Prahallad CR, Partner – Customer Solutions, Robert Bosch Engineering and Business Solutions provided an overview of digital twin technology in a conversation with Dataquest.

What solutions do you offer for a digital twin?

We offer Data Twin, Product Twin, Application Twin, System Twin (will be available from 2022) and Process Twin (will be available from 2024). Their main features include a bespoke, organization-driven, modular digital transformation solution and a cyber-physical system based on sensors, software and a service framework. It has four layers: connect, collect, consume and cognition.

The AI-powered IAPM that relies on natural intelligence and first principles and the digital prediction machine generates physical, technical, operational and business information. These are reinforced by virtual sensors and an interactive and immersive 3D environment. A set of scalable digital engineering models designed to solve specific business problems include digital tools to transform the traditional workforce into an interdisciplinary digital workforce and a collaborative digital solution that enables the C suite to generate business results.

How is the digital twin crucial for the development of IoT technology?

Today, industrial systems are designed, built and operated based on a variety of data sources, many operating environments, and specific business models. The gradual proliferation of computing in the industrial space has encouraged companies in multiple ways to work with huge amounts of data.

A digital twin is a super integrator; it can contextually ingest the information flow of every idea, every process, every machine, every stakeholder and possibly the business goals of the company. Ultimately, this forms a unified digital business that helps improve businesses of any size.

In this digital age, we are exposed to volumes of data generated from multiple sources that flow in as continuous streams. Gathering this data and understanding it to decipher insight to support management decisions is a big challenge.

A digital twin creates a digital highway that combines all data pathways, bringing them together into a single point of truth through a dashboard, immersive environment, or APMIC. It reduces anxiety related to digital transformation or industrial IoT by using full lifecycle data to drive innovation in real time. It also brings transparency and real-time visibility into systems, helping businesses in critical decision making.

What organizational challenges do you see in India? How will the digital twin play a role in identifying these issues?

The on-the-ground challenge of investing and creating a digital twin to drive targeted business results relies entirely on data accuracy across the value spectrum, which connects the physical and digital worlds at all points in the chain. valuable. The top five challenges in building the digital twin include the inviolability of field data, a clear narrative of business issues, missing or invisible data telling an incomplete picture, rare class flaws, and the human factor.

A digital twin can handle predictable and avoidable business challenges, which helps to gather valuable insights. Technical information can help improve TRS, reduce unplanned downtime, reduce maintenance costs, and improve quality. Business insights can help understand asset criticality, plant efficiency, and reduce failure mitigation costs by enabling predictive maintenance. With this progression, organizations are experiencing a boost resulting from the evolution of reliability-centric maintenance.

Digital twins can instill efficiency and help offset rising infrastructure, material and component costs, with predictive and preventative maintenance planning and agile production processes resulting in less waste. It would also help reduce downtime and production times, giving businesses a competitive advantage. It can create the right situations that open the door to innovation and multiply the possibilities of what can be achieved through collaboration. Companies can now establish perpetual connectivity with industrial infrastructure, which would help them reduce costs and leverage new business models to generate additional revenue.

Will the implementation of AI and data analytics in the digital twin allow more information? How? ‘Or’ What?

Essentially, a digital twin helps an organization convert information into data, data into knowledge, and knowledge into wisdom. This wisdom helps organizations drive business results. A unique feature that sets the digital twin apart is its ability to provide access to the subject of the digital twin from anywhere. This enables asset monitoring and enables remote control of the asset under human surveillance by deploying appropriate feedback mechanisms. A digital twin is powered by sensors, software and services which in turn are connected to data and algorithms.

AI, data analytics, and data science are the essential building blocks needed to create successful digital twins for organizations. AI, in layman’s terms, is tasked with turning a digital twin into a scalable decision factory.

The availability of qualitative data, insights from data analysis, and measures for improvement suggested by data science will aid more informed and timely decision making under normal, harsh and distressing operating conditions. . With its ability to generate and separate personality-based recommendations, the digital twin’s automated reporting system will ensure that the right data is available to the right people at the right time; thus improving predictability and transparency. In common parlance, organizations aspire to have digital twins that provide information, correlations, and comparisons on conditions as designed, as built, as operated, and as maintained. They want their staff to be enriched with physical, technical, operational and business knowledge, which enables them to drive business results.

Achieving this scenario in a practical timeframe is extremely difficult when the organization has missing or invisible data. On the contrary, if the organization has systems that have the highest degree of sensor deployment with reliable telemetry, high-end automation, data centers, and command and control centers, they are likely to be more successful. .


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