The summer of 2025 has been anything but calm for global supply chains, which is exactly why decision intelligence belongs at the center of planning right now. The usual choices around demand, production, sourcing, inventory, and logistics face new layers of volatility that human judgment alone struggles to process quickly and consistently.
For enterprises that have not yet embraced decision intelligence and decision automation, the need is now critical.
Many organizations invested in real-time data and dashboards, yet decision makers still face cognitive limits and bias under pressure. Decision augmentation gives planners a way to model choices, compare outcomes, and raise decision quality at speed.
Building a faithful digital supply chain twin is no longer enough: with greater fidelity between the simulation and reality comes more data to sift, which is why smart supply chain analytics (informed by experiential decision intelligence) are now a must-have. This fusion of technologies creates value and pinpoints the drivers of waste and, with an integrated supply chain command center, teams can act proactively rather than reactively.
This aligns with Gartner’s guidance to prioritize business-critical decisions and to improve them by combining human and AI decision making with a feedback loop that measures results. In practice, that loop is powered by continuously comparing and syncing master data with demonstrated performance so the model reflects reality.
Because decisions are only as good as the underlying data, tightening master data to reality matters. Even a simple cadence difference in operations can add hours to real lead time, inflating safety stocks or risking service service – until the mismatch is exposed and corrected by an effective, AI-powered decision intelligence and analytics tool.
Start with the decision, not the tool. Identify one or two high-value, time-sensitive decisions and define the signals and constraints that matter most.
Sync the model to reality. Use the tool to reconcile master data with actuals and to quantify variability, so simulations explore real-world ranges rather than averages.
Simulate, decide, and learn. Run targeted scenarios, execute the chosen plan, and feed back outcomes to refine data and rules over time. This is how the feedback loop compounds value.
With Axon™, our team has built upon the traditional digital supply chain twin with AI-powered decision intelligence and supply chain analytics engines, allowing users to augment daily and tactical decisions with near-real-time modeling and end-to-end simulations that account for uncertainty and interdependencies. Coordinating decisions via the Axon Command Center, users can see a birds eye view of all identified issues and move forward with executing optimized plans based on the best information. The goal is not to replace people but to leverage the technology to help them make evidence-based decisions faster and better.
For instance, Skyline View gives real-time visibility on inventory by product, segment, and location while letting planners simulate the impact of changing routes, modes, or tariffs before they commit. Those simulations translate directly into inventory, lead time, and cost trade-offs that teams can act on.
A quantified example helps to ground the value. When master data is corrected against performance and planners use Axon to tune safety stocks, we commonly see around a 2% reduction in inventory, which can readily translate into millions in material cash savings at scale.
What would you decide differently if you could see the ripple effects first? What if you could test a route change or a tariff shock before it hits service?
Curious how decision augmentation would look in your network? Let’s explore a focused scenario together, then decide whether a deeper demo adds value.
