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Beyond the Numbers
Quality as Culture: Cultivating Excellence in Every Step
Quality assurance in discrete event simulation (DES) is not a one-time event; it’s a continual process, a culture deeply ingrained in our best practice series. After all, a misaligned tool can be more detrimental than no tool at all. In this post, we’ll explore the multifaceted approach MISIM takes to ensure our models are robust, reliable, and ready for the rigors they’re designed to simulate.
Unseen Forces: The Critical Role of Shadow Models in Simulation
At MISIM, we consider shadow models an integral component of any well-designed simulation. Their primary advantage? They inject a level of thoroughness into the validation process that is indispensable. Shadow models provide an essential quality assurance check that cannot be understated, ensuring that the underlying simulation model is both accurate and well understood.
Enhancing Simulation Efficiency: The Power of Decoupling Data From Code
In the world of discrete event simulation (DES), the management of input and output data stands as a critical governance function. Our best practice series highlights the segregation of input and output data from model code as a critical workflow enhancement.
Warm-Up for Peak Performance: Optimally Achieving Steady-State in Discrete Event Simulation
The concept of a warm-up period is just as critical in the realm of discrete event simulation as it is in in your exercise routine. Let’s dive into why it is integral to the accuracy of simulation models and how it can be used to reduce compute requirements.
Building Confidence: Harnessing the Power of Confidence Intervals in Simulation
In the precision-driven world of discrete event simulation (DES), confidence intervals are the statistical bedrock upon which we base the reliability of our models. They are the quantifiable boundaries that tell us not just what might happen, but how sure we can be about those predictions.
Controlled Randomness: How Seeding Shapes Simulation Outcomes
In the intricate dance of discrete event simulation (DES), where each step is choreographed by algorithms and probability distributions, the concept of ‘randomness’ plays a lead role. However, this randomness is not left to chance. Through the practice of random number seeding, we introduce controlled variation into our simulations, a pillar in our series on best practices.
