tied to loads of different tasks across the whole operation. Faced with a mountain of data, he and his team might struggle to figure out what really matters—a classic case of analysis paralysis.

How to Make Things Easier: Simplified Models

The good news is, you don’t need to model every tiny detail. The trick to effective portfolio risk analysis is to keep it simple. By trimming out the less important stuff and focusing on the biggest risks and dependencies, you can run targeted simulations that actually help you make decisions, instead of just creating more confusion.

• Group similar projects or tasks together.
• Only model the shared resources that swing the most.
• Zero in on event chains that could have the biggest impact.
  This approach turns mountains of data into clear, actionable results. For Napoleon’s campaign, a simplified model could focus on just a handful of key issues—like winter supply logistics—so timely changes could be made.

Wrapping Up: Putting Theory into Practice

Integrated Monte Carlo simulations are a powerful way to manage risk across project portfolios—if you keep complexity in check. Start small, simplify where you can, and make sure your results help drive actual decisions.