
Most business leaders I know would agree that increased visibility on the evolution of their supply chain is the key in these uncertain times. Supply chain disruptions are not a "when", but a continuum. Chief Supply Chain Officers need to constantly navigate a long list of uncertainties- geopolitical, financial, regulatory, material shortages, market volatility and estimate the impact of uncertainty exposure on their organization as a 63% revenue loss (Gartner report).
The complexity of supply chain networks has grown exponentially, with the number of interconnections between people, machines, and systems multiplying worldwide by a factor of 200,000 over the past two decades.
This ongoing uncertainty and surge in complexity– combined with pressure to become more energy efficient and a re-evaluation of geographic operational footprints– has led to supply chains that are increasingly vulnerable, with consequences often difficult to anticipate or unforeseen. Each disruption or decision has the potential to set off a series of effects that ripple through the entire supply chain.
Achieving visibility of the future is more imperative than ever before, but also more challenging. Today’s supply chain management requires a more advanced approach to visibility, both comprehensive and dynamic. Technology today offers a solution, but only provided that several types of AI are combined. Generative AI, if integrated with AI simulation, delivers transformative supply chain visibility, enabling all decision-makers to navigate complexity and uncertainties and drive continuous value in supply chain management.
Impact-Based Decision Making, the Key Driver of Supply Chain Visibility
In a complex and uncertain environment, forecasting the future by mirroring the past never works, and this is particularly true when it comes to bringing the visibility necessary for managing high-performing supply chains. Supply chains rarely display the same behavior that they have in the past. The more complex a supply chain is, the more a decision or an event within it can generate cascading effects on the whole, and the more value can be trapped.
This fact fundamentally changes the visibility needs of the supply chain. Decisions can only be made based on the visibility of the impact they will have in the future, or by taking into account the impact of disruptions or external events.
When demand variations are ranging from-5 to +5% all the way to-30 to +30% as in the automotive sector, a demand forecast will always be far from reality. The challenge for the supply chain scheduler is not to obtain the best customer forecast to organize production and supply accordingly. It is about knowing what will be the impact of uncertainty on the supply chain’s future performance- service level, inventory cost, resources- to choose among hundreds of probable demands the one that will best achieve its KPIs.
As risks multiply, the procurement manager's ability to know where to prioritize mitigation actions to have the biggest impact in the future supply chain performance is now crucial to build resilience.
Finally, sustainability, which adds to the list of hundreds of KPIs to consider and the arbitration of competing objectives, makes it even more evident how impact-based decision-making is central to supply chain management today and to the necessary future visibility requirements. Impact now underpins and guides decision-making. This shift demands AI advanced technological solutions to properly support supply chain management.
The Key Role of Simulation When Combined with AI Techniques for Supply Chain Visibility
Providing continuous visibility of the impact of uncertainty and decisions on the future performance of the supply chain is therefore the challenge for all global manufacturers. This is a technological challenge that can only be achieved by combining several AI techniques: complex system simulation; AI guided optimization algorithms; machine learning forecasts and generative AI.
Dealing with intricate supply chain processes and real-world uncertainties, complex system simulation is the pivotal AI technique to bring the advanced visibility needed for the supply chain, both comprehensive and dynamic.
Based on modeling the complexity of the supply chain, taking into account everything that affects its future, all parts of the supply chain and cascading effects can be simulated with the resulting outcomes on every targeted KPI. All possible impacts of decisions or disruptions can be simulated, including edge cases or unknown scenarios.
By combining complex system simulation at its core with AI guided optimization and machine learning, AI simulation delivers accurate present and future visibility with a level of reliability and explainability for supply chain decision making that machine learning techniques alone cannot match: AI simulation also extends supply chain visibility with recommendations on how to reach better performance. Guided by AI algorithms, thousands of simulations are automatically generated and optimized to achieve the optimal trade-off of targeted KPIs. These goal-seeking simulation capabilities can also identify the most vulnerable nodes of the supply chain, those that will have the greatest impact on the business.
Using machine learning forecasts as input for every possible demand variation, AI simulation predicts the impact of each variation on supply chain performance and recommends the demand to consider in order to strengthen the supply plan and improve outcomes. Finally, generative AI introduces a new layer to AI simulation, further amplifying the power of this combined approach.
Generative AI as a Democratizing Force for Transformative Visibility with AI Simulation
The full potential of Generative AI for supply chains lies in its combination with other AI approaches. Building on the foundational role of AI simulation, generative AI, based on Large Language Models (LLM), adds a layer of rapid interaction and guidance, making advanced visibility powered by AI simulation more accessible to a broader range of decision-makers.
By working together, AI Simulation extends generative AI capabilities and avoids the hallucination issues encountered with standard Generative AI applications. It does this because LLM responses in natural language are informed by the results of simulating the impact of decisions or disruptions on the company’s performance.
With robust, hallucination-free insight and recommendations, copilots based on an AI Simulation approach are opening up transformative visibility to all levels of supply chain management.
Both executive and operational managers, not just data scientists or supply chain specialists, can continuously navigate the uncertainty and complexity of their supply chain, thus saving time while minimizing risks and uncovering new opportunities for optimization. For instance, if a main supply route is blocked, the supply chain director can immediately understand the impact on the service rate and precisely identify which suppliers will contribute the most to this decrease. The operational manager can then find the best alternative supply to maintain the service rate with the optimal cost and CO2 trade-off.
Continuous Supply Chain Visibility with AI Simulation: A Catalyst for Change
As we’ve seen, the combination of AI simulation and generative AI offers a breakthrough in achieving future supply chain visibility, enabling businesses to continuously manage complexity and uncertainty. Operating at the core of operations and complex processes, AI-simulation guided copilots empower companies to implement a cycle of continuous improvement, enhancing not only economic performance but also resilience and sustainability in an ever-evolving business landscape.
Today, leading manufacturers are already demonstrating how an AI Simulation approach is elevating their supply chain efficiency, potentially reducing costs by 10-20% while ensuring their net-zero trajectory. By fostering a culture of continuous innovation and resilience, it is evident that combined generative-AI and AI-simulation solutions will increasingly become central to decision intelligence capabilities, ensuring companies’ future readiness for better competitiveness and sustained success.