Simulation Lifecycle Management: Maximizing the Business Advantages

Simulation can replace or augment costly, time-consuming physical prototyping and testing.

The use of engineering and scientific simulation techniques to understand and predict the real-world behavior of physical phenomena is widespread across a number of diverse industries. This is true for simulations involving product performance attributes, manufacturing processes, or fundamental research. The benefits that result from the use of simulation are around us every day, from the vehicles we travel in, to products we use daily in work or play, to medical devices that provide treatments seemingly impossible only a few years ago. These techniques, when used effectively, provide a distinct business advantage to product manufacturers if they replace or augment costly, time-consuming physical prototyping and testing.

The Effectiveness of Simulation

Historically, the disconnected and isolated nature of simulation work -- which is due to the myriad tools, data, and processes used across the different domains and disciplines of simulation -- has resulted in tremendous inefficiencies as valuable knowledge is retained solely in the minds of the analysts. Collection and dissemination of information has been largely through manually-prepared reports, which are inherently inappropriate as a mode of managing critical information such as the relationship of simulation to product or process development.

For simulation to be truly effective as an integral part of the product development cycle, the processes, authoring tools, data, and resulting intellectual property associated with simulation must be shared, managed and secured as strategic business assets. A new solution category, simulation lifecycle management (SLM), is emerging to address these issues. The vision for SLM is bringing order to simulation processes and provides the technology to ensure simulation data integrity, traceability, knowledge capture, and collaboration. The end goal is to assist organizations in leveraging their simulation assets more effectively and bring a new level of efficiency in shortening development cycles, reducing waste, cutting costs, and improving product quality, while fostering a culture of collaboration and innovation.

It is only recently that some organizations have begun to focus on managing these assets and costs in a way that systematically optimizes the potential ROI. Previously, control and management of simulation IP was largely ignored, leading to significant negative consequences to effectiveness, efficiency and business benefit.

Since valuable IP is generated during the design, deployment, and execution of simulation methods, typical instances in which this information is ignored include:

  • Lost Data (such as material properties or results)
    Problem: Simulation data is lost or confused with similar results
    Symptom: Wrong decisions are made or simulations are repeated resulting in significant costs and time delays
  • Non-Standard Methods (such as modeling technique or software chosen)
    Problem: Individuals design and execute their own preferred methods
    Symptom: Results have questionable absolute value, lack repeatability, and cannot be reused reliably
  • Communication Problems (between people, disciplines, or companies)
    Problem: No framework or language for passing information or collaborating in a simulation context
    Symptom: Inefficient and error-prone decision making

However, data, processes, and knowledge captured within simulation and simulation methods provide a significant competitive advantage -- especially in situations where simulation competency is high but process competency is yet to be formulated and captured. To capitalize on this advantage and make direct impact on enterprise growth and profitability, simulation assets must be effectively deployed, adopted, retained, protected, and reused.

Defining SLM: Best Practices

Simulation's basic purpose is to support and enrich decisions about product design or process development while reducing or eliminating build-and-break cycles. Simulation Lifecycle Management is best defined as management of the intellectual property associated with simulation tools, data, and processes as related to product or process development. An SLM system must encompass four essential functional areas to be effective:

  • Collaboration
  • Simulation Data Management
  • Integration and Process Automation
  • Decision Support


Collaboration is SLM's foundation. No process or product is created in isolation; in fact, most often they are created by teams and individuals in different facilities scattered over different time zones. Each person involved must be able to compare their results to work out differences before the late phases of product development, when the heavy money starts to hit the table. Cross-functional collaboration also promotes innovation and higher quality by giving everyone in the development process insight into the latest intellectual property and helps them keep abreast of others' work that impacts their own.

For example, cell phone manufacturers need to incorporate multiple types of testing into their product design process. Technology advancements have made cell phone development more complex: the devices now regularly perform as cameras, media players, and personal digital assistants as well as phones. In addition, cell phones are expected to work reliably in extreme environmental conditions for long periods between battery charging, and also be convenient and aesthetically pleasing to consumers. To meet their many requirements, manufacturers must perform a variety of simulations across multiple departments, and all of these groups need to be in direct communication.

Virtual drop and vibration testing is used regularly in the cell phone design process, using technology that is proximal to that used in automotive crashworthiness and NVH. This enables design engineers to analyze stress and strain on components as the phone strikes a surface from various directions, allowing them to evaluate the stiffness of the device's components against performance requirements.

A cell phone's ability to manage and channel thermal energy is another significant consideration. Thermal simulation plays a critical role in influencing the design of the phone casing and electronics placement, helping to guide design decisions and balance physical requirements with consumer wants -- such as having smaller, lighter models.

Electromagnetic radiation simulation is also regularly employed in cell phone design. The device must emit energy at levels high enough to reach antenna towers kilometers away, yet conform to regulations regarding the potential health risks associated with absorption of this energy by the human head. Manufacturers rely on simulation to help them maintain this important balance of functionality and safety.

Simulation Lifecycle Management