How to avoid double trouble in digital twinS
Digital Twins have been promoted as an essential tool in the armoury of a sustainable and streamlined enterprise in the modern era.
Put very simply, a digital twin is a virtual representation of a facility or process and can be used for a variety of purposes including planning, predicting potential issues and assessing the viability of concepts.
Their use can mean companies are able to avoid the expense of deploying new equipment or changing processes for the sake of an idea which may be a non-starter.
However, as with any technology promising great gains, adopters are also faced with a multitude of challenges to ensure investments in these systems actually deliver what they intend.
Here Insight’s Chris Donkin quizzed experts from across the industrial ecosystem for their tips on navigating pitfalls of deploying a digital twin, with a common theme being the need to ensure the right data is in place from the very start.
Dial D for data
iBwave Solutions director of market development Nazim Choudhury branded data “the backbone of digital twins”, adding “they rely heavily on it to function properly. When data is not accurate, it can lead to challenges that result in issues with the virtual replica”.
This sentiment of ensuring the accuracy of information being fed into the system is prevalent across commentary on the segment, with internal silos and incompatible details coming from legacy systems cited as issues.
Anupam Singhal, president manufacturing at Tata Consultancy Services, said for many in the manufacturing sector, “data is lying everywhere” and they “may not even know what data they have”.
Singhal indicated the requirement to simplify data is being recognised by several of its clients, with many investing in this area.
NTT Data SVP group enterprise IoT products and services Devin Yaung said its analysis of why some digital twin deployments fail to meet expectations found those which “focus on getting their digital twin up and running as fast as possible without consulting other teams across their organisation run the risk of a fragmented deployment that may only be useful to a handful of users”.
“The key challenge is that executives want the flashy details and models but typically forget to invest in how the data will be collected and digested”.
Engagement
Yaung added teams which are set to directly benefit from digital twins should be engaged in the process, given the intelligence they can provide.
“For example, working alongside legacy workers who traditionally inspect machines by hand to determine if downtime is required for a fix. Educating these workers and understanding how this technology can support their on-the-ground experience will maximise benefits”.
As has been a common theme across many industries deploying new technologies, finding the skills to drive and conduct the process can be a significant barrier.
Viktor Clintom, chief operations manager at logistics player Clintopia, noted “many companies struggle to find talent with the right expertise in both the digital and manufacturing spaces. Without the right people, it’s hard to unlock the full potential of digital twins”.
Necessity
Although highlighting challenges, the experts were all positive on the prospects of digital twins, given the continued evolution of technologies adjacent to deployments such as the strides in AI over the last two years.
Singhal concluded for those manufacturers dealing with legacy systems who were perhaps wary, “the choice is not whether I should” but “how soon can I do it” given the ever-evolving ability of the competition to deploy these technologies to boost their operations.
