The Power of Knowledge Democratization in Product Development: Digital Twins and Simulation

The “Expert Bottleneck” in Product Development

In many engineering organizations, critical tasks like simulation, manufacturability checks, or testing are funneled through a limit amount of specialists – creating an “expert bottleneck.” This dependency on experts slows down design iterations and can stall projects.

Let’s consider one real-world example from my experience: When a designer wants to reduce the thickness of an injectable pen (medical device) to use less plastic and meet sustainability targets. The designer makes this change directly in CAD software. But that’s just the first step. Next, the design change moves to the industrial engineer to verify usability, then to the simulation engineer to confirm structural integrity, followed by the tolerance specialist to ensure it meets specifications. Afterward, manufacturing checks feasibility, assembly reviews ease of build, and finally, testing specialists validate performance.

Each handover involves converting files into neutral formats, waiting for experts to perform analyses, coordinating meetings, and making collective decisions. When a product change decision must wait in line for an expert sign-off, valuable time is lost to hand-offs, meetings, and approvals. The result is slow design cycles, where changes that should take hours can stretch into days or weeks or even months.

Ideally, the designer should instantly see the implications of the change. This is exactly what democratizing knowledge achieves: faster, clearer insights—without depending entirely on specialists.

Lack of inhouse Expertise

From another angle, product development studies show that lack of in-house expertise is a significant challenge. In one study, 19% of companies cited “lack of expertise/resources” as a major challenge for product development, which correlated with product launch delays (schedules slipping ~16% on average) and difficulties meeting quality or cost targets.

No Unified Tools and Process

Even when experts are available, using different tools, either due to software type or maturity (excel vs Simulation Software), adds friction – companies use an average of 3.6 different CAD systems and 3.3 different solvers for analysis, which demands tedious and time-consuming file conversions and data re-entry. It’s no surprise that engineers report working with outdated information roughly 28% of the time, leading to costly rework and missed deadlines.

The traditional siloed approach – design here, analysis there, manufacturing elsewhere – introduces extra hand-offs at every step, slowing decision-making to a crawl.

The solution: The Power of Knowledge Democratization in Engineering Product Development: Leveraging Digital Twins and Simulation

Knowledge democratization refers to the process of making specialized information and technical capabilities accessible to a wider audience without requiring extensive training. In the context of technology, democratization implies providing access to tools, processes, and knowledge previously reserved for experts, allowing more people to leverage these resources regardless of their technical expertise level.

The democratization of technology represents a significant shift from the traditional paradigm where specialized bodies of expert professionals maintained monopolies over technical knowledge. Instead, it fosters an environment where technology, tools and processes becomes a shared resources that can be utilized by many, promoting a culture of sharing rather than maintaining rigid corporate hierarchies.

Implementing Knowledge Democratization in Engineering Product Development

Creating Technical Infrastructure

Successfully democratizing knowledge in engineering product development requires appropriate technical infrastructure. This includes:

• Unified Ecosystem - To democratize technical knowledge effectively, organizations must first invest in robust technical infrastructure. This means establishing a unified ecosystem of digital tools that make complex engineering knowledge accessible to a broader group of engineers. A key aspect is integrating previously fragmented systems—such as CAD, simulation software, and data management—into a connected platform, PLM System, or “digital thread” that links all stages of product development  . By connecting these tools, data and insights can flow seamlessly across teams, reducing manual hand-offs and ensuring everyone works from the same information.

• Infrastructure and Scalable Computing resources - To handle complex simulations and digital twin operations.

• Data and Process Interoperability - To enable models, processes, and applications to access, exchange, integrate, and cooperatively use data, ensuring smooth and meaningful data flow between systems while maintaining integrity and usability.

Changing Ways of Working

Beyond technology, the way we conduct analysis needs to be changed:

• Standardization of tasks as a foundation for automation – expert tasks and analysis need standardization with concrete steps, requirements, inputs, outputs, and automatic result evaluation to promote consistency and ease of analysis.

• Automation of analysis – Another critical infrastructure component is making advanced simulation tools more user-friendly and widely accessible. This could involve leveraging cloud-based simulation platforms and intuitive interfaces or applications (sometimes called simulation “apps”) that embed expert knowledge. For example, a specialized analysis can be pre-packaged by experts into a guided workflow, enabling less-experienced engineers to run sophisticated simulations by following prompts rather than building models from scratch . This democratization of simulation technology ensures that engineers without deep simulation expertise can still benefit from it, effectively expanding in-house capabilities and reducing the bottleneck on specialists.

