Simulation Traceability: Holistic Visibility for Product R&D and Digital Manufacturing

Today’s engineers navigate numerous decisions to develop a market-ready product. The journey from concept to commercialization involves the collaborative efforts of diverse teams, each with their own specialization from research to manufacturing. When teams collaborate closely in this process they can develop products that not only meets customer requirements and is also cost-effective and, above all, safe. As new products advance towards market entry, engineering leaders must monitor R&D milestones, ensure steady progress, and make critical decisions within tight deadlines. Such oversight involves considering various team inputs, including simulation tests, previous trade-off choices, and lessons learned from earlier simulations on similar projects.

Simulation Traceability: Harnessing Data for Improved Collaboration and Decision-Making

The rapid rise in the use of simulation by more R&D teams and the volume of data it produces has created a new opportunity to make better decisions at each stage of R&D. And when warranty or safety issues arise, it’s critical to trace what designs, materials, testing, and decisions went into it. In heavily regulated industries such as aerospace and healthcare, providing precise documentation on all specifications is a mandatory part of being certified to sell a product. Giving teams the ability to organize and find all their relevant data – seamlessly in their day-to-day workflow – creates more opportunities to discover new insights and find critical answers.

Rescale works with customers across a range of industries to effectively manage their simulation data with traceability throughout complex product R&D processes to reach regulatory milestones faster, build more competitive products, and improve overall collaboration. In partnering with engineers who need high confidence in making highly consequential decisions, we are helping them gain a clear picture of their designs down to the part and even down to the location where the raw materials were mined. 

Overcoming Growing Product Complexity and Competitive Pressure to Innovate 

An engineering.com study found that over the past five years, a significant majority (92%) of design and engineering professionals reported an increase in product complexity. This complexity has manifested in various forms, such as more intricate mechanical designs (57%), the incorporation of more electronics (47%), the need to adopt different materials (43%), and the integration of software, hardware, and electronics systems​​. Further, 76% of respondents indicated that their products have become more complex in three or more ways. This indicates a multi-dimensional increase in complexity, encompassing aspects like mechanical designs, electronics, materials, and software integration. 

At the same time, companies face the pressure to innovate rapidly to gain or maintain market share. With access to global markets and information, consumers expect new and improved products, increasing the pressure on companies to innovate quickly. 

Engineering and R&D teams have responded to increasing complexity and pressure to innovate with accelerated use of simulation and modeling in the design process. 

Solving Data Fragmentation and Disconnection in Engineering and Scientific Simulation 

Traditional engineering approaches often suffer from fragmentation and disconnection, posing significant challenges in data management and collaboration. These approaches are characterized by a fragmented structure where simulation data is stored in silos, making it difficult to access. Furthermore, these traditional methods often result in disconnected processes where simulation goals are either not captured or are captured separately from the actual work performed.

Such fragmentation and disconnection leads to what can be described as ‘Islands of Analysis’, where work is siloed, manual handoffs are prevalent, duplication of work occurs, and data is used ineffectively. Cross-functional teams struggle to gain a comprehensive view of all product efforts due to the lack of data access and context. Additionally, teams might use different simulation tools for investigating the same questions, further complicating data sharing and hindering efficient collaboration. This results in incomplete visibility where findings are shared via presentations and ad-hoc discussions on separate aspects of product decisions, rather than in a cohesive and integrated manner​​.

As a result, product decisions are made without taking advantage of enterprise simulation data, past or present. Micro decisions made by product engineers lack context. More often than not, errors creep into the product design at the “silo interfaces” where visibility is at its lowest, leading to flawed decisions that propagate through the management chain.

Shared Access and Enriched Context for Data-Driven Engineering Transformation

Therefore, data-driven engineering transformation has become an imperative, in the modern landscape of research and development (R&D). Data driven engineering necessitates shared access to and context for data.This approach enables organizations to effectively manage their engineering and simulation data, thereby positioning them to digitally transform their R&D processes. Such a transformation leads to improved R&D velocity, efficiency, and product quality. It empowers engineers by providing them with the information they need precisely when they need it.

In this environment of connected collaboration across various product and project contexts, all teams have access to shared insights, essential for innovation, regardless of the data’s location or storage method. This facilitates seamless collaboration, making quick decision-making far easier, faster, and more efficient. Teams can reference a single source of truth on all simulation activities which is accessible and contextualized, aiding in rapid decision-making. This is especially crucial as modern products increasingly require complex sets of requirements and interdisciplinary systems​​.

Supporting Model Based Design Collaboration, a Standard for Modern Engineering Teams

Model based design collaboration facilitates engineering data sharing and accelerates operations. At its core, model based collaboration involves the use of detailed, high-fidelity models that represent complete products or their components, along with their behaviors under specific conditions. They include logical models, detailed physics models, CAD models, process plan models, and comprehensive test models that simulate product behavior in real-world scenarios.

The strength of model-based collaboration lies in its ability to unify and automate data sharing across the product development lifecycle. Integrating various models in a cohesive manner, onto a single data platform ensures that data is accessible for all authorized users. Allowing users to comment on their simulation to leave context for the reader creates a basis for decision documentation that is traceable to the simulation model. This approach reduces the reliance on anecdotal and ad-hoc document sharing, streamlining the workflow and enhancing collaborative efficiency. Comprehensive metadata management on all simulation resources creates the basis for organizing data and providing traceability to both the model data and decisions arising from the modeling.

Rescale enables flexible tagging of Jobs, Workstations, and Files allowing for flexible enrichment and categorization of simulation data. Tagged metadata can be used to share context on computing activities across projects and teams.

Rescale enables sorting, filtering, and searching to find simulation resources across teams within the workspace, improving the decision time and quality of decisions

Make Better Product Decisions Faster with Rescale Metadata Management

Organizations with a holistic view of product data can make better decisions faster. Such an approach breaks down data silos, promoting cross-functional collaboration and comprehensive understanding of all product efforts. A unified data platform can combine various data sources, offering a single source of truth for all simulation activities across the organization. This centralization of data facilitates easier access and analysis, allowing teams to spend more time on data informed design tradeoff. Finally, integrating multidisciplinary simulation data broadens the analytical perspective, leading to more informed decision-making. 

Learn More About Simulation Traceability

Explore examples of simulation traceability on Rescale to help your data-driven teams to collaborate and make product decisions faster. Rescale Metadata Management capabilities like resource tagging capture important details about each simulation. To learn more request a custom demo from our team.

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