At Rockwell Automation, we get the opportunity to talk with many companies about their use of Arena® simulation software.  Regardless of the industry, we see a lot of commonality in the ways organizations apply discrete event simulation software.  Many companies utilize simulation software because they have a specific and immediate problem to be solved.  These problems tend to “strategic” and are designed to answer big-picture challenges related to the direction of the business.  Discrete event simulation software is an excellent tool to address these sorts of strategic challenges.  However, once they have completed the project and presented their results, regular day-to-day responsibilities take priority and the simulation software is often placed on the shelf until another strategic challenge arises that calls for simulation.  While this type of one-off problem solving generates significant ROI, to focus only on these strategic challenges limits the benefits from discrete event simulation to your organization.

Strategic vs. Operational Simulation Models

A number of forward-thinking companies are expanding their use of simulation beyond strategic decisions and repurposing these models for operational use.  While strategic models tend to address larger directional decisions for a business; operational simulation models are focused on making the best decisions about how the process should function over a much narrower time-horizon using real-time data about the current state of the process.

Why is this important?  The reality is that organizations and their processes are dynamic, “living beings”.  Things are always changing.  The best answer last week isn’t necessarily the best answer for this week.  In order to maintain peak performance, companies must regularly evaluate their performance and make adjustments to ensure business continues to run at optimal levels.  While these incremental adjustments may be small, their cumulative value over time can be greater than the value realized from the “strategic” decisions.

Characteristics of Operational Simulation Models

As with any simulation, the first issue to be addressed is:  what question are we trying to answer?  In the case of operational models, the questions tend to focus on allocation of personnel or resources based upon current demand loads.  The following are a few examples:

• Given current orders/demand, how many resources do I need?
• Where should I assign them?
• How do I best schedule patients given the treatments required?
• How do I dispatch service personnel given current backlog?

Another aspect of operational simulation models is that they need to read in the current state of the operations.  In other words, the model needs to be populated with live or real-time data that reflects the current state rather than employing a warm-up period in the simulation.  This data can be read into Arena in a variety ways as Nancy Zupick discusses in this month’s Tips and Tricks article.

Often, operational models will employ optimization to help drive the simulation to an optimal set of conditions.  OptQuest is an optional add-on to Arena that is an excellent tool for accomplishing this.  The optimization is driven by an objective function.  This is normally an equation that describes profitability for the process.  The optimization algorithm then makes adjustments to the input variables, runs simulations and then evaluates how to best make changes to the inputs so that the objective function is maximized (or minimized).  The end result is a set of optimal inputs than can be implemented within the process that represent an improved state of operation.

Our featured case study describes how MTS Medical Technologies turned a strategic model into an operational simulation model with Arena to improve operational efficiency and ensure their customers meet production requirements.

In short, operational simulation models are an effective way to significantly increase the value your organization realizes from your investment in Arena.