• Roles and collaboration models - organizations might consider new roles and collaboration models to support this way of working. A “simulation facilitator” or engineering IT specialist team, for example, can maintain the simulation infrastructure and develop simplified models for others to use. Meanwhile, cross-functional teams—bringing together design, analysis, manufacturing, and even field service perspectives—can collaborate using shared digital models. Regular design reviews can be augmented with live simulation demos, where any team member can pose a hypothesis and test it in real-time. This fluid, interactive style of working breaks down silos between departments. Over time, the distinction between “the person who designs” and “the person who analyzes” blurs, as all team members contribute to both aspects at appropriate levels.

• Product development Workflow - Standard operating procedures should be updated to reflect the new approach (for example, requiring a simulation-driven check at each design milestone that can be performed by the design engineer). Training on these new processes is essential so that everyone understands how and when to utilize the digital tools at their disposal. In essence, democratizing knowledge goes hand-in-hand with democratizing decision-making in daily engineering work: empowering individuals at all levels to make informed technical decisions based on data and simulations, rather than always deferring to a small group of experts.

Cultural and Organizational Considerations

Even the best tools and processes will fall flat without the right organizational culture to support them. Democratizing knowledge in an engineering context represents a significant cultural shift for many companies.

Continuous learning Culture - Traditionally, specialized knowledge (like simulation expertise or deep domain know-how) has been the domain of a few, and others relied on them for critical insights. Transitioning to a democratized model requires overcoming the “expert-centric” mindset and fostering a culture of continuous learning and sharing.

Leadership - Leadership support for democratization initiatives, recognizing broader access to technology may challenge traditional hierarchies. Yet, It should be clear that the goal is not to replace experts, but to allow their knowledge to reach further. In fact, experts may take on new roles as mentors or reviewers, guiding less experienced engineers and validating results, rather than being the sole producers of analyses. Recognizing and rewarding such mentorship and knowledge-sharing behaviors can reinforce the desired culture. For example, performance reviews and incentives can include goals related to teaching others or implementing process improvements via new tools.

Training and skill development - Organizations should invest in training and skill development to build confidence among engineers to use new digital tools. This might include formal courses, peer learning sessions, or hands-on workshops with digital twin platforms. Creating internal communities of practice (e.g., a user group for simulation enthusiasts or a knowledge-sharing forum) encourages people to ask questions and share tips.

Governance frameworks - should be designed not only to ensure accurate interpretation of simulation results but also to foster psychological safety. Clear guidelines combined with a supportive management approach encourage team members to confidently run experiments and learn from mistakes without fear of blame. By embracing a culture of “fail fast, learn faster,” organizations can leverage simulations and digital twins as safe environments for rapid iteration, experimentation, and continuous improvement.r.

Benefits of Knowledge Democratization

Democratizing engineering knowledge through digital twins and simulation unlocks a multitude of benefits. Some of the most significant advantages include:

• Faster Innovation Cycles: When more team members can directly contribute with analysis and insights, decisions can be made faster. Engineers no longer need to wait in queue for a specialist’s input; they can iterate designs and validate ideas on their own. This accelerates development timelines and allows more design iterations in the same amount of time, ultimately leading to more refined and innovative products.

• Improved Design Quality: Broader participation in simulation means more opportunities to catch potential issues early. By simulating various scenarios and extreme cases, teams can identify and address design flaws before they become costly problems. The result is a more robust product design, since it has been vetted by many eyes and through many virtual tests.

• Empowered and Engaged Teams: Engineers, designers, and decision makers who have access to powerful simulation and modeling tools tend to have more ownership of the product. They can test their own ideas without always needing approval or help from an expert, which boosts their engagement and confidence. Over time, this builds a workforce with stronger multi-disciplinary skills and a deeper understanding of the product as a whole.

• Reduced Bottlenecks and Dependencies: Knowledge democratization helps smooth out bottlenecks caused by over-reliance on a few experts. If an analysis task can only be performed by one person or department, projects are vulnerable to delays. Spreading knowledge and tools means work can continue even if a particular expert is unavailable. It also reduces single points of failure in the organization’s knowledge base; the departure of an expert doesn’t cripple a project because their know-how has been transferred into processes and tools used by others.

• Seamless Collaboration: With a common digital platform, collaboration between different disciplines becomes easier and more effective. Everyone is looking at the same up-to-date information. Miscommunications decrease as the digital twin provides a shared “source of truth” about the product. Teams can work together in real time, with design, simulation, and even manufacturing considerations, evaluated together rather than in isolation.

