Recently, I had a fascinating conversation with Fourier-E<\/a>, a company that builds, amongst others, simulations using FlexSim<\/a>, about how organizations actually use simulation. <\/p>\n\n\n\n What started as a first introduction quickly turned into something broader: how we think about learning, complexity, and value in operations design.<\/p>\n\n\n\n Below are the reflections that stayed with me. They tell a story about common pitfalls in building simulations and how to utilize a simulation as a real decision-support tool.<\/p>\n\n\n\n Many companies begin their simulation journey with ambitious goals. They want a full-scale digital twin: the entire warehouse, the full operation, every process, every forklift.<\/p>\n\n\n\n Yet after a few months, they often realize what they really wanted was something smaller and sharper: to test a few bottlenecks, validate layout alternatives, or compare resource strategies.<\/p>\n\n\n\n It raises an interesting question: should those have been the starting point all along?<\/p>\n\n\n\n In practice, probably not. However, a lot of clients aren\u2019t ready to think small at first. They need to \u201csee the whole picture\u201d before they can appreciate what simulation can do. The evolution can therefore go from full digital twin<\/em> to targeted decision model<\/em>.<\/p>\n\n\n\n Building an entire digital twin upfront is costly and the opposite of Agile.<\/p>\n\n\n\n As a consultant, I see a lot of untapped potential for simulation in SME because of this approach. A lot of businesses could profit from a small well-defined simulation model. However, many companies think that they must build a huge model first which results in a very high entry barrier. It\u2019s going to be costly and time-consuming, right? With an agile approach, it doesn\u2019t have to be.<\/p>\n\n\n\n The key questions become:<\/p>\n\n\n\n Stakeholders often want to see the \u201cfinal product\u201d early, but that demand itself can raise barriers. Ironically, insisting on perfection early can delay the very insights that make simulation valuable.<\/p>\n\n\n\n A simulation is not a shortcut that lets you skip the \u201cboring\u201d parts. \u00a0It builds on them. Defining the process, collecting data, and understanding variability are what give the model its backbone. What a simulation adds is the ability to capture how processes truly interact. Not in isolation, but as a living system. It shows the ripple effects of timing, dependencies, and shared resources.<\/p>\n\n\n\n In other words, simulation connects logic to reality. It accounts for process variation, timing mismatches, and the very real limitations of space, motion, and human behavior. That\u2019s where its value lies: not in replacing analytical tools, but in showing what happens when everything runs at once.<\/p>\n\n\n\n One of the most debated topics in simulation is visual detail. It\u2019s easier for an operator or manager to spot errors when they can literally see<\/em> their process. Therefore visuals can add real value to a model. But there is also a trade-off: beautiful visuals take time and money.<\/p>\n\n\n\n Budget approval often depends less on analytical merit and more on how visually convincing the model looks. As a result, simulation becomes a two-in-one tool: part analysis, part marketing. Real human behavior doesn\u2019t follow neat logic. Simulation should capture systemic logic, not individual mood swings.<\/p>\n\n\n\n Each model has its limitations. So when something unexpected happens, it cannot always facilitate that scenario. <\/p>\n\n\n\n The restaurant model for example, couldn\u2019t handle a party of eight because it wasn\u2019t designed for that. It\u2019s tempting to dismiss that as a shortcoming of the model, and expand the simulation to facilitate this option. But often, it\u2019s adding insight. If your standard process can\u2019t handle exceptions, maybe your real-life system can\u2019t either.<\/p>\n\n\n\n Pause and discuss: does the process need to change? Do we need to add this in the model? Or are we dealing with an outlier which we should not build our entire process around and\/or spend too much time simulating.<\/p>\n\n\n\n A good model doesn\u2019t just run smoothly, it reveals where your process doesn\u2019t.<\/p>\n\n\n\n Simulation experts love complexity. It\u2019s fun to build, fun to show off, and adding complexity pushes experts to new limits of their craftsmanship.<\/p>\n\n\n\n But the risk is overbuilding: creating something technically beautiful that adds no real decision value. Having been both a simulation buyer and a builder myself, I recognize how hard it is for decision-makers to know what simulation can (and can\u2019t) do. That small bit of practical experience makes for much better collaboration. If I had to summarize our discussion, it would come down to this:<\/p>\n\n\n\n Simulation isn\u2019t just about predicting outcomes, it\u2019s about creating understanding. Interested in applying the power of simulation to your own business needs? Take a look at the Simulation services<\/a> FlowFast offers. <\/p>\n\n\n\n <\/p>","protected":false},"excerpt":{"rendered":"Starting Big, Then Zooming In<\/strong><\/h3>\n\n\n\n
The Agile Alternative<\/strong><\/h3>\n\n\n\n
A more iterative approach starts with a single area, tests assumptions, refines logic, and then expands if needed.<\/p>\n\n\n\n\n
The Right Tool for the Job<\/strong><\/h3>\n\n\n\n
<\/p>\n\n\n\nThe Power (and Price) of Visualization<\/strong><\/h3>\n\n\n\n
High-fidelity graphics (people, branding, forklifts, or even food ingredients) look impressive and help non-technical stakeholders connect to the model.<\/p>\n\n\n\n
Decision-makers who aren\u2019t familiar with simulation may struggle to grasp its technical value but they do<\/em> respond to what looks real.<\/p>\n\n\n\n
It\u2019s a tricky balance. On one hand, visual realism gets attention and funding. On the other, it risks overshadowing the real question: Is the model actually driving better decisions?<\/em><\/p>\n\n\n\nModeling Human Behavior (or not)<\/strong><\/h3>\n\n\n\n
The extent to which a waiter has attention for their customer\u2019s needs a restaurant simulation can create entirely different outcomes. However, it\u2019s neither practical nor useful to model that level of psychology.<\/p>\n\n\n\nWhen the Model Breaks, Pay Attention<\/strong><\/h3>\n\n\n\n
The Expert\u2019s Trap<\/strong><\/h3>\n\n\n\n
True consulting discipline means questioning every feature. What value does it add?
The best model is the one that is most useful<\/em>, not the one that is most complete.<\/p>\n\n\n\nSitting on Both Sides of the Table<\/strong><\/h3>\n\n\n\n
My advice? Get your hands dirty. Try a free demo version, move a few objects around, run a simple process.<\/p>\n\n\n\n
Once you understand the basics, the conversation with consultants shifts from \u201cshow me something impressive\u201d<\/em> to \u201chelp me learn something valuable.\u201d<\/em><\/p>\n\n\n\nSummary<\/strong><\/h3>\n\n\n\n
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Final thought<\/strong><\/h3>\n\n\n\n
When used well, it\u2019s a bridge between data and decision, between what we imagine vs. what actually happens.
And like every good bridge, it\u2019s built one solid piece at a time.<\/p>\n\n\n\nYour own simulation<\/h2>\n\n\n\n