Designing and operating conventional simulation and digital twin models is a time-consuming process that includes the following steps:
- Dragging and dropping objects from pre-built libraries
- Defining the operational behavior of each object according to its properties
- Configuring the model to function accurately
Although dragging and dropping objects from a pre-built library simplifies the design process, assigning properties such as rules or complex logic to objects creates its own set of difficulties… and any increases in the complexity of the facility’s operations create further modeling challenges. For example, developing the complex rules required to model the process of choosing the fastest option between two production lines requires extensive design activities. The designer must consider the crucial operational behavior of each line and integrate them as rules.
Simio eliminates the difficulties associated with designing models using traditional methods by providing you with the intuitive use of data tables. Using data tables you can create and define objects using data coming from real-time data capturing sources. This process involves the following steps:
- Integrating your enterprise data with Simio
- Define objects by using enterprise data
- Modify models in real-time by changing object data
The ability to modify object parameters by tweaking your data is an intuitive approach that eliminates the need to define operational behaviors for every object and continuously redefine these behaviors to capture changing parameters. Thus intuitive approach speeds up the simulation and digital twin modeling process to support real-time operations.
Use Cases for Data Tables
Simio integrates the use of data tables to enable you to simplify design and operational activities when developing:
- Supply chain and logistics models – Supply chain data is generally captured using RFID tags and IoT devices that keep track of transportation assets navigating supply or delivery routes. These data sets are captured and transferred to centralized computing platforms such as your MES or cloud computing storage solutions. The captured data sets provide the information data tables require to rapidly design digital twin models of your supply chain.
The digital supply chain then becomes a virtual platform for evaluating new supply routes and the effects of increased demand, collaborating with third-party suppliers to deal with disruptions, and a monitoring platform for implementing real-time monitoring strategies to optimize your supply chain.
- Production and scheduling plans – Data from pieces of equipment, production assets, and the manufacturing process stored within MES, ERPs, and excel sheets are the sources of information data tables utilize in defining the behavioral patterns of assets within facility digital twin models. Hence, when updating or developing new scheduling plans, data tables and templates ensure that the designer does not have to create or recreate complex logic and patterns from scratch.
Data tables utilize the new data sets to assign new parameters to modeled assets within the digital twin model for developing scheduling and production plans. This speeds up the modeling process and supports the development of accurate real-time schedules.
- Developing Digital twins from simulation models – Robust digital twin models incorporate real-time data transfers to execute real-time evaluations or plans. Conversely, Simulation models rely on historical data sets for evaluating and predicting the future performance of the modeled facility. Data tables enable the development of digital twin models using the simulation model of a facility and real-time data.
Here, the simulation model already incorporates models of the assets and processes within the facility and data tables add real-time behavioral patterns to these modeled assets using real-time data thus, creating a functional digital twin.
- Resource planning and allocation – The digital twin is a powerful tool for evaluating resource allocations and developing plans that maximize their use across the production line. Utilizing data tables to continuously model the ongoing activities across each workstation or production line supports the development of agile resource allocation plans. These plans take into consideration increased real-time demands and their effect on the shop floor’s production capacity. Hence, providing manufacturers with real-time insight into the resources required to meet diverse demand cycles.
Why You Should Use the Data Tables Design Approach
Data tables provide you with a powerful tool that empowers you to leverage your enterprise data. These enterprise data sets could be from your ERP systems, IoT frameworks, edge devices, and even conventional data-storing solutions such as excel sheets. The value-added advantages of using data tables include:
- Rapidly designing models to evaluate diverse operational scenarios such as evaluating the impact of additional resources
- The capacity to identify bottlenecks in real-time across production lines and processes to improve productivity
- Supporting the use of digital twins to develop optimized real-time schedules
- Optimizing capacity and resource planning processes to provide project managers with the information needed to make accurate decisions.
The digital twin and its transformational capabilities is a powerful tool for implementing driving change and implementing new business ideas. The use of Data Tables ensures your digital twin leverages your enterprise’s crucial data sets to provide the insight required to optimize productivity. You can learn more about the application of Digital Tables by speaking to a Simio applications engineer today.