The Covid-19 pandemic, changes to the global supply chain, and unforeseen occurrences such as the raw material shortages for baby formulas highlight the increasing importance of developing accurate industrial plans to manage productivity. Recognizing the importance of accurate planning, approximately 75% of organizations plan to use a digital twin to improve business operations and respond to changes.
Despite where you are in your digital twin journey, either at the adoption or utilization stage, seeking technical advice from experts will always simplify your journey. These tips are the first in a series of two blog posts discussing how to get the best out of your digital twin. Here, the five most important things to know before getting started will be discussed.
What is a Digital Twin?
The digital twin is a virtual representation of an object or system that spans its lifecycle and is updated using real-time data from the physical system. The digital twin employs the use of simulation, artificial intelligence, and neural networks to evaluate processes and help with decision-making.
Defining a digital twin this way is important because it highlights three important factors –
- You can use your developed digital twin as an optimization tool throughout the lifespan of a project or a facility.
- Your digital twin will leverage near real-time data flows to ensure continuous accuracy
- You can leverage artificial intelligence to reduce the design and modeling challenges and optimize decision-making processes.
With the digital twin, you get a lifelong virtual companion to evaluate, predict, analyze, and improve every aspect of your organization’s operational processes. Using the manufacturing industry as an example, the digital twin becomes a virtual companion for evaluating and implementing business changes from the supply chain to the production floor and product delivery.
Do You Need a Digital Twin or a Simulation Model?
Many enterprises mistake their need for powerful analytical software to mean they require a digital twin and this may not be the case. A digital twin brings more capabilities to the table that may not be required at the moment thus leading to the use of resources that could be useful elsewhere. Hence, before beginning the process it is important to define your project’s requirements and goals.
If the goals focus on evaluating business processes using historical data such as deciding how to allocate resources to meet determined production goals, simulation modeling may be the better option. Simulation models are also virtual representations of physical systems and they’re powerful predictive and analytical tools.
If the goals are expanded to include the use of real-time data to optimize decision-making or to monitor the industrial system, then an interexchange or data flow pipeline is required. The real-time capabilities of a digital twin provide the features needed to leverage real-time data to effect changes or respond to real-time situations. So, the tip here is to decide what best serves your enterprise’s analytical interests before deciding on developing a functional digital twin.
What Technical Resources Do You Need to Develop a Digital Twin?
Everyone knows digital twin software is required to develop models of a system but many overlook the importance of other resources such as data availability and a skilled designer. The digital twin software relies on the data it is fed to accurately monitor or analyze real-world situations. Hence, before developing one a data collection framework is required.
The data collection framework could be manually collected data within an Excel sheet or collected using an MES or ERP application or data collected through advanced IoT frameworks. Cutting-edge digital twin software can utilize the data captured within these frameworks to analyze facility or process operations.
The second resource to consider is the human angle. Developing and putting your digital twin to work requires technical experience in taking advantage of the features digital twin software offers. Hence, motivating your application engineers to improve their modeling skillsets by attending training workshops is required. Other options include partnering with a digital twin software service provider to assist your organization with the deployment process.
How Quickly Should You Expect Returns on Your Investment?
The digital twin complements all your data collection frameworks by putting them to use. The determining factor concerning how quickly you can begin to see returns on your investment is the goals you set and including a timeline of 6 to 18 months to evaluate your digital twin goals after implementation is recommended.
In scenarios where the goals are centered on condition monitoring or risk-based scheduling, the digital twin can begin to collect equipment data once developed and rely on historical data to find irregularities. In this scenario, your organization begins to receive returns on its digital twin almost immediately. For more complex activities such as predictive maintenance and implementing data-driven plant optimization insights, some extended timeline is required before you can begin to receive returns on your investment.
How Do You Choose the Right Digital Twin Software?
With the above tips in mind, the process of developing a digital twin can begin. The first step is determining how to choose the correct digital twin software and service provider to develop models. To choose the right fit, extensive research on the features of the digital twin should be the starting point. The software applications that make your short list must be capable of supporting the industrial-specific tasks you intend to accomplish.
For example, the digital twin software for the manufacturing industry must be capable of ingesting data from Industrial IoT frameworks, and the MES your facility uses. The digital twin service provider must be capable of providing extended technical support to ease the deployment and use process. Technical support can include training, deployment, and developing complex models when the need arises.
Choosing the right digital twin software and building an extensive support system of professionals to manage implementation ensures you can begin to optimize business processes in record time. The final article in this series will focus on getting the best out of your digital twin after implementation. Stay tuned!