Simulation Lab
This is an innovation prototype that lives solely in Q Labs. Q Labs products are experimental and pre-roadmap, as such they are for awareness and co-innovation interest only. Q Labs Status: Experimentation Background In an increasingly complex business environment, simulations are a key component of any organization’s toolkit for making optimal decisions. Combining simulations with domain models (ontologies) rooted in an organization’s data fabric, allows businesses to explore and evaluate many “what if” scenarios–like changes in market conditions, competitive moves, or operational disruptions–without real-world consequences. Simulations serve many use cases across industries: Industry Use Cases Banking Credit & Market Risk: Stress-test portfolios under adverse economic scenarios to anticipate defaults and downturns. Operational Risk: Model internal processes to identify vulnerabilities and improve internal controls. Threat Intelligence: Simulate emerging risks (e.g., cyber fraud) to enhance proactive risk management. Insurance Catastrophic Events: Simulate geopolitical and natural disasters to forecast potential claims distributions. Underwriting Optimization: Use simulation insights to adjust premium pricing and design effective reinsurance strategies. Government Cybersecurity: Test how cyber-attacks might impact critical infrastructure and public safety. Policy Impact: Simulate long-term economic and social effects of policy interventions. Regulatory Oversight: Inform regulation design to bolster national resilience and public welfare. Corporations Supply Chain Resilience: Model disruptions from geopolitical events and external shocks to identify bottlenecks and optimize logistics. Geopolitical Event Modelling: Anticipate impacts from global events to support strategic planning. Threat Intelligence: Integrate competitive and operational risk data to enhance agility and decision-making. Given the significant role of simulations in Decision Intelligence, we set out to explore potential innovations in this area. Motivation Despite the wide use of simulations in the industry, most simulation approaches–whether based on rules or machine learning models–remain primarily quantitative and rely on human experts to encode their domain knowledge into models and model parameters. The advent of Large Language Models (LLMs) has opened new possibilities in the world of simulations. Given the breadth of their training data –often the entire textual contents of the internet– and their reasoning abilities, LLMs are inherently good at understanding the relationship between events and entities and can understand cause-and-effect relationships for instance, to generate many potential “effects” for a cause event and vice versa. By leveraging this quality of LLMs we can augment human experts’ ability and knowledge to consider a wider range of scenarios. Moreover, by using LLMs as a bridge between qualitative scenario generation and quantitative analysis, we can automate the large-scale evaluation of many “what if” scenarios. Algorithms such as Monte Carlo Tree Search (MCTS) allow us to identify the most promising scenarios to flag to human experts, bringing significant efficiencies to the process of building robust and large-scale simulation systems. How it Works Simulation Lab is our LLM-powered platform that transforms raw data into actionable future scenarios. Here’s a quick rundown of its process: Entity-Event Graph Construction: The system builds a dynamic graph that connects real-world entities (such as companies or public agencies) with a series of time-ordered events. Each event is assigned a likelihood and a severity score, while “leads to” relationships capture the causal flow between events. Scenario Generation: Using our large language model, Simulation Lab expands an initial set of user-defined events. Users can guide the LLM with focused prompts (e.g., “focus on geopolitical consequences”) and adjust parameters like likelihood, severity, and randomness (temperature) to explore a wide range of potential outcomes. Interactive Exploration: A user-friendly, point-and-click interface lets experts build and refine their entity graphs. This visual tool allows you to map out relationships between key actors and drill down into how specific events might cascade through your network. Actionable Insights: The result is a curated set of future scenarios that highlight emerging risks and opportunities. These scenarios can then be used to inform decision-making processes, from risk management to strategic planning, as well as to create alerts for early detection of any leading indicators. By merging expert knowledge with the generative power of LLMs, Simulation Lab bridges the gap between qualitative foresight and quantitative analysis, enabling more robust decision intelligence. Conclusion In conclusion, Simulation Lab represents a forward-thinking approach to decision intelligence by merging the generative power of LLMs with expert insights. This simulation environment transforms raw data into dynamic, actionable scenarios, empowering organizations to proactively anticipate risks and seize opportunities in an ever-changing landscape. As we continue to refine and expand this experimental prototype within Q Labs, we invite collaboration and feedback to help shape its evolution and maximize its impact on strategic planning and risk management. Get in Touch If you like to hear more about Simulation Lab, please get in touch with us, as we are actively exploring different options for how to take it forward and are open to collaboration. Please click here to get in touch and leave us any feedbackQ News Intelligence for Supply Chain Monitoring
