The Competitive Advantage of a Calibrated Digital Twin

The Competitive Advantage of a Calibrated Digital Twin
The Competitive Advantage of a Calibrated Digital Twin

Data-driven, AI-powered, networked “smart factories” are the hubs of the fourth industrial revolution (Industry 4.0). Using simulation to optimize production, reduce downtime risks and deliver the highest efficiency possible in industrial settings is a crucial aspect of Industry 4.0’s focus on data-driven operations.
Facility managers must maintain optimal KPIs to compete, despite dynamic employee numbers, order volumes and logistics bottlenecks. Facility models can help staff identify critical production points to achieve aggressive operational targets, but many of these models are rigid at best or inaccurate at worst. Maximizing productivity necessitates a tunable model, but most facilities do not have the trained personnel or the time to perform adjustments manually. In addition, with so many control variables, it is difficult to know what to even simulate in a model. Models guided by AI and machine learning can navigate these common roadblocks to optimization.

Simulation as part of the design process has become standard across many industries when planning new plants, lines or individual machines. The models used in design simulation help ensure that designs will operate as intended across mechanical, electrical and automation functions, saving time and money during startup. However, after commissioning, these simulations are often not used in operations. That same predictive technology likely already employed in your design process brings real value when applied to process optimization.
Simulation can augment production planning and process optimization, but simulations are only as good as the starting data. On the other hand, a calibrated digital twin of a unit, line or whole plant can provide ongoing, daily predictive insights that simulate an entire plant or process. So, what is a calibrated digital twin, and why isn’t it yet a “standard” like simulation in the design process?
For digital twin accuracy, constantly feeding data from the physical world into the virtual model ensures the simulation is valid. The resulting simulation is a calibrated digital twin. This calibration is essential to trust a simulation’s predictions and recommendations, such as on associate assignment, machine utilization or material handling bottlenecks. Accurate, real-time data is the key that unlocks the ability to use simulation as a predictive tool in operations. Reliable predictions can’t be made with outdated data, leading many manufacturers only to use simulations as a design tool before operations are up and running.

Insight-bearing digital twins

A digital twin is a virtual representation of a physical asset. Particularly in material handling and manufacturing, it is challenging to determine labor and machine utilization, and a digital twin can identify and provide insights and recommendations to improve the efficiency of these systems. While a system is in service, digital twins help users make informed decisions to adjust processes on the fly for improved efficiency.

Facility staff can set up automatic report generation at specific time frames, such as before or during shifts or in preparation for a daily staff meeting. In situations involving many parameters and theoretical combinations to experiment with, automated software helps eliminate redundant or impractical experiments by intelligently identifying feasible ones. These optimizations can reduce thousands of potential trial combinations to tens or fewer, ultimately helping determine the best set of parameters.
Using these concepts, the digital twin methodology provides precise insight for optimizing parameters to meet and maintain KPIs. A calibrated digital twin creates an accurate replica of the assets’ current states to forecast accuracy beyond that of a standard digital twin without feedback, aided by artificial intelligence and machine learning. Fine-tuning operations by providing reports with optimal parameters, such as machine settings, workforce allocations and shipping/receiving capacities, does more than enhance efficiency; it provides a competitive advantage.
A calibrated digital twin minimizes the time required for manually monitoring production data and eliminates human guesswork involved in planning procedural changes and resource reallocation to increase efficiency. Increased efficiency translates to streamlined operation and higher profit margins, empowering manufacturers and intralogistics companies to compete more in demanding markets.

Best practices for implementing or scaling digitalization

For manufacturers to implement digitalization and simulation solutions, there are several considerations:
  • Real-time data is crucial. Without up-to-date, data, accurate precitions are impossible. 
    • Understand how production data is delivered and stored. That data is what needs to be fed into simulations in real time to calibrate a digital twin and power simulations.
  • Identify where downtime risks are most significant to focus on implementing simulation first. For example, in O&M, change management, process management and training–simulation has benefits across processes, and you can employ simulation tools in diverse ways.
  • Ensure the solution chosen delivers flexibility and open customization to provide insight and optimizations for unique applications.
  • Choose a simulation solution with a user interface allowing real-time predictions by operators and managers, not only analysts. Accessibility enables the simulation to benefit every level of design, process, production and optimization–every day, not just when an expert is on hand.

 As with any significant shift, Industry 4.0 technology standardization means manufacturers must keep abreast to stay competitive. There are many simulation approaches and solutions that offer some level of modeling. One-off simulations are inefficient, while dynamic simulations that use real-time data provide lasting value. Predictive simulations are the foundation of future competitive advantage.

About The Author

Colm Gavin has worked for Siemens for 20 years and is currently responsible for promoting digitalization topics from Siemens Digital Industries Software group for machine and line builders. In this position, he has leveraged his experience in discrete manufacturing to help companies take advantage of the innovations of Industry 4.0. Colm holds a Bachelor’s degree in Manufacturing Engineering from Trinity College, Dublin, Ireland.

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