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Exploring the Transformation to the Digital Enterprise: 4 Key Takeaways

Josh Epstein — Wed, 15 May 2024 19:00:00 GMT

Digital transformation has changed how engineering teams design, manufacture, launch, and service products. Many of these teams build and release products at an unprecedented speed — all while adhering to more stringent requirements and regulations than ever before.

To balance velocity and quality, today’s engineering teams are turning to new methods for connecting data and fostering cross-disciplinary collaboration. The digital thread framework — linking product data gathered from across the product lifecycle and its associated manufacturing systems — is becoming especially important. By connecting data from all corners of the manufacturing process, organizations can better collaborate, make intelligent decisions, and meet growing requirements.

In the first episode of Pulling the Digital Thread, Aras’ thought leadership speaker series focused on exploring the digital enterprise's transformation from the perspective of the digital thread, four industry leaders share their thoughts on facilitating an enterprise-level digital thread. The panel included Lionel Greaulou, managing director and founder of Xlifecycle Ltd.; Oleg Shilovitsky, cofounder and CEO of OpenBOM; Brion Carroll, CEO and board member of Sabrion Digital Group; and Aras CTO Rob McAveney. I moderated the discussion.

Here are four of their recommendations for organizations that want to better adapt to today’s fast-paced product lifecycles and high consumer expectations.

1. Break from document-based processes and prioritize data

Historically, organizations have almost exclusively used document-oriented processes. Engineers use computer aided design (CAD) documents, compliance teams use audit-ready documentation, etc. However, today’s digital threads must follow a data-oriented approach to succeed. Documents tend to create silos, making it challenging to see a product from the beginning to the end of its lifecycle. On the other hand, a data-driven approach promotes efficiency and collaboration.

According to Oleg Shilovitsky: “It's getting to the point where [a document-based process] prevents companies from becoming more efficient…We need to break the boundaries of documents, be more granular, start operating with data, and see how we can use data for process management and decision support.”

A data-first approach allows an organization to collect holistic analytics and identify ways to improve processes and resource usage over time. For example, Lionel Grealou says teams can use data analytics to help with supplier selection in procurement or to make financial decisions about which products should be decommissioned or replaced.

2. Build a digital thread with your unique priorities in mind

While there are some standard best practices for building a digital thread (e.g., connecting siloed technology like BOMs and CAD), the specifics of how you develop your digital thread should revolve around solution ideas that empower what makes your business unique – your differentiation, goals and operational structure — which tool connections are most important, which downstream engineering practices your organization uses, etc.

Brion Carroll recommends creating a detailed deployment plan before starting: “It's all based on priority. You have to sit down and come up with a playbook: ‘Who's ready to start adopting this connectivity?’ Where is the value?’ ‘What are the business imperatives, the strategic advantages?’...You don’t need to boil the ocean; you just need to know where to fish.”

3. Leverage the cloud for better collaboration and innovation

Businesses should use cloud-hosted solutions as they build out a digital thread as they are essential to support collaboration with internal and external stakeholders. Why? They increase accessibility allowing teams to collaborate, in real-time, anywhere with an internet connection. These solutions are highly scalable enabling the ability to share complex designs and handle the velocity of change with many stakeholders accessing, visualizing, and commenting simultaneously. Additionally, integration with other productivity tools is seamless enabling teams to connect and share information vs working in disparate tools.

Rob McAveney explains how the cloud allows for greater agility and even innovation:

“We can bring supply chain partners and other business partners into the mix and connect their data to the digital thread. We can also link our data through AI and cloud-based analytics services. Those things are going to provide a lot of value over time. We can bring in external data sources for component libraries and regulatory compliance kinds of requirements, material databases — all these things that can augment our existing digital thread with new data that's out there on the cloud.”

4. Build AI into your digital thread for specific use cases

Today’s manufacturers face unprecedented requirements — whether from societal pressures, sustainability initiatives, market feedback, or internal process changes. They must identify ways to respond to these ever-changing requirements with a cross-disciplinary strategy for the digital thread.

Organizations can turn to emerging AI technology to help them parse through static requirements documentation and decipher which ones apply to which aspects of the product lifecycle.

“This is where AI technology can help us make sense of regulations and their changes,” says Grealou. “It can give humans the right information for making decisions… it brings the ability to compute large data sets across functions and suppliers.”

While AI technology can help organizations better understand and prioritize the right requirements, teams must also use it cautiously. For example, organizations should verify AI output, as many of these tools haven’t been adequately trained.

“Everyone likes to speak about the opportunities of AI these days, but then you go to someone and say, ‘Can I get your data and do some experiments?’ Everyone gets cold feet, right?” says Shilovitsky. “If someone is trying to sell AI, the first question I always ask is, ‘Where did you get the data for your AI?’”

In McAveney’s words: “We should be more concerned about methods to ensure that whatever is produced by this new technology can actually be verified to meet the requirements that we've set out for it. So, let's not be afraid of it — let's utilize it where it works best. But let's also trust but verify.”