This is an innovation prototype that lives solely in Q Labs. Q Labs products are experimental and pre-roadmap, as such they are for awareness and co-innovation interest only. Q Labs Status: Experimentation Background In today’s fast-paced and interconnected world, supply chains form the backbone of nearly every industry. Yet, with increasing complexity and globalization, maintaining visibility into these intricate networks has become more challenging than ever. Disruptions can arise from anywhere: geopolitical events, natural disasters, supplier issues, or even minute inefficiencies in logistics. For businesses to ensure resilience, efficiency, and sustainability, the ability to monitor and adapt their supply chains in real time is no longer a luxury—it’s a necessity. By viewing an organization's supply chain as a graph, with the organization as an entity in the middle, connected to it's tiers of suppliers, Quantexa is uniquely positioned to create a supply chain monitoring capability. We combined Q’s News Intelligence, which delivers NLP-enriched news data from 80,000 global sources producing 1-2 million articles daily. With Q’s text2network which runs entity resolution on textual and unstructured data by extracting entities, identifying relationships between them, and assigning roles to each of these entities. Enabling us to: Retrieve news articles about a supply chain. Supplier focus: Identify financial disruptions, technological advancements, and operational disturbances that could impact suppliers, along with announcements of various innovations. Geospatial focus: Detect environmental disasters and geopolitical events—including civil unrest, terrorism, and war—occurring in supplier locations. Analyze article sentiment and flag anomalies by detecting unusually high volumes of news coverage. Leverage Quantexa’s expert Entity Resolution and linking capabilities to provide a unified view of entities. Utilize cutting edge LLMs to summarize key news articles, assess their potential impact and rank them on severity and likelihood. Present these insights in an AI powered dashboard that integrates Quantexa's mature alerting framework and scoring models Ultimately, this creates Q’s News Intelligence for Supply Chain Monitoring. Our Prototype We have tested this prototype with Apple's supply chain since they publish it for public use yearly. We have kept the visualization generic to target multiple use cases but would love to have your feedback on how you would like to visualize this intelligence. Supplier Focus: This view features an interactive bar chart that highlights anomalous values by event types for Apple’s suppliers. We have identified the highest anomalous category for these event types to immediately showcase the top supply chain risks. Users can interact with the chart by hovering over bars to reveal detailed news summaries from multiple sources. For instance, clicking on the dark green block in adverse events displays ”Luxur Precision Industry Company” and highlights a recent event where the supplier was accused of illegal operations in Taiwan, flagged as a high-risk issue. The impact analysis assesses potential disruptions to Apple's supply chain, including delayed component deliveries and reputational risks. Additionally, each supplier risk includes a severity and likelihood score, helping users filter and prioritize alerts within the dashboard. Geolocation Focus: This view emphasizes the geographical aspect of supply chain monitoring, featuring interactive maps that visualize news events impacting various regions. Each map categorizes events, providing insights into potential disruptions. Users can hover over points to reveal tooltips with more detail. For example, hovering over the large circle in the Infections and Infectious Diseases category for America, displays a recent outbreak of bird flu in Iowa, which led to a large-scale poultry culling. The impact analysis identifies several suppliers in the affected area who may face workforce shortages. The dashboard’s configurable event types allow users to track diverse risks, ensuring comprehensive supply chain awareness. Single Pane of Glass: This view features a comprehensive supply chain intelligence dashboard that integrates all insights into a single interface. On the left-hand side, a heat map visualizes suppliers with a color-coded, merged representation of previously analyzed geographical event maps. On the right-hand side, a prioritized summary displays high-impact events, sorted by severity and linked to key suppliers or critical geographic locations. For example, the flooding event in Bavaria appears first, followed by power outages in Puerto Rico, emphasizing major supply chain risks. This unified view enables users to quickly evaluate and address potential disruptions. Its Future As you can probably tell this prototype presents a world of opportunities and advancements, including making this real time, linking these insights back into Quantexa’s scoring framework and/or utilizing our graph capabilities to add another dimension to the information. We have many ideas for advancing this project and adding new features, but we greatly value your insights and feedback. Please connect below to express your interest in this initiative. Please click here to get in touch and leave us any feedback
