Receive business insights and make better decisions by putting your enterprise asset’s data to work. Integrate Simio in Enterprise Workflows to enable data visualization and simulation tasks.
The age of information is being run by data and in every system, business operation, and facility data is produced. Thus, to understand these data-producing processes data visualization, analytics, and in some cases, simulation is required. The match to industry 4.0 is also being driven by data and this is true for both service-driven and production-based enterprises. But before analytics comes the need to capture data. Also, the accuracy of captured data determines the effectiveness of the analytical process.
The need to accurately capture data has also spurred growth in interrelated technologies. These emerging technologies include the following:
- Smart Edge Devices or Hardware
- Edge computing
- Embedded devices
- The internet of things (IoT) and the industrial internet of things (IIoT)
These technologies are currently being deployed across every industry to capture data. Once the process of accurately capturing data has been solved, the next phase is data analytics. Here, the ability to accurately analyze data is also important and data analytics is done in diverse ways depending on expected outcomes.
Data Analysis and Manipulation Techniques
The different types of data analysis techniques are generally grouped into five major processes. These processes include:
- Test Analysis – This process involves the use of database management or mining tools to discover patterns within large data sets or big data. Test analysis is generally applied by enterprises to discover customer behavioral patterns, the effects of increased demand, and manage inventories. The mining tools used here are programming languages such as SQL and Python
- Statistical Analysis – Descriptive analysis and inferential analysis are subsets of the statistical process of managing data. Statistical analysis is used in defining what happened or interpreting process that occurs within industrial ecosystems. This analytical process is used to receive business insight on sales volumes, supply and demand relationships etc.
- Diagnostic Analysis – This analytical process is used to determine the cause or driven factors behind an event. Here, business insight can be found by examining collected data and identifying patterns that led to a problem or reaction. A diagnostic analysis is used in troubleshooting shop floor challenges and service delivery issues.
- Predictive Analysis – This analytical technique is used as a yardstick to accurately predict future occurrences and plan for them. At the asset or equipment level, predictive analysis is used to monitor and manage the lifecycle of the equipment. At the process and facility level, predictive analysis is used in planning and implementing new business models to solve existing problems. It is also used to guide future growth through the digital twin and data visualization.
- Prescriptive Analysis – This process combines insight from diverse analysis and outcomes to determine the next steps to take when making decisions. Prescriptive analysis is generally used by most enterprises as it captures information from multiple processes within a system and analyzes them. Digital twin technology and simulation solutions are also generally used in handling prescriptive analysis tasks.
Enterprises can take advantage of Simio Software data analysis tools to drive business intelligence initiatives. Simio provides an intuitive interface for both predictive and prescriptive data analyses. With Simio, you can receive insight through the following options:
- The Digital Twin – Enterprises can use the data captured from shop floors or facilities to create digital twin environments. The digital twin is a digital representation of physical assets, systems, and process within a facility. It also integrates the data they produce to enable enterprises to develop discrete event simulation and schedules for multiple scenarios, to receive business insight. As a predictive analytical tool, you can digitally execute plans and predict the outcomes of new business models before implementation.
- Digital Transformation – Simio provides a seamless platform for enterprises interested in digital transformation. You can choose to use Simio to digitize facilities and the assets within them or digitize complex systems and large facilities. Whatever your choices are, the end product is accurate data visualization in 3D and the option of manipulating your data. With Simio’s digital transformation features, enterprises can handle predictive and statistical analysis in a 3-dimensional digital environment.
- Data Visualization – With Simio, the option of data visualization through 3D models and animations enables enterprises to share business insights across stakeholders of different backgrounds. Simio also integrates the use of cloud computing to assist multiple stakeholders with assessing visualized data from any location and provisioned device.
Benefits of Using Simio for Data Analysis
The benefits of using Simio for your predictive and prescriptive analysis through the digital twin and data visualizations include the following:
- Visualize Complex Processes – As a digital twin solution, Simio can be used to digitally transform large facilities with hundreds of variables and interrelated relationships. You can then run simulations within your complex digital environment to receive timely business insights.
- Visualize Data in 3D – The option of 3D modeling and animation provides enterprises with more interactive options for data visualization. These 3D models can be used to enhance presentations, marketing initiatives, and communication processes.