The Ever-Expanding World of Interconnected Digital Twins

In this second installment of Jessie MacAlpine’s two-part report on Interconnected Digital Twins (IDTs), we examine how companies are successfully combining the power of open data sharing with digital twin simulations.

Automotive digitalization (Image: Nvidia)

Automotive digitalization (Image: Nvidia)

Recently, a study by van Dyck et al. published in the Journal of Product Innovation Management identified how leading industry experts view the future (link to companion article) of digital manufacturing and the adoption of interconnected digital twins (IDTs).

When it comes to IDTs, many companies have remained hesitant to adopt the technology due to concerns over their return on investment. If data is shared across companies to develop robust, reliable digital twins, how can a company ensure they maintain a competitive edge within their market or industry?

Although this remains an open question and will likely continue to evolve over the next few years, several companies are already seeing success with IDTs without sacrificing their bottom line. In their study, van Dyck et al. summarized a comprehensive list of use cases where companies successfully develop and employ IDTs to support their digital manufacturing journey.

An important finding is that successful IDTs don’t necessarily require data sharing between companies. Even an IDT that incorporates data from disparate manufacturing sites within a given company or an IDT that integrates multiple devices into a full production simulation can be useful for data-informed decision-making. This type of IDT can even act as a bridge between conventional digital twins and a full adoption of industry-wide IDTs for system-wide developments.

Sustainability and Innovation in the Automotive Industry

For example, the automotive industry includes several interesting use cases where companies are beginning to adopt IDTs using data from both within and between companies.

An ongoing partnership between Volvo Trucks and Scania is using an IDT to improve the development and production of transport trucks. The companies take usage data from connected vehicles and integrate the real-time information into the IDT to support ongoing development and better support customer needs. Despite being competitors, the two companies have worked together to take advantage of their large amounts of real-time data to continue to push innovation in the automotive industry.

In contrast, Tesla uses an IDT without partnering with any other automotive companies. Currently, Tesla employs a digital twin for every electric car to transfer real-time usage data to Giga factories. With this information, battery usage is continuously optimized to support sustainability across Tesla and all their current vehicles. The company can then offer “over-the-air” updates in a subscription-based model based on ongoing and real-time insights from usage behavior across their fleet of Tesla vehicles.

Keeping the Agricultural Industry Competitive

Beyond the automotive industry, the agricultural sector also stands to change with the ongoing shift to digital manufacturing. Recently, John Deere announced their transition to an Operation Center comprising digital twins of farmers’ machinery. Their digital twins rely on Matterport’s spatial data platform and 3D capture technology, and sensing and communication technology integrated into all their farming machinery. To remain competitive against some of the other giants in the agricultural industry, John Deere partnered with two other farming equipment manufacturers in a joint data exchange effort that facilitates IDT development for the companies and farm operations. Farmers can now integrate their multi-brand machinery data into one digital twin platform.

Unfortunately, this new project remains uncertain in terms of the exact ROI for John Deere. Instead, the company seems to be focusing on a layered approach to take advantage of some open data strategies while keeping some operations internal and proprietary. Hopefully, the success of their vision will become clearer over the next few years.

Although not an IDT per se, DataConnect is also an interesting cross-company collaboration in the spirit of open data sharing. DataConnect is a platform developed by five large agricultural OEMs that allows farmers to manage all their equipment in one place. Regardless of the equipment manufacturer, farmers can then view, manage, and control machine data in one system to better optimize resource use and improve sustainability for operations at scale. A natural extension of this partnership and platform will likely be IDTs over the next few years.

Safely and Securely Exchanging Data

In addition to DataConnect, other platforms are emerging that are industry agnostic and focused on collecting and sharing data for IDT development. For example, in Europe, the Industrial Data Space was developed by a consortium of European manufacturers as a decentralized platform for secure data exchange consistent with open standards and governance in the European Union. The idea is that data exchange can be mediated by an institutionalized alliance instead of a single company or entity to ensure the safe, fair, and transparent sharing of data.

Although Adoption is Slow, IDTs Use is Expanding

The term “Industry 4.0” is now more than ten years old. Yet, it often feels like we continue to circle around the same promises of AI-assisted automation and big data analytics in digital manufacturing. To try to better understand the relatively slow adoption of technology across industries, van Dyck et al. interviewed industry experts to see what they think about digital manufacturing trends between now and 2030. By focusing on IDTs, the study can connect common themes in digital manufacturing, such as open data, AI-powered analytics, and real-time decision-making.

Although many companies are approaching IDTs from different perspectives and for various applications, the main takeaway from the study seems to be that IDTs, on almost any scale, can still drive some degree of ROI. Plus, the more disparate the data used in an IDT, often the more robust the resulting simulation.

As companies learn to balance their proprietary data with the insights gleaned from an IDT, we will hopefully see a push toward these robust simulations over the next few years. This will be especially important for attaining key sustainability metrics and ensuring transparency in how companies operate to minimize their emissions.

Read the first part of this report: A New Study Finds No Clear Consensus on the Future of Digital Manufacturing