• Cost Savings: Although setting up the infrastructure for democratized knowledge requires investment, it can lead to cost savings in the long run. Fewer physical prototypes may be needed because more testing is done virtually up front. Issues caught early via simulation save the huge expenses of late-stage design changes or warranty failures in the field. Additionally, by leveraging in-house talent more broadly, companies can reduce the need for external consultants or expensive niche hires for every project, as the internal team becomes more versatile.

Ultimately, the democratization of technical knowledge creates a more agile and competitive organization. Products can reach the market faster with higher quality, and the engineering team becomes more adaptable to new challenges. People are empowered to contribute at their highest capacity, and the organization benefits from the collective intelligence of the group rather than just the input of a select few.

Challenges of Knowledge Democratization

While the benefits are compelling, democratizing knowledge have challenges. It’s important for leaders to be aware of potential challenges and plan for them:

• Maintaining Quality and Accuracy: Opening up simulation and design tasks to a wider group means some users will have less expertise. There is a risk of misusing tools or misinterpreting simulation results, which could lead to design errors if unchecked. Organizations must implement proper validation and review practices. For example, having an expert double-check critical simulations or setting up automated result checking to maintain high quality standards as more people contribute.

• Training and Skill Gaps: Not every engineer initially has the skills to effectively use advanced simulation or interpret complex data. A significant investment in training and continuous learning is required. This takes time and resources, and during the learning curve, projects could even slow down. Companies need to be patient and provide support as their workforce upskills, which might include mentoring programs or partnering novices with experienced analysts until they gain confidence.

• Cultural Resistance: Company culture might resist change. Some experts may feel their authority or job security threatened and thus be reluctant to share knowledge or tools. On the other side, some engineers might be uncomfortable stepping outside their traditional roles to take on analysis tasks, fearing mistakes. Overcoming this requires strong change management – clearly communicating the vision, addressing concerns openly, and demonstrating that democratization will benefit both the individuals and the business. Early wins should be celebrated to show the value of the new approach and build support.

• Tool and Process Integration: Simply deploying new software or digital twin systems doesn’t guarantee people will use them effectively. If the tools are not well integrated into everyday workflows, they can end up underutilized. Also, technical issues like software compatibility, data interoperability, and user access can pose challenges. It’s critical to have IT and engineering process leaders work closely to ensure the tools mesh with existing systems (such as PLM software) and that using them is as frictionless as possible for engineers. The goal should be that utilizing the digital thread and simulation tools is the path of least resistance for getting work done, not an extra hassle.

• Data Management and Security: As more people generate and use data from simulations and tests, the volume of data will increase dramatically. Organizations must have strong data management practices to ensure that information remains organized, version-controlled, and accessible to those who need it (while protected from those who shouldn’t have access). There’s also the challenge of protecting intellectual property – when more employees have deep access to models and simulations, companies need to ensure that security protocols are in place to prevent any unauthorized sharing of sensitive design data.

• Measuring Impact: It can be difficult to quantify the benefits of knowledge democratization in the short term, making it harder to justify the investment and effort initially. While over time faster cycles and better products should show clear value, leaders might face skepticism early on. Establishing metrics (such as reduction in development cycle time, number of simulations run per engineer, or decrease in hand-off delays) and tracking them can help demonstrate progress.

Conclusion

The push toward democratizing technical knowledge in engineering product development is more than a passing trend – it is rapidly becoming a cornerstone of competitive strategy for innovation-driven organizations. By leveraging digital twins and advanced simulation tools, companies can break free from the old paradigm of isolated experts and slow, sequential workflows. Instead, they can create a future where insight and creativity flow freely across the organization, unconstrained by traditional role boundaries.

In this new way of working, an engineer with an idea can almost immediately test it in a virtual environment, a designer can validate a concept’s feasibility in real time, and a field technician can feed live data into a digital twin to improve the next generation of the product. The collective intelligence of the organization, amplified by accessible technology, drives faster learning and better decision-making at every level. This not only accelerates product development, but also fosters innovation that might not emerge in a more siloed setting.

To realize this vision, leaders must act with intent. Building the right infrastructure, nurturing the appropriate culture, and guiding the change in work practices require investment and perseverance. The transition may be challenging, but it prepares the organization to navigate the increasing complexity of modern engineering.

Ultimately, the power of knowledge democratization lies in its ability to unlock human potential through technology. When every engineer is equipped and empowered to contribute their best ideas and analyses, the organization as a whole becomes more agile, inventive, and resilient. Companies that leverage digital twins and simulation to spread knowledge and skills will not only develop superior products, but also cultivate a workforce that’s continually evolving.