Listen to the full conversation about digital thread

Building a foundation for your digital thread with data-driven approaches, organization-specific prioritization, robust cloud connectivity, intelligent strategy for meeting requirements, and proper usage of AI technology can prepare your organization for existing and upcoming challenges and opportunities.

Watch the full Pulling the Digital Thread: Exploring the Transformation to the Digital Enterprise presentation on-demand to learn more.

Rethinking Your Digital Engineering Strategy Leveraging the Power of Generative AI

Josh Epstein — Wed, 14 Feb 2024 21:10:00 GMT

Recently, I had the pleasure of chatting with Rik Rasor, Head of CoE Artificial Intelligence at Fraunhofer-Institute for Mechatronic Systems Design, and Ruslan Bernijazov, Co-CEO at AI Marketplace & Systems, Engineering Lead at Heinz Nixdorf Institute, about the future of artificial intelligence (AI) and its impact on modern product lifecycle management (PLM). Our discussion had four distinct tracks:

Broad approaches to apply AI to PLM and Digital Engineering
Opportunities for Generative AI in PLM
Building AI-augmented natural language search in Aras Innovator
What is the future of Gen AI in PLM and Digital Engineering?

Let's take these one by one.

Applying AI to PLM for Digital Engineering

We can all agree that the hype and excitement surrounding advancements in AI have far exceeded practical applications for incorporating AI into traditional digital engineering tools and business processes. According to Gartner's 2023 Market Guide:

The challenge is to move forward confidently implementing AI into one’s PLM tech stack and do so in a way that provides real value to your company and your customers.

According to Rik, you can break down the current uses of AI in PLM to include machine learning, Generative or GenAI, Large Language Models (LLMs), and conversation agents (like ChatGPT and GPT4; sometimes these are called copilots). We know that machine learning has already been integrated into thousands of engineering applications, so AI is not that new from a research perspective. Rik says that what is new, however, is how, one year after the launch of ChatGPT, we see the field of generative AI is capable of doing something previously expected of humans.

Opportunities for Generative AI in PLM

So, where are the opportunities for using GenAI in PLM? For Rik, they begin with product marketing, go all the way to variant management (VM), and everything in between!

The first step in crafting a strategy is to categorize the broad set of applications into a framework in which diverse personas can understand, evaluate, and prioritize where to start. From there, a roadmap can be built to locate more "low-hanging fruit" that can quickly unveil additional benefits of this technology.

AI in PLM: Three use cases

During the demonstration section of the discussion, three examples of AI use with low-hanging fruit were shared, starting with Systems Engineering. When used with technical documentation, AI can check if a product's new requirements are consistent with its other requirements, related documentation, or previous versions.

The second example was the use of AI in technical documentation drafting. It's possible for AI to generate basic user guides rooted in data and supplemented with a chat user interface. In addition, we see the benefit of leveraging the structure of data in Aras Innovator to create additional, unstructured documentation.

Last, we talked about Enterprise Search and how chat can be used to search your PLM system. This will ultimately require that systems have a fluid user experience and a seamless chat interface to succeed. The system must be able to recognize user prompts and make the appropriate query to the system.

What data infrastructure is needed to apply AI to PLM successfully?

After the demonstrations, we shared our thoughts on what it takes to be successful when developing your AI integration strategy. Rik's concluding remarks revolved around engineering data and the importance of a semantically rich database with sufficient data, an open system that offers the right APIs, and semantics and evidence that AI can differentiate data for extraction.

Ruslan concurred with Rik's view that although good quality data is important, data access is crucial. With much of the data in a PLM system entered by engineers, it seemed like quality was less of a concern than availability.

Where do we go from here?

It comes down to this: you need to start with data; the more data, the better, including more integrated and connected data across the product lifecycle. Then, you need to focus on the semantics of that data. Is it machine-readable? Does it recognize how data information intersects? Is it understandable?

Important things to keep in mind:

Understand what risks should be considered and managed
Make sure you have governance policies around managing AI-augmented business processes
Become comfortable with AI modeling

The most challenging bit may be just becoming used to AI modeling. While it is clearly a mistake to think AI can't understand inconsistent requirements, it is equally dangerous to trust AI too much. AI often has what are called “hallucinations,” in which it creates an answer to a question that sounds good but, in fact, is incorrect. The subject of hallucination detection and mitigation is a hot topic in all LLM circles, and organizations need to carefully monitor the current state of the art and ensure their Gen AI implementations have proper guardrails in place to manage.

Training those in your PLM ecosystem will be a necessary step toward making sure the workforce has the new skills it needs, and that opens the door to wider implementation and greater benefits of AI.

Check out the on-demand webinar here.

Interested in learning more about the role of AI in PLM? Read my earlier post, Exploring Practical AI Use Cases in Product Lifecycle Management.

