How to Confront Challenges in Digital Twin

By Matanat Rashid

Tue, 23 Oct 2018

The concept of the digital twin is supposed to help various industries undergo a massive overhaul to improve revenues, optimise productivity and increase operational agility by meeting the market demands. Gartner recently conducted a poll which suggested that 48 percent of major organisations that are implementing IoT programmes said they are already using or plan to use digital twins in 2018.

The reason why this technology lures interest and investment is the fact that it offers a compelling promise. However, Alexander Hoeppe (research director at Gartner) believes that understanding and incorporating the digital twin isn’t as simple as it may seem. According to Hoeppe, “creating and maintaining digital twins is not for the faint-hearted, but by structuring and executing digital twin initiatives appropriately, CIOs can address the key challenges they pose.”

The digital twin creates a set of complex challenges which needs to be addressed. Implementing a digital twin without understanding the challenges will not let organisations yield large-scale benefits. Engineering, commercial, financial, technical and most importantly the data management are the main challenges that need to be addressed. Fortunately, the technological giants who are already benefitting from the twins have outlined a few approaches that could be substantial in confronting these complex issues.

Strategizing the Digital Twin to Confront Challenges:

Systematic Approach:

Many organisations possess a misconception that building accurate visual models of physical equipment is what the digital twin is all about. It is imperative to understand that creating models of only physical equipment cannot be classified as digital twinning. The right approach is to create a digital twin that has the connections between a wide range of devices, rather than just replicating a single device type. A standalone model would only improve the equipment, while a systematic approach would improve the entire production process.

Start Small, Expand Later:

It is ideal to play safe in the beginning. The digital twin offers two modelling approaches; going wide or going deep. Experts suggest that companies should start with the wide strategy, then go deep if necessary. Starting with the wide strategy will let the companies grasp the idea of how everything is working together. This will then lead to focus on any individual components that prove to be significant through the deep approach. Starting with the deep approach is the reason why the application of the digital twin fails.

Utilise All the Data:

Collecting piles of big data wouldn’t change anything as data itself, is not intelligence. Thus, it is vital to design a digital twin which can incorporate all the available data. It should be effective to utilise both the structured and unstructured data or else the results will only offer insights that many other technologies could offer too. Feeding limited data to the digital twin will hinder its true power of providing crucial insights regarding problem-solving and improving overall operations.

Target ROI in the beginning:

Organisations should always identify the final project ROI they want to achieve while building the digital twin for their system. It becomes challenging when a company decide to invest in tools (digital twin) without knowing its full potential and ROI. The hierarchy should be prepared to show patience as new digital twins will need tuning through iteration, and iteration may consume time. They need to identify a solution that can pace-up the iteration process.

 

The digital twin, like many other technological evolutions, has its challenges. However, industries benefiting from it clearly suggest that the right approach in implementing the twins would automatically eliminate most of these challenges.


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Matanat Rashid

matanat59@gmail.com

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