A digital twin helps form a unified digital enterprise

Prahallad CR, Partner – Customer Solutions, Robert Bosch Engineering and Business Solutions gave insights into the digital twin technology in a conversation with Dataquest.

What are the solutions you are offering 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 key traits include tailor-made, organisation-driven, modular digital transformation solution and cyber-physical system built on sensors, software, and services framework. It has four layers: connect, collect, consume, and cognition.

The AI-powered IAPM that relies on natural intelligence and first principles and digital prediction machine generate physics, engineering, operational and business insights. These are enhanced by virtual sensors and a 3D interactive, immersive environment. A set of evolving digital engineering models built to address specific business problems includes digital tools to transform the traditional workforce into an interdisciplinary digital workforce and collaborative digital solution that enable the C-suite to drive business outcomes.

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

Today industrial systems are designed, built and operated based on diverse data sources, numerous operating environments and specific business models. The gradual proliferation of IT into the industrial space has encouraged enterprises in multiple ways to work with enormous amounts of data.

A digital twin is a super integrator; it can contextually ingest information stream from every idea, every process, every machine, every stakeholder and eventually the business objectives of the enterprise. Ultimately, this forms a unified digital enterprise that helps improve companies of any size.

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

A digital twin creates a digital highway that combines all the lanes of data, collating them into a single point of truth through a dashboard or as an immersive environment or as an APMIC. It reduces the anxiety around digital transformation or Industrial IoT by employing full life-cycle data to drive real-time innovation. It also brings in transparency and real-time visibility into systems, assisting companies in critical decision-making.

What organisational challenges do you see in India? How will digital twin play a role in identifying those issues?

The challenge in the field to invest and build a digital twin to drive targeted business outcomes rests entirely on the accuracy of the data across the spectrum of value, which bridges the physical and digital worlds at all points along the value chain. The top five challenges in building the digital twin include field data sanctity, clear business problem narrative, missing or invisible data narrating an incomplete picture, rare class faults, and the human factor.

A digital twin can handle business challenges that are predictable and avoidable, which helps garner useful insights. Engineering insights can help improve OEE, reduce unplanned downtime, reduce maintenance costs, and improve quality. Business insights can help understand asset criticality, plant efficiency and reduce failure mitigation cost by enabling predictive maintenance. With this progression, organisations experience impetus resulting in the evolution of reliability centred maintenance.

Digital Twins can instil efficiency and help offset increased costs of infrastructure, materials, and components, with predictive and preemptive maintenance scheduling, and agile production processes causing less wastage. This would also help reduce production downtime and lead times, giving enterprises a competitive edge. It can create conducive situations that open the door to innovation and multiply the possibilities of what can be achieved through collaboration. Enterprises can now establish perpetual connectivity with the industrial infrastructure, which would help them to cut costs and derive new business models for additional revenue generation.

Will the implementation of AI and data analytics in digital twin enable more insights? How?

A digital twin basically helps an organisation to convert information into data, data into knowledge and knowledge into wisdom. This wisdom helps organisations drive business outcomes. 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 monitoring of the asset and allows for the asset to be remotely controlled under human supervision 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, data science are the core elements that are required to build successful digital twins for organisations. AI, in simple terms, is responsible for transforming a digital twin into a scalable decision factory.

Availability of qualitative data, insights churned out of data analytics and improvement measures suggested by data science will help in more informed and faster decision-making during normal, hardship and distress operating conditions. With its ability to generate and segregate persona-based recommendations, the digital twin’s automated reporting system will ensure the availability of the right data to the right people at the right time; thus enhancing predictability and improving transparency. In common parlance, organisations aspire to have digital twins that provide insights, correlations, and comparisons on as-designed, as-built, as-operated, and as-maintained conditions. They want their personnel to be augmented with physics, engineering, operational and business insights, enabling them to drive business outcomes.

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

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