Challenge 1 - Data Management
A digital twin has to be an accurate representation of the physical counterpart. And for that, it requires massive amounts of data. The data can include sensitive information. Therefore, it is crucial to ensure high-level safety.
Mounting to this challenge, companies need to collect, store, process, and analyze these data in real time as well.
The key here is to use robust data management platforms and practices. Cloud-based storage solutions are an option. It can provide the necessary capacity and scalability to handle this data. Also, implementing advanced analytics can help analyze the data more effectively.
For example, Amazon Web Services (AWS) provides cloud computing services that handle large-scale data management for digital twin applications.
Challenge 2 - Integration with Existing Systems
Digital twins need to integrate seamlessly with a company's existing IT infrastructure. This includes ERP, PLM, MES, and other systems. Depending on the compatibility of the systems, this can be quite complex.
Choosing a digital twin platform that offers broad compatibility and interoperability is crucial. It's best to partner with or hire experienced IT professionals to help you with this process. They will have a dedicated team well-versed in the DOs and Don'ts, accelerating the process.
If you take Siemens, for instance, they have their digital twin technology that easily integrates with many existing manufacturing and IT systems.
Challenge 3 - High Costs
It's expensive to create, implement and maintain a digital twin. This is mainly because of the cost of the sensors, software, and computing resources required for the process.
In addition to this, there exist personnel costs for data scientists and other specialists. With digital twins being a relatively new tech, it will also be harder to onboard experts in the field.
The best approach is to start small and scale up. Begin by implementing digital twins for specific processes or machinery, and then expand as you see the benefits and ROI.
Additionally, leveraging scalable and cost-effective cloud services can help manage expenses. Since building an in-house team is harder and more costly, it will be better to partner with a digital twin company.
Challenge 4 - Skills Gap
This is something we have already said in the previous challenge. Like how app developers were scarce two decades ago, the number of experts in digital twins is relatively low.
Furthermore, implementing and operating digital twins requires a specific skill set. The skills include data science, IoT, machine learning, and more. Most organizations have a significant skill gap, making it a crucial challenge to implement digital twins.
One way is to provide comprehensive training and development for current staff. OR hire personnel with the necessary skills. Alternatively, companies can partner with external tech firms or with the required expertise. For example, Toobler offers services to help businesses implement and manage digital twins.
Though there are challenges that seem daunting, you can overcome them with the discussed solutions. And as more and more companies learn about the benefits of digital twins, more solutions will be available. This means challenges like high cost and skill gap will lower considerably.
This brings us to the next section, which is the future of digital twins in manufacturing.