If you’re an engineer on the front lines of product R&D, you know that increased demand for highly individualized products and services is making itself felt across industries. What companies understand is that to deliver the customer experiences and outcomes that distinguish them in the market, they need to deliver highly customized products tailored to the needs and whims of the customer.
This market pressure for personalization means that more products and assets are now designed with their own unique footprint. Increasingly, each product is defined by specific raw materials, assemblies, components, or ingredients. These products can be produced as ordered – and often they require the orchestration of specific operators, plant machines, and partners. Typically, the product or asset is sold not at a predetermined price, as with the mass-market model, but under custom-defined terms and conditions and with specific service agreements.
For product developers, this means designing for wide variability and increased complexity. To manage it all, leading companies are moving to the idea of digital twins and networks of digital twins.
Digital twins and their networks
A digital twin establishes a direct connection between the physical asset and its designed, manufactured, and deployed digital representation. The paired physical asset and its digital representation can be said to be connected.
A network of digital twins (NDT) brings many of these pairings together in a common network, with further connections made to business applications, business processes, and business networks of partners involved throughout the product lifecycle. Applications can include ERP, CRM, and SCM. The most obvious business process would be product lifecycle management – but any critical process could be connected. Business network partners include designers, manufacturing teams, suppliers, maintenance provides, product support specialists, and teams that manage the decommissioning of assets at the end of the product’s lifecycle.
All about the data
The critical thing about digital twins is the data generated. Take the example of a pump running in an oil refinery. In a simple digital twin scenario (no network), you could mirror the conditions under which the pump operates, tracking KPIs such as age, heat, moisture, flow rate, and vibration with data transmitted by embedded sensors (Internet of Things). Based on an analysis of these KPIs over time, you could conceivably predict part failure by drawing correlations between historical conditions and previous failures.
With an NDT, you can share this data with the network, intermingle it with data from all the other machines, and perform more sophisticated analyses. For example, you could generalize the conditions under which parts fail and take remedial action at scale to change these conditions where possible across all deployed machines in all facilities – thus avoiding downtime and extending pump life. At the very least, you could plan better across your deployment and send service technicians to replace parts proactively.
What does this mean for R&D?
The advantages of digital twin technology are increasingly driving product design decisions. Where possible, engineers are thinking about ways to build the NDT idea into the products they sell. Here, what’s critical is the extended nature of the network – the part that incorporates business applications, processes, and networks (lines of business, design partners, suppliers, logistics, etc.).
Let’s say you’re starting with a design for a new pump aimed at improving shortcomings of previous models. With NDT capabilities in place, you can first build an accurate 3D model that incorporates requirements, specifications, documentation, and bills of materials and service. Against this 3D model, you can run simulations to get an accurate idea of performance under various real-world conditions – without having to first build a prototype.
The data used for this simulation, of course, is based on conditions and performance data you’ve gathered from other pumps deployed in your NDT. But because this design is new – with different components in place – you need to work with other design experts and suppliers. With the digital twin, you can readily share all relevant information with working partners.
After the design phase, when the pump is in operations, your NDT collects all relevant data throughout the lifecycle of all pumps of this kind deployed in the field. The result is a single source of truth for entire deployments of machines in the field. Based on this authoritative source of data, design teams enter a virtuous cycle of data transparency that can again be leveraged on a continuous basis to collaborate with partners, internal and external, on design improvements moving forward.
Personalization and complete lifecycle management, too
The NDT idea facilitates the drive toward more personalized products by giving you agility that only digital can deliver. Based on a digital twin template of sorts, you can customize your offering based on customer requirements – pulling data from backend business applications to make informed decisions about costs and inventory.
You can also collaborate with suppliers and other partners based on the digital representation of the individualized product – and then move into production or assembly with complete specifications and documentation ready to go. Once the product is sold or deployed, you can also use the digital twin to manage it in the field, predict maintenance, and execute repair operations. And when it comes time to retirement – especially in the case of large assets deployed in the field – you can capture all relevant information about its lifecycle to make better decisions about new products in the future.
In the end, you’re able to collect the data that enables you to manage the complete lifecycle of the products and assets you produce. This can yield the kind of insight and knowledge that helps you succeed in the digital economy.
If you want to dive in further about how SAP approaches the idea of NDT, read the IDC Infobrief: Network of Digital Twins.