Tag: optimisation

  • Why LLMs Aren’t Enough: Engineering Antifragile Operations with Composable Decision Intelligence

    Why LLMs Aren’t Enough: Engineering Antifragile Operations with Composable Decision Intelligence

    As we navigate the technological landscape of 2026, Generative AI has undoubtedly transformed the way we interact with information. Chatbots and Large Language Models (LLMs) have proliferated across enterprise software, streamlining communication and automating basic workflows. However, for operations and supply chain leaders in complex, capital-intensive industries like FMCG, Automotive, and Retail manufacturing, a stark reality is emerging: LLMs are not a silver bullet.

    While language models excel at processing text, they cannot single-handedly optimise a global supply chain network, nor can they provide the quantitative assurance needed to de-risk a £50m factory expansion. When dealing with physical realities, extreme market volatility, and fragmented legacy systems, text prediction is insufficient.

    By 2026, 75% of Global 500 companies will apply decision intelligence practices

    Gartner

    The definitive competitive edge in 2026 belongs to those looking beyond Generative AI toward Decision AI. It belongs to organisations thoughtfully advancing their tech stack and building on established capabilities to embrace the architecture of a Composable Decision Intelligence Platform (DIP).

    The Industrial Reality: High Stakes and High Volatility

    Traditional planning systems struggle to account for agile and accelerated business and the consequent hazards. Today’s supply chain and operations leaders are caught in a crossfire of overlapping challenges, two of the most critical being:

    • Demand & Supply Volatility: SKU proliferation, shifting consumer behaviours, and frequent supply chain disruptions are breaking static planning models. The inability of legacy systems to cope with this extreme volatility inevitably results in poor service levels, excess inventory, and spiralling costs.
    • High-Stakes CAPEX Uncertainty: Securing funding for major capital investments—whether a new automated line, a facility expansion, or rationalising a post-merger manufacturing network—requires robust, data-driven justification. Without quantitative assurance, it is incredibly difficult to de-risk these investments and guarantee ROI.

    Solving these multi-dimensional problems requires more than just analysing past data; it requires a platform capable of simulating the future and discovering the optimal path forward.

    The Path to Implementation: A Composable Architecture

    At Decision Lab, we deliver Decision Intelligence to help leaders master this uncertainty. We achieve this not through a rigid, black-box AI model, but by building a Composable Decision Intelligence Platform based on responsible AI TRiSM principles.

    Composability is the principle that enables businesses to be agile. Rather than relying on a single vendor’s inflexible suite, a composable DIP orchestrates best-in-class, modular capabilities that ingest data from fragmented ERP, MES, and WMS systems. This creates a unified, dynamic view—an AI Simulation Twin.

    The Strategic Advantage of the AI Simulation Twin

    Instead of waiting years for a fully instrumented, hardware-dependent Digital Twin, leading organisations are accelerating their time-to-value by deploying an AI Simulation Twin.

    Traditional Digital Twin programmes often stall in pilot purgatory due to immense IoT integration challenges, prohibitive hardware costs, and fragmented legacy data pipelines. A Simulation Twin, while still ingesting real data, fundamentally bypasses these immediate infrastructure hurdles. It delivers the core predictive and prescriptive advantages now—providing a high-fidelity virtual environment to solve urgent CAPEX and operational bottlenecks—while your physical IoT maturity can be developed as a separate, parallel track. This decoupling ensures you realise ROI in months, rather than years, before moving into the four pillars of the platform:

    Infographic of the four pillars of a composable decision intelligence platform.

    1. The Cognitive Engine: Autonomous AI Agents

    Agentic AI serves as the reasoning layer of the platform. These agents can interpret complex scenarios, model market volatility, and process multi-tiered supply chain dynamics, translating raw data into actionable context.

    2. The Virtual Sandbox: Simulation

    To understand a complex physical network, you must be able to interrogate it and test it. Practically, that means replicating it. We use simulation to build a high-fidelity digital twin environment, employing appropriate technologies, such as AnyLogic’s multi-method capabilities. A simulation maps constraints, machines, and distribution nodes, providing the holistic view necessary to test what-if scenarios safely. It answers critical CAPEX questions before money is spent.

    3. The Mathematical Engine: Optimisation

    Where simulation shows you what could happen, optimisation dictates what should happen. For us, that means employing mathematical optimisation, such as Gurobi’s world-class mathematical solver, to cut through millions of potential permutations. It discovers the mathematically perfect production schedules and inventory policies—maximising throughput and service levels while minimising duplicated costs. The key is being timely—it is no good getting the answer after it was needed. Gurobi’s speed is key here (Gurobi white paper on solver speed).

    4. The Continuous Learning Loop: Reinforcement Learning

    This is where the platform moves from a passive analytical tool to an active operational asset. By applying Reinforcement Learning, specifically leveraging AgileRL, a platform can learn from real-time feedback. It continually experiments within the simulation, discovering new strategies to navigate supply shocks or demand spikes as they happen.

    Engineering the Antifragile Supply Chain

    The ultimate goal of implementing a Composable Decision Intelligence Platform is to shift operations from a state of fragility to one of Antifragility.

    A robust system merely survives a shock. An antifragile operational system improves when exposed to volatility. When a sudden supply chain disruption occurs, the reinforcement learning algorithms immediately assess the new reality within the simulation, trigger the optimisation engine to recalculate the best path, and deploy autonomous agents to orchestrate a self-adapting response. Relying on singular AI models or monolithic ERPs to solve complex physical problems is being consigned to the past.

    For leaders navigating constant disruption, true agility requires an adaptable, composable ecosystem. By implementing a Decision Intelligence Platform, you gain the foresight not just to predict the future, but to engineer your position—a compelling competitive advantage now and for the future.

    To find out more, check out our case studies or contact us!

  • The Power Couple of Decision Science: Integrating Simulation and Optimisation

    The Power Couple of Decision Science: Integrating Simulation and Optimisation

    In the world of complex decision-making, organisations often rely on two distinct tools. On one hand, there is Simulation (‘What happens if…?’), allowing us to model uncertainty and test scenarios. On the other, there is Optimisation (‘What is the best choice?’), allowing us to find the ideal solution within constraints.

    Separately, they are powerful. But when integrated, they unlock a new level of capability—moving from simple decision support to intelligent, autonomous systems.

    At Decision Lab, we don’t believe there is one single best method for this integration. The ideal approach depends entirely on the business problem at hand. Below, we explore the three primary patterns we use to drive value for clients like Migros, FedEx, and Nestlé, and how these models contribute to building truly antifragile organisations.

    Three Patterns of Integration

    We generally view the integration of simulation and optimisation across a spectrum, moving from tactical support to full autonomy.

    1. Optimisation within Simulation (Complex Decision Support)

    In this pattern, the simulation runs a large-scale system, such as a warehouse. When a complex, real-time decision is required, the simulation pauses to call a dedicated optimisation algorithm.

    How it works: The algorithm solves the specific sub-problem, and the simulation continues, testing how that “optimal” decision performs under real-world uncertainty (like worker delays).

    Case Study: For Migros, we utilised this method. Their warehouse simulation calls an optimisation algorithm to determine the most efficient trolley-picking route every time a new order arrives. This allows us to test the routing logic’s real-world impact on the system’s total throughput. Case, video.

    2. Optimisation controls Simulation (Strategic Design)

    Here, the roles are reversed. An external optimisation wrapper searches for the best strategic solution, such as a factory layout or supply chain network.

    How it works: For every solution the optimiser proposes, it uses the simulation as a high-fidelity “evaluation function” to test performance against stochastic conditions.

    Case Study: For DataForm Lab, an optimisation model proposed various wind farm layouts. Our simulation then tested each layout against uncertain wind and wave conditions to calculate true energy output. The optimiser used this feedback to find the next, better solution.

    3. Simulation trains Optimisation (The Autonomous Future)

    This is where we enter the realm of the Digital Twin and Reinforcement Learning (RL). The simulation acts as a high-speed, risk-free training environment.

    How it works: A machine learning agent interacts with the simulation millions of times, learning an ‘optimal policy’ for making autonomous decisions.

    Case Study: For FedEx, we built a simulator for their linehaul operations. An AI agent was trained inside this simulator to learn the optimal policy on when to “cancel, delay, or add” linehauls based on uncertain package volumes, dramatically improving efficiency.

    Building Antifragility: Beyond Resilience

    Why go through the effort of building these combined models? It isn’t just about efficiency; it is about survival and growth.

    A key philosophy at Decision Lab is Antifragility. While resilient systems merely withstand shock, antifragile systems improve because of it. By integrating simulation and optimisation, we create a “Digital Twin” that acts as a long-term asset.

    We use these models to perform rigorous Sensitivity Analysis—identifying which inputs drive outcomes—and to stress-test operations against millions of potential scenarios. This allows organisations to design supply chains and operations that are prepared not just for the average day, but for uncertainty and volatility.

    Navigating the Challenges

    Every powerful methodology has trade-offs. We mitigate these through a rigorous Verification & Validation (V&V) process.

    • Computational Cost: Evaluating thousands of simulation runs is intensive. We use sensitivity analysis to identify key variables early, reducing the search space and focusing computational effort where it matters.
    • Data Dependency: “Garbage in, garbage out” applies doubly here. We don’t just use average values; we statistically analyse historical data to find correct probability distributions (e.g., ensuring orders follow specific peak-and-trough patterns, not just flat averages).

    The ‘Human-in-the-Loop’

    Ultimately, we build these models with you, not just for you.

    Our methodology relies on a Human-in-the-Loop (HITL) framework. Whether we are helping Gousto compress development time for routing logic from months to days, or helping Nestle optimise warehouse slotting, the goal is the same: to present insights that empower expert judgment, not replace it.

