Arena Newsletter and Blog

During my time developing Arena models, I’ve greatly appreciated the speed at which the software runs and the typically short time that it takes to run multiple replications of models. However, I have run into one or two models that were large and complex enough that runtime became a concern. Based on those experiences, I’ve compiled a list of some of the top tips for how to reduce runtime in your simulation models.

Two members of our Arena Consulting & Services team were delighted to have their session submission selected for the Society of Women Engineers WE20 Annual Conference. Their session was titled “When Experience and Excel Aren’t Enough, is Simulation the Solution?” This presentation was intended to introduce discrete-event simulation and when it is an appropriate analytical tool. If you would like to ask questions around this presentation, please send them to arena-info@ra.rockwell.com

We have been receiving a number of questions about building Arena simulation models from healthcare organizations that are struggling to cope with the COVID-19 crisis. While we will continue to provide support as quickly and thoroughly as we can to individual questions, we wanted to write a post to help with general suggestions for users building models focused on predicting healthcare capacity and bottlenecks. 

While simulation tools have improved over time, most of these improvements have been in regards to simplifying technical hurdles, like importing data or making the user interface easier to understand. The complexity of how to break down a complicated, real-world process into concrete steps that can be modeled in a simulation language continues to be the most difficult part of using any package.

Complex, expensive vehicles – such as airplanes or trucks – often contain a number of parts that will fail over the lifetime of the item. Approaching the modeling of this usage and failure tracking can be a tricky prospect. One solution is to use the entity’s attributes to model each part. Attributes can even be arrayed to contain additional information about the individual part, such as the installation date, number of trips made or miles logged, part number, and a unique identification code. But since every entity has a copy of every attribute, what happens when different vehicles have different part requirements? The number of attributes needed to manage this level of complexity can quickly become overwhelming and cumbersome.

Worldwide, the average human is 5’6” (167cm) tall. This is a true fact – I know because I found it on the internet. However, is it a useful fact? Assume for a moment that you are a manufacturer of blue jeans. Would you take this average height value and then size all of your equipment so that it can only make jeans for individuals who are exactly this height? Now imagine your business was more narrowly focused in terms of your potential customer pool, e.g. men living in the United States. If you’re aiming for a smaller group, would it make any more sense to buy equipment that only makes one length of jeans? Or should you take the range of potential heights into account when designing your factory?

In this month’s case study, one of the chief goals of the simulation model was to test several potential operational modifications. When there are several changes to test, the questions arise:

(that no one wants to talk about)

While the technical approach to creating a simulation project is documented and widely understood, there are very important aspects of the process that are not often discussed but which are critical to the success of the project.  Nancy Zupick and Melanie Barker, two of our expert engineers, share four key elements in this post that are imperative to conducting an effective simulation study and how they impact the progress of the study.  If you’d like to read the all of Nancy and Melanie’s thoughts on this topic, download the white paper linked below. 

Welcome to the Arena Tips and Tricks column! We’ll be using this section of the newsletter to highlight features of Arena that we want to call to your attention – either they may not be as well known, or they’re very valuable and deserve a second glance. For our first column, we’ll be starting with the A() Variable.

As anyone who’s been to an airport recently can attest, queueing is part of the experience. We queue to get through security, we line up to order food or drinks, and last we wait our turn to board the aircraft. Most simulation models revolve around contention for resources or constraints with limited capacity, and the modern flying experience has its fair share of contention.