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Beyond the Numbers

Exploring the World of Simulation Modeling. Follow us to learn some of the keys to successful simulation modeling to maximize business potential.

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  • How Simulation Modeling Improves Supply Chain Optimization

    Supply chain optimization has become one of the most pressing priorities for operations leaders across mining, logistics, manufacturing, and distribution. 

  • Strategic Planning Under Uncertainty: How Simulation Improves Business Decisions

    Strategic planning has always required leaders to make big commitments with incomplete information. What has changed is the degree of uncertainty involved. 

  • Digital Twin vs Simulation: What’s the Difference?

    Executives evaluating a major capital project often hear both terms in the same conversation, sometimes from the same vendor in the same breath. Digital twin vs simulation is a question that determines how much a project will cost, how long it will take, and whether the resulting model will still be useful two years from…

  • 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.