How Digital Twins and AI Transform Product and Service Development

Editor’s Note: Sef Tuma, is Global Lead – Industry X, Intelligent Products and Platforms for Accenture. Matthew Thomas is the North America Lead, Industry X, Engineering for Accenture

Digital twins represent physical objects, systems or processes and are used to enhance the production process and make better products.

Done right, the technology enables a process change that boosts efficiency, accelerates development, delivers better design options and generates new revenue streams.

How can businesses ensure they’re deploying digital twins in the right way? The important thing is to combine digital twins with artificial intelligence (AI). Doing so enables the physical product to be brought closer to the supply chain and customer, dramatically reduces manufacturing cycles and unleashes creativity.

Combining AI with digital twins lets companies embed even more intelligence into the product and service development process, turning it into a cycle of continual evolution. Even when a product is in the hands of customers, two-way interaction with the twins captures information on usage and customer needs that can be used to design new functionality.

This customer-led vision is ground-breaking but complex. It demands tools and processes that enable collaboration at scale across the supply chain.

Agile development is a must. Flexible manufacturing, modular design and automated engineering make it possible to update and refine designs quickly and affordably. Digital twins support this process by enabling a single source of truth that supports “what if” scenario modeling to improve decision making. Concurrently, AI assistants work alongside human designers to streamline the data captured by the twins and create an iterative process.

Digital twins enable products to be sold as a service. Such an approach requires an extended ecosystem of manufacturing, retail, cloud-based services and distributed networks of devices. Here, R&D teams function like platform developers, managing communities of suppliers, ecosystem partners and end-users who are all responsible for aspects of product design. They are supported by AI-enabled tools that capture feedback and support ecosystem management.

For example, AI-powered digital twins enable heavy equipment manufacturers to help customers find and quickly procure spare parts that are compatible with their equipment.

The twin integrates
- their design engineering data (which parts are compatible with which models),
- manufacturing details (which models contain which parts),
- information on where specific models were sold (which models with which parts are in which geographies), and
- how quickly parts typically need to be replaced (via part sales, services insights, etc.).

To reach the end goal of a fully automated, customer-led approach to product and service design, companies requires a four-step migration:

1. Intelligent analytics are used to augment product development with dashboards and control towers.
2. Different stakeholders across different processes gain access to a single source of data to enable better collaboration.
3. AI coaches help human stakeholders with their activities.
4. Digital twins automate individual tasks or processes so that it requires little to no human intervention.

Digital twins make the real world machine-readable and machine-controllable, which makes them ideally suited to AI applications. When the two come together, companies are empowered to produce in new ways and create a new generation of “living” products and services that better meet the needs of their customers.

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