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5 Things to Look Out for in the Right Digital Twin Solution for Manufacturing

Slowing productivity has always been the harbinger for the application of new ideas to deliver growth. Providing enough products to cater to an expanding population was crucial to establishing the first industrial revolution and centuries later humanity found itself in the same situation. Productivity growth across the industrial sector fell from 2.9% from 1995 – 2005 to approximately 1.6% from 2006 – 2014…and once again a paradigm shift was required.

Enter Industry 4.0 and the digital transformation technologies that define it.

 - Success in Simulation and SchedulingThe application of digital transformation technologies is expected to assist the industrial sector, with special emphasis on manufacturing, to be agile enough to respond to change. Digital twin solutions fall into this category. The Digital twin will be tasked with leveraging a significant asset many manufacturers overlook; their data.

The ability to capture shop floor data and gain operational insight from it is the foundation of Industry 4.0. Technologies such as IIoT, edge computing hardware, and smart devices make data capture possible while analytical solutions such as simulation modeling software and the digital twin enable real-time analysis.

The Digital Twin – A New Way to Boost Productivity

The digital twin is a virtual replica of physical components, processes, or facility. Unlike the average 3D model/replica, the digital twin forms a cyber-physical entity with the process it mirrors. Thus, interexchange of data across the physical and virtual ream is made possible with a digital twin.

The advanced analytics the digital twin supports helps manufacturers discover hidden bottlenecks and solve complex problems to optimize productivity. There are three major applications of digital twin technology in the manufacturing sector and they include:

  • Process Optimization
  • Condition Monitoring and Management
  • Driving Innovation

The digital twin is applied as a process optimization tool in multiple manufacturing areas which include; capacity planning, predictive maintenance, supply chain analysis, resource allocation etc. For example, an oil and gas service provider tasked with servicing customers across North America struggled with expanding its storage capacity to meet fluctuating demand. Increased customer demand meant storage tanks of different sizes and configurations were required.

 - Success in Simulation and SchedulingTo accurately evaluate its capacity requirements, the service provider developed a digital twin of its current facility’s operations. The model implemented a combination of both discrete (batch) and continuous flow object logic to represent the products present within various stages along the pipeline and planned storage facility. System demand, or in this case product batches coming from upstream pipeline supply, were designed to be based on user-defined deterministic input schedules or random events. System infrastructure, i.e. tankage and connections, was designed to be configurable in terms of number deployed and related capacity.

The digital twin assisted the enterprise with visualizing and accurately analyzing the capacity expansion configuration it required to meet increased demand from its customers.

The cyber-physical environment a digital twin provides also support condition monitoring of the production process to capture factory data to optimize productivity. The data from condition monitoring initiatives are applied to developing predictive manufacturing strategies which can improve productivity by 20% and reduce downtime by 70%.

Choosing the Right Digital Twin Solution

Applying the advanced analytics digital twins provide to manufacturing data produces the insight needed to optimize processes but implementing a digital twin starts with choosing the right technology partner. You can apply the following steps to choose a turnkey digital twin solution:

  1. Ease of Use – The average manufacturer is expected to be an expert with dealing with operations on the shop floor not with advanced analytics using a digital twin. Thus, when choosing a digital twin solution the ease at which it can be used by non-technical individuals must be taking into consideration. Ease of use features such as intelligent-object based modeling and 3D visualization capacity ensures operators can at least understand the results from evaluations and apply them.
  2. Data Collation Capabilities – The digital twin relies on data and the more data available to model a digital twin, the more accurate results it produces. Thus, a best-in-class Digital Twin solution must be capable of integrating data from enterprise resource planning software, manufacturing enterprise systems, IoT frameworks, and edge computing solutions.
  3. Application Track Record – The right digital twin should have an application track record where it has been applied to analyze or optimize operations similar to what your facility experiences. An application track record gives you the information needed to analyze the analytical capacity of a digital twin solution in your manufacturing niche. Customer reviews are also records that can help you make informed decisions before choosing the right solution.
  4. After-sales Services – The technical aspects of utilizing a digital twin can be overwhelming to first-time users. Digital twin solution providers who ensure customers can access dedicated support staff ease the on-boarding process associated with developing a digital twin. Advanced after-sales services can also provide organizations with the skillsets required to leverage the power of complex modeling to evaluate complex operational processes.
  5. Total Cost of Ownership – The digital twin is expected to function with existing technological solutions and data-producing sources. Thus, the total cost of using a digital twin includes the cost of purchasing the software, the costs associated with collecting and transferring data, and the cost of creating a data analysis team to manage the implementation process.

Managers in charge of selling the idea of deploying a digital twin must understand these costs in other to develop accurate ROI analysis to get C-level executives to support the implementation process.

Conclusion

The digital transformation of manufacturing processes using advanced analytical technologies is a continuous process that must be repeatedly deployed to achieve successful outcomes. Thus, manufacturers must approach the application of digital twins as an ongoing enterprise in partnership with experienced service providers to reap its rich rewards. Request a demo today to learn how the Simio Digital Twin can be applied to improve your manufacturing processes.

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