Which sectors can benefit from a digital twin?
The digital twin was invented to represent a virtual replica of a real-world object that is run in a simulation environment to test its performance and efficiency. It has now evolved to a transformational model that can simulate, support, and even substitute human decision making to solve complex business problems.
Today, almost all industry sectors can take advantage of this innovation, particularly in manufacturing, oil and gas, metals and mining, retail, and consumer packaged goods (CPGs), among others.
For example, a new car that is being developed would greatly benefit from the creation of its real-time digital counterpart. The makers can use this virtual model to test its performance and solve any potential issues before the car is built or hits the road.
There are several benefits to using digital twin technology: the right interventions can be made during the manufacturing process that can reduce the output of bad batches and increase product quality with lowered costs.
How can businesses service better with digital twin?
A digital twin can be used to simulate business scenarios and manufacturing processes by assessing optimal production possibilities. The model inputs data in real time and helps businesses with actionable intelligence that can increase predictability, availability, product quality, as well as provide viable alternatives and improve risk assessment.
For example, in a manufacturing process, a digital twin can identify adverse temperature changes, predict the possible problems these changes could lead to, and suggest solutions. In the service sector, digital twins can identify different customer journeys and map possible outcomes.
This allows early intervention in case of any possible problems, therefore allowing companies to better manage customer expectations. Digital twin technology also provides the customer care department with enhanced insights and, thus, improves the Net Promoter Score (NPS).
Elsewhere, several governments across the world have employed the digital twin model in city planning to ensure weather-related issues are predicted and planned in advance.
How is Machine Learning and Digital Twins connected?
The advancement of technology has resulted in ML already becoming mainstream in the world of digital twins. For example, within TCS, one of our Japanese utility customers wanted to build a model for optimizing operating conditions.
By using the digital twin concept, we were able to build a predictive model based on algorithms and ML from historical and real-time operating data. This insight was used to develop an application that can predict optimal operating conditions for coal fired power plants. The Japanese customer found the model effective in reducing costs with lowered fuel consumption.
Could you provide an example of a successful digital twin model implementation in the service industry?
One of the largest telecom providers in South Africa wanted to improve its customer service level, customer satisfaction, and lower SIM swap frauds. When a consumer walks into a retail store of any telco and orders a high-end phone, normally the order is created in a system and the manual process begins at the back end.
Cases of phone theft, increased delivery time, and losses in transit were increasing. TCS innovated a digital twin of the entire customer journey, predicting pain points and issues rising in multiple scenarios, and designed automated mitigative measures with little to no human intervention.
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