The 28th to 2nd of March saw Simio follow through on its promise to provide informative tips, techniques, and best practices for using Simio Software to accomplish complex design and operational analysis. Attendees were introduced to powerful features such as the use of templates to speed up the development of complex processes in a model and the use of data tables to auto-generate complex models. This post serves as a follow up to these techniques, as well as, an introduction to the Simio Solutions Webinar Series billed for the 6th of May.
The Data-Driven or Data Generated Approach to Simulation and Digital Twin Modeling
A comprehensively animated simulation or digital twin model in full flow is one of the most beautiful visualization tools you could ever witness in action. Although the bells and whistles of exquisitely designed 3D animations or objects are important features, your simulation and digital twin models are powered by data. Hence, a data-driven or data-generated approach to designing and operating models is the next logical step in simulation and digital twin modeling.
Simio new design feature empowers modelers to take advantage of enterprise data to enhance the design and operation of their models. It also shouldn’t go without saying that this feature supports the use of data generated from any source – Excel, CSV files, and your manufacturing enterprise systems or ERPs.
So, what does this mean for the average Simio user? The data-driven approach means you can optimize the design and operation of already built models by updating model properties using data tables. For example, a model of a machining manufacturing facility built with the traditional drag and drop approach can be reworked to leverage a data-driven design and operate approach. Using the Simio data table feature, properties can be changed intuitively by building a data table using Simio’s table schemas. The table, which holds the task sequence information, can then be applied to the objects within the model.
The data-driven approach also empowers you to transfer the properties associated with a server or object to a new custom server or object. The new custom object will retain the properties associated with the object it mirrors as its default properties. Hence, you can quickly design and integrate new objects into existing models without having to apply the traditional drag and drop approach, as well as to redefine the object’s properties.
The data generated approach to modeling enables Simio users to apply an auto-create technique to automatically create models from your existing databases. This quick start feature ensures that you can quickly build models from scratch without having to go through the extensive drag, drop, and property assignation process. It also supports the addition of automatically built objects into existing models.
The auto-creation process involves using data tables to define the properties and scale of the object, as well as, to define the location of the objects by editing its coordinates. For example, to add an auto-created object to the existing machining facility, defining the X, Y, and Z coordinates in the location subcategory of the table will automatically place the new object in the specified location.
Auto-creating Paths and Workers through the Data Generated Approach
The data generated approach to creating custom objects or servers also apply to the auto-creation of paths and workers to new and existing models. To do this, the starting and ending node associated with an object must be specified using a path table. The values specified within the table determine the path’s link to other objects, its trajectory or course.
A worker table can also be used to auto-create workers to an existing or new model. The worker table enables you to define the trajectory of the worker by defining the worker’s movement coordinates. The ability to auto-create server objects, workers, and paths within a blank slate speeds up the modeling process and enables you to save reusable templates. Saved models created using the data generated approach become templates for quickly designing models, and editing models using your enterprise data.
From the examples above, it’s easy to note that auto-creating more complex models with diverse tasks using the data-generated approach will require the creation of too many tables which could confuse the modeler. To eliminate the need for a plethora of tables, using relational data tables is recommended. Relational data tables refer to tables that capture relational data, using keys, which define the relationship across multiple objects, paths, and workers.
A Demonstration of the Tips and Tricks to Using Simio’s Auto-create, Data Generated Features
Leveraging the use of data tables is crucial to revolutionizing the time-consuming traditional process of building simulation and digital twin models from scratch. Data-driven techniques will help you speed up your modeling, editing, and operational processes as can be seen in the comprehensive video demo showcased in this video.
To learn more tips and tricks with using Simio, register today to attend the Simio Solutions Webinar Series. Demonstrations showcasing the use of new Simio tools such as the Results & Dashboard Reports feature, Constraint Logic, Periodic Statistics, as well as, the use of data-driven/data generated modeling will be provided.