INNOVATIONS IN SIMULATION
The first months of 2026 have made one thing unmistakably clear: volatility in global trade is no longer episodic – it is structural. The World Economic Forum and PwC’s latest white paper, Trade Compliance for Leadership: Navigating a Shifting Global Landscape, delivers a clear message: geopolitical fragmentation, sustainability-driven border measures, accelerating regulatory change and rising technological and data demands are collectively reshaping international trade and how organizations manage risk.
These forces are not isolated. They interact, reinforce and compound one another, transforming trade into a complex and continuously evolving system. Their implications extend far beyond trade compliance, reshaping supply chains, capital allocation and asset management.
This shift raises a more fundamental governance question for boards and executive teams: How should organizations make and govern decisions when uncertainty is structural rather than exceptional?
After decades of relatively stable globalization, we have entered an era of structural uncertainty. Trade tensions, sanctions, fragmented customs regimes and new sustainability requirements such as Carbon Border Adjustment Mechanism (CBAM) or EU Deforestation Regulation (EUDR) are continuously rewriting the rules of cross-border business.
The World Economic Forum estimates that more than $9.7 trillion in global trade flows are affected or at risk due to non-compliance. A single failure in traceability or regulatory alignment can trigger delays, financial penalties, asset seizures or exclusion from key markets.
Yet the real challenge is not only the scale of these risks. It is the erosion of predictability itself.
For decades, strategic decisions have relied on forecasts, probabilities and “most likely” scenarios. These approaches assume that uncertainty can be reduced through better data and increasingly refined models, and that tomorrow will resemble yesterday with manageable variations.
When regulatory cycles accelerate and geopolitical shifts invalidate planning assumptions overnight, forecasting can create a false sense of confidence. Probability-based risk models struggle to capture cascading effects across jurisdictions, supply chains and capital allocation decisions. Under structural uncertainty, prediction does not eliminate risk — it can obscure it.
In this context, forecasting is structurally insufficient with complex systems governance. What matters is the ability to explore how decisions and disruptions propagate across complex systems. The real question is no longer how to forecast more accurately, but whether forecasting alone can remain the foundation of strategic decision-making.
What has fundamentally changed is not just the level of uncertainty, but the nature of the systems organizations operate in.
Global trade, regulatory frameworks and geopolitical dynamics now behave as complex adaptive systems. But so do the organizations that operate within them. Modern enterprises — spanning global supply chains, production networks, logistics systems and capital-intensive assets — are themselves complex systems embedded within a wider ecosystem of markets, regulations and geopolitical forces.
These systems are characterized by deep interdependencies, non-linear effects and delayed, cascading impacts that are difficult — if not impossible — to predict in advance.
A regulatory change in one jurisdiction can reshape sourcing strategies worldwide. A geopolitical shock can disrupt trade routes, energy flows and investment returns far beyond its point of origin. Sustainability regulations can simultaneously affect compliance, cost structures, asset utilization and market access.
Inside organizations, the same dynamics are at play. What were once relatively linear value chains now operate as interconnected value threads — cross-functional activities such as procurement, forecasting, production, logistics and customer operations that continuously influence one another.
In complex systems, small events can trigger disproportionate and often unsuspected consequences whose full impact may unfold gradually and remain invisible in the short term.
In this context, deterministic planning, simplified models or historical data alone are insufficient by design. They attempt to forecast the future by projecting patterns from the past, assuming a degree of linear stability that no longer exists.
When uncertainty is structural and interactions are non-linear, prediction alone loses relevance; understanding systemic impact becomes the real source of decision quality.
What is emerging is a shift toward impact-based decision making, enabled by simulation-based approaches.
In complex and volatile environments, the value of a decision no longer lies in its performance under a few expected scenarios. What matters is its ability to remain viable across multiple plausible futures and the trajectory of impacts it creates over time across operational, financial and sustainability performance.
Rather than asking what is most likely to happen, leaders increasingly ask what the impact would be if a given scenario materialized. This shift fundamentally changes governance:
Gartner reports on Decision Intelligence highlight the need to reinvent decision frameworks to cope with rising levels of complexity and uncertainty.
Simulation-based decision making allows boards and executive teams to explore how disruption and decisions propagate across operations, assets, compliance, financial performance and sustainability.
This transformation is particularly visible in domains where operational and strategic decisions are deeply interconnected — notably trade management, supply chains and capital-intensive asset systems.
In these environments, decisions increasingly operate under multi-dimensional constraints and trade-offs, where choices simultaneously affect cost structures, resilience, regulatory exposure, sustainability objectives and long-term asset performance.
Simulation-based approaches make it possible to explore these systemic impacts and identify robust trade-offs before committing resources or capital.
This becomes possible when simulation capabilities combine two essential properties. They can reproduce the real-world complexity of a system and its response to decisions over time. They can also generate the full space of plausible future scenarios — including situations never previously encountered.
As a result, leaders can understand how decisions interact and truly perform across the system under very different conditions, revealing optimization opportunities and performance improvements that go far beyond what traditional planning and forecasting methods can achieve.
In global supply chains, these capabilities are already enabling companies to redesign sourcing, logistics and inventory strategies under evolving tariffs, customs regimes and geopolitical disruptions.
A leading global mobility manufacturer, for instance, used simulation to redesign its sourcing strategy for an emerging market. The analysis led to a five-year sourcing plan that reduced logistics costs by €10 million annually, cut transport and customs costs by more than 60%, and improved global profit margins by more than 5%.
This logic applies to more operational decisions. In global supply chains, simulation is increasingly used to evaluate alternative transport or inventory strategies, allowing organizations to adapt decisions as demand, supply constraints or trade conditions evolve.
A similar transformation is taking place in asset-intensive sectors such as electricity transmission, hydropower and large infrastructure networks. Operators of these systems increasingly rely on simulation to explore maintenance and investment strategies under multiple regulatory, operational and financial scenarios.
One European electricity transmission operator has relied on system-wide simulation for many years to evaluate the long-term trajectory of its grid assets over a 30-year horizon. This approach enabled the operator, for example, to demonstrate that increasing maintenance spending in the short term would significantly reduce future costs, avoiding hundreds of millions in additional expenditures over time. The results supported constructive discussions with the regulator, who approved the proposed increase in the operator’s maintenance budget and later encouraged the use of simulation to guide broader asset management policies.
More broadly, this approach enables executive teams to challenge strategies by quantifying over time how CAPEX and OPEX decisions influence future costs, risk exposure, asset life, and asset value. They understand why certain strategies may prove infeasible once technical, regulatory and resource constraints are fully taken into account — moving decision processes beyond top-down budgeting toward more realistic, optimized and scenario-based planning. It provides a shared analytical foundation for prioritizing investments and aligning stakeholders around the evolution of critical infrastructure under uncertain conditions.
Executives today face growing expectations from regulators, investors and boards to explain how major decisions will perform under uncertainty and how they may affect long-term performance, resilience and compliance.
In complex systems, better decisions do not come from better predictions, but from better exploration. The challenge for leaders is therefore to move beyond traditional risk management and toward scenario-based decision making: understanding how strategic and operational choices propagate across interconnected operations, assets, markets and regulatory environments. This implies a fundamental shift at board level, from reviewing plans to stress-testing decisions.
What is changing is that advances in system simulation combined with AI techniques now make it possible to capture the real-world complexity of these systems, explore how decisions unfold across thousands of possible futures, and generate optimized recommendations.
Used this way, simulation becomes more than an analytical tool. It provides a validation layer for strategic decision-making, allowing leadership teams to test assumptions, quantify impacts and challenge strategies before resources or capital are committed.
This is what enables the shift toward impact-based decision making: evaluating decisions not only by their expected outcome, but by the trajectory of impacts they create across operations, finances, resilience and sustainability.
More importantly, it allows organizations to continuously rehearse decisions — rapidly testing new conditions, verifying strategic assumptions and adjusting plans as environments evolve.
In a world where uncertainty is structural and regulation continuously evolves, leadership is no longer about predicting the future.
It is about continuously rehearsing decisions across multiple potential futures.