Exploring Practical AI Use Cases in Product Lifecycle Management

Josh Epstein — Thu, 30 Nov 2023 12:01:00 GMT

Create a Roadmap for Building AI-Augmented PLM Capability

Building value with practical applications of AI is fundamental to digital transformation initiatives in 2023 – and will continue to be in 2024. There is enormous pressure in every organization to keep pace with practical AI innovation in order to stay competitive. While most of the recent buzz is on Generative AI and Large Language Models (LLMs) like ChatGPT, there is a much broader set of AI/ML technologies and workflow automation that are transforming the modern enterprise. Interestingly, adoption seems to have been slower in digital engineering and PLM use cases than in other enterprise business processes like customer support and marketing. While classic simulation and digital twins have been making a massive impact on the engineering space for years, finding practical applications of modern AI is still in its early days. Recent research from McKinsey estimated that 10x more companies are actively relying on classic simulation technologies vs. AI/ML. They estimated that only 5% of organizations are actively using AI/ML today.

We have seen growing interest in the topic from the Aras community and will be exploring the topic more frequently in our blog and monthly webinars. It will also be a topic at our upcoming community event, ACE 2024.

While AI hype is rampant, we’ll strive to focus on practical applications and avoid AI-washing and conflating Generative AI with the broader set of AI/ML and data science technologies that have applications to digital engineering.

Join us for an upcoming webinar with researchers from the Fraunhofer Institute for Mechatronic Systems Design and AI Marketplace to discuss the basics of applying Gen AI to PLM and demonstrate some of the “quick wins” for gaining value from the technology in your digital engineering programs. Here is a preview of and some background for our discussion.

The state of AI, Generative AI, and Large Language Models (LLMs) in PLM

Putting all the buzz aside, Generative AI holds the potential to transform digital engineering and PLM. Gartner predicts that Generative AI will play a role in 70% of text- and data-heavy tasks by 2025, up from less than 10% in 2023. They have also concluded that by 2026, Generative AI capabilities would be implemented in 50% of PLM vendor solutions instead of 5% now. There are many reasons digital engineering may be slower to adopt AI, and it makes for a good discussion. A few to consider include:

Concern over security, IP protection, and regulatory compliance
Data quality and reliability
The complexity of integrating of LLMs into existing application infrastructure
Identifying practical applications that augment existing business processes

Thinking through these topics and creating a proactive strategy for leveraging AI is the first step in building a roadmap for your organization.

The Aras perspective on AI and digital engineering

Aras Innovator^® is an open platform for building collaborative digital engineering and PLM applications on a unified digital thread. Our customers and partners leverage the low code development capability within the platform to extend and adapt Aras applications to fit their specific business needs. The centralized architecture establishes a unified digital thread that connects critical product data, information, and process information across the full spectrum of supported business processes. The open design enables connectivity to the entire digital engineering and enterprise application ecosystem, including manufacturing, supply chain, maintenance, and asset management application ecosystems. This single source of truth for product data, combined with powerful query and search APIs, positions Aras Innovator as a platform on which to build AI-augmented PLM capabilities.

Partners like AI Marketplace and Razorleaf are doing this today.

Exploring the full potential of AI to transform PLM

Aras CTO, Rob McAveney, discussed the subject of AI applications in PLM and digital engineering in this recent video interview. It’s a great discussion about the hype and potential reality of AI-augmented PLM. Rob covered the important subject of AI copilots. The use of this term in the context of digital engineering is on the rise, but typically without any specificity on the applications. Rob discussed the potential for AI-driving virtual assistants to make PLM users’ jobs easier – going beyond basic “auto-complete” or “are you trying to…” type utilities most commonly associated with the concept.

The discussion went beyond the subject of Generative AI to cover the potential for incorporating predictive AI/ML into advanced simulation applications. AI holds the potential to not only transform engineering business process efficiency but also accelerate design cycles, improve product quality, and impact the actual design of organizations’ core product offerings.

Join the conversation on integrating AI and PLM

Let us help you kickstart the conversation about Generative AI in product lifecycle management with a conversation with researchers from Fraunhofer Institute and AI Marketplace. We will cover the basics of AI, Generative AI, and Large Language Models (LLMs) in a discussion focused on finding practical applications of the technology, including intelligent documentation, AI-augmented collaboration, and knowledge management. The webinar will include a live demonstration showcasing AI's practical, tangible implementation in a PLM system, providing experiential insight into the future of intelligent and interconnected product lifecycle management.

Piqued your curiosity? Watch now: Evolution or Revolution? Exploring Applied Generative AI and LLMs for Product Lifecycle Management.

Josh Epstein

Josh Epstein — Tue, 21 Nov 2023 18:46:27 GMT

Josh Epstein is an accomplished technology chief marketing officer with a record of building category-defining enterprise technology brands across various industries, including cybersecurity, cloud infrastructure, data, analytics, and AI. He has held a range of marketing leadership roles at venture-backed startups and global technology leaders. Most recently, Josh served as CMO at AtScale, a data and analytics software provider helping enterprise data teams modernize business intelligence and analytics for the cloud. Prior to joining AtScale, he held marketing leadership roles with ObserveIt, Kaminario, Oracle, and Dell/EMC.