Digital twins in process industries involve complex modeling and integration aspects

Praveen Mysore, India Industry Consultant Senior Director, Dassault Systèmes, India.

How has the digital twin evolved from a design-stage simulation tool into a living model that continuously influences product design and manufacturing decisions?

Initially, digital twins were used mainly for virtual prototyping: engineers ran physics-based simulations on 3D models to validate concepts, reduce physical tests, and optimise designs before production.

As connectivity, cloud, and IoT matured, these models began ingesting real-time operational data, creating a continuously updated representation of the asset or system that spans design, manufacturing, and in-service use.

Today’s Virtual Twins can detect anomalies, predict failures, and suggest design or process changes, enabling faster iterations, higher OEE, and better sustainability outcomes on the shop floor and in the field.

This closed loop from ‘as-designed’ to ‘as-manufactured’ to ‘as-operated’, means every operational insight can flow back into engineering, driving ongoing improvement rather than one-off optimisation.

Gen7 from Dassault Systèmes is the next generation of its 3DEXPERIENCE platform, combining virtual twin, generative experience, knowledge and know-how to create intelligent, software-defined products and manufacturing systems.

By unifying legacy environments with Gen7’s Virtual Twin Experiences, manufacturers can orchestrate end-to-end operations, continuously tune KPIs, and industrialise new products and processes with higher speed and lower risk.

Can digital twins realistically reduce physical prototyping and time-to-market without compromising reliability – and where do physical trials still remain indispensable?

Digital twins can realistically cut a large share of physical prototypes and shorten time-to-market by letting teams validate performance, manufacturability, and integration virtually often with richer insight than a traditional test program.

Dassault Systèmes’ Virtual Twins enable high‑fidelity, multi-physics and system-level validation, so physical tests become fewer, earlier, and more targeted rather than eliminated. Physical trials, however, remain indispensable for safety‑critical certification, human factors, and extreme or highly uncertain conditions, where regulators and engineers must still see proof in the real world.

What are the biggest technical or organisational barriers to creating high-fidelity digital twins – data quality, model accuracy, IT/OT integration, or skills?

From Dassault Systèmes’ perspective, the biggest barrier is not a single factor but the ability to align data, models, integration, and skills around a clear value agenda. In India, this challenge is amplified by uneven digital maturity and fragmented investments.

Indian industries are making strong, strategic use of virtual twin technology, but the adoption curve is more gradual than in mature markets, with wide awareness yet slower, pilot-heavy deployment and limited dedicated budgets. Indian companies are using virtual twins to sharpen product-market fit, boost manufacturing efficiency, and advance sustainability, and CXO‑led initiatives signal growing top‑down commitment.

How do digital twins in process industries differ from those in discrete manufacturing, particularly when modelling complex, long-life assets and continuous operations?

Digital twins in process industries involve complex modeling and integration aspects compared to discrete manufacturing. Process industries involve bulk manufacturing and may have lots of variability in operations that need to be tracked to ensure optimal parameters settings on equipment/production line for quality output. Also, involves lots of planning for changeover to the next batch or for plant maintenance.

Are we seeing digital twins being trusted for closed-loop or semi-autonomous control in process plants – and what safeguards are needed before this becomes mainstream?

Digital twins provide the bridge between the design and manufacturing of process plants. This helps in engaging in continuous improvements as the plant/equipment model can be leveraged for ‘what if’ study in a virtual environment based on the production process data. This helps largely in exploring various operational parameters in a virtual environment and then upon finalising a specific set of operational parameters, the same can be set up on a process plant for optimal output. This kind of experimentation is not feasible with closed loop or semi-autonomous controls.

Over the next 3-5 years, how do you see digital twins evolving from isolated models to enterprise-wide or even ecosystem-level twins connecting suppliers, plants, and customers?

In the coming years, digital twins are expected to shift from isolated, site-level models to enterprise-wide Virtual Twins that provide a continuous thread from ‘as-designed’ to ‘as-operated’ across business functions. These enterprise twins are expected to extend into ecosystem-level networks where OEMs, suppliers, logistics partners, and customers selectively share models and data to coordinate capacity, quality, sustainability, and service. Dassault Systèmes’ 3DEXPERIENCE platform and Gen7 evolution are specifically built to enable this progression, federating product, plant, and supply chain twins on a single, secure, AI-powered, model-based environment ready for new, outcome-driven business models.

(The views expressed in interviews are personal, not necessarily of the organisations represented.)