There’s a long-standing perception in logistics and warehousing that running an accurate simulation requires vast amounts of perfect, highly structured data. That belief has held many businesses back from using simulation technology to its full potential, slowing decision-making and limiting confidence at a time when operations need to respond quickly to change.
It’s an understandable view, rooted in past experiences. Traditional methods, often relying on Excel spreadsheets or legacy tools, demanded either detailed data inputs, technical expertise or both. Most operational teams don’t have those resources readily available, so simulation technology has often been seen as out of reach.
Advances in AI and no-code simulation have removed many of these barriers. Whether the available data is complete or not, it is now possible to build models that are close enough to reality to deliver value, especially for higher level operational questions where extreme precision isn’t critical.
Getting real answers from real world data
Most operations have a decent amount of usable data: timing of deliveries, stock keeping unit quantities, resource shifts, layout diagrams. These may sometimes be rough estimates or incomplete. The key is knowing how to work with what’s available.
Let’s take process timings, for example. If exact figures aren’t recorded, warehouse teams can still use realistic assumptions. They might say each pick takes four seconds or build a more nuanced model where the pick time follows a specific distribution based on samples. More often than not, that level of approximation is enough to produce meaningful insight.
In some cases, even basic data such as where a task starts and ends can be used to estimate how much time is spent walking versus picking. With a bit of modelling, these assumptions can be fitted to what’s observed in production, even without fine grained tracking.
Where timing data is missing altogether, a simple time and motion study that captures footage of real activity over a day or two can generate the input data needed to build a useful model. These are just a few examples of how simulation can be grounded in reality, even when data is limited or coarse.
AI unlocks new capabilities
This shift is possible thanks to the integration of AI techniques that support and augment the simulation process. AI can now synthesise missing data based on the insights already available. If a few days’ worth of process timings are captured, those can be expanded into larger representative datasets. If historical order profiles exist, AI can modify them to simulate different volumes or item mixes.
Layout data can also be generated more easily than ever before. Using lidar or even smartphone camera scans, warehouse layouts can be reconstructed digitally without needing detailed CAD files. That’s particularly useful in cases where formal layout documentation isn’t readily available or has evolved over time.
Another area where AI proves invaluable is in identifying and correcting errors in input data, helping turn it into accurate and usable information. It’s common for shift patterns, inventory records or task logs to contain inaccuracies. AI helps flag and correct these issues, improving data quality with far less manual intervention.
Empowering warehouse teams
The biggest shift is not only technical but also cultural because simulation technology is no longer the domain of analysts and engineers alone. Operations teams are now able to model scenarios directly, using no-code interfaces and platforms that guide users through the process. The barrier of needing to extract, structure and validate massive datasets is fading.
Instead, teams can start with basic assumptions, use built-in assistance to check whether those assumptions are sound and iterate quickly. They no longer need to wait for specialist support, as AI-assisted simulation has become intuitive enough to be useful to anyone with a working knowledge of the operation.
That doesn’t mean every assumption will yield a reliable result. However, AI can help advise users on which inputs are reasonable and what level of approximation will still lead to useful insight. In many cases, the ability to model and test “good enough” scenarios is far more valuable than waiting weeks for high fidelity models that answer narrower questions.
Turning strategic ideas into practical decisions
Simulation’s versatility means it can be applied across the full spectrum of operational questions, from early-stage strategy to fine-tuned tweaks.
At one end of the spectrum, there are strategic decisions where no real-world data exists, for example evaluating a new fulfilment model or forecasting order volumes for a potential new business unit. In these cases, synthetic data and AI generated scenarios can help teams model rough forecasts and compare different system designs. It’s less about precision and more about exploring how outcomes may vary based on different assumptions.
At the other end are highly specific operational changes such as adjusting a conveyor path and speed to ease a suspected bottleneck. These decisions often require high fidelity models and detailed, accurate input data because the expected performance gains are small and need to be quantified precisely.
Between those two ends lies a broad middle ground, where teams are assessing changes to layout, resources or throughput. This is where AI supported simulation is particularly effective, enabling practical, timely insights using the data that’s already available without relying on technical experts or months of preparation.
AI-assisted simulation as an every-day asset
Simulation has been around for decades. What’s changed is how accessible it has become. In the past, it was seen as a specialist tool, limited to teams with the time, technical skill and structured data to make use of it. With AI removing many of the technical and data-related barriers, simulation technology is now something that warehouse teams can use directly, not just to analyse what’s happening, but also to test what could or will happen. This enables faster, more confident decision-making and reduces risk when implementing change.
Whether it’s introducing new technology, altering processes, entering new markets or taking on new clients, organisations can now test scenarios, compare options and understand the likely outcomes in advance. The result is clearer communication, better planning and a higher chance of successful implementation, backed by measurable ROI.