    Ready to build your Digital Twin?

    To ensure a successful project, we look for four preliminary conditions:

    1. A clear, quantifiable business problem.
    2. Access to operational and historical data.
    3. Dedicated engagement from your Subject Matter Experts (SMEs).
    4. Clearly defined system boundaries.

    If you are ready to move beyond simple guesswork and start engineering an antifragile operation, Contact Decision Lab today.

  • Bold Choices in an Uncertain World

    Bold Choices in an Uncertain World

    Why Gurobi’s Latest Keynote is a Blueprint for Antifragility

    In the current landscape of supply chain and operational planning, the search for certainty is a losing battle. Yet, many organisations still build their strategies around static forecasts, hoping the world will comply with their spreadsheets.

    At the recent EMEA Gurobi Summit in Vienna, a different path was illuminated. In their keynote Bold Choices, Proven Methods, Gurobi’s Dr. Kostja Siefen and Ronald van der Velden outlined a comprehensive framework for empowering confident decision-making. They effectively demonstrated that optimisation is not only a tactical tool for efficiency; it is a core method underpinning bold leadership. Their presentation encapsulated what we at Decision Lab recognise as Antifragility and the ability to not just withstand volatility, but to use it to gain a competitive edge.

    While resilience is about surviving a shock, antifragility is about improving because of it. Siefen and van der Velden’s keynote perfectly articulated the mechanics required to build such a system. They argued that to move from tentative planning to bold action, organisations must master three specific areas: challenging the status quo, mastering the projects, and achieving business value.

    Here is how Gurobi’s technical roadmap aligns with the strategic imperative of antifragility.

    Resilience Over Prediction

    One of the most dangerous traps in decision-making is the ‘illusion of certainty’. As noted in the keynote, “failures in planning and strategy often stem from misplaced confidence… rather than from making the ‘wrong’ decision”.

    A fragile system assumes the forecast is correct. An antifragile system, however, relies on a Robustness Strategy. As their presentation articulated, the goal is not to eliminate uncertainty but to design processes that endure when reality diverges from expectations. By moving away from a single, unrealistic scenario and embracing uncertainty-aware optimisation, businesses can design plans that remain effective even when the unexpected happens.

    Evolution Through Pacing

    True antifragility is not achieved through a single ‘big bang’ implementation. It requires an Evolution Strategy, viewing the past and future not as opponents, but as partners.

    The keynote speakers emphasised the importance of a Pacing Strategy, where change is introduced through focused stages that create momentum. Rather than demanding instant perfection, successful optimisation projects ‘start small, learn fast, and adapt’. This iterative approach allows an organisation to absorb stressors, such as data quality issues or stakeholder resistance, and use them to refine the model, making the final solution stronger and more fit for purpose.

    The Art of Adaptive Focus

    In the age of Digital Twins, there is a temptation to model every atom of a supply chain. However, complexity without clarity leads to paralysis. The Gurobi keynote introduced the concept of Adaptive Focus: the practice of purposeful abstraction to create robust output.

    By keeping the level of detail adjustable (by balancing relevance, detail, and time) decision-makers can utilise a ‘zoom lens’ to focus on what truly matters in a crisis. This capability is essential for antifragility; it allows leaders to filter out the noise and make rapid, high-quality decisions based on the relevant constraints of the moment.

    Trust as the Currency of Change

    Perhaps the most critical insight for leadership was that trust drives adoption. No matter how advanced the mathematics, a solution will fail if the humans at the helm do not trust it.

    Gurobi’s Trust Strategy suggests that trust should be an informed stance rather than an emotional leap. By utilising what-if scenarios, explainability, and human-in-the-loop validation, organisations can transform compliance into commitment. When teams trust the ‘black box’, they are empowered to make the bold choices required to navigate volatility.

    AI trust and AI TRiSM, a woman printing to a node network featuring a padlock and a shield with a check mark.

    Building trust in complex systems is essential. See how Decision Lab builds trust into projects from the start using the AI TRiSM framework.

    The Decision Lab Perspective

    The methods outlined by Siefen and van der Velden at the Gurobi Summit confirm that the technology to build antifragile systems is already here. We see the future of supply chain planning as range-based experiment-driven. Indeed, Gartner recently recognised Decision Lab as a representative provider in an Innovation Insight report.

    On the journey from fragile, through resilient, to antifragile, we recognise the speed and power of Gurobi’s world-class solver, leveraging its capabilities for solutions to supply chain and defence related challenges. By combining Gurobi’s proven methods with a strategic focus on antifragility, we help our partners stop fearing uncertainty and start using it to their advantage.

    As a Trusted Partner, we offer Gurobi Compass training. It allows teams to integrate solutions and make the most of the solver quickly, whatever challenges they seek to resolve.

    Our Antifragility C-Suite Roadmap for supply chain from Decision Lab CEO David Buxton. Get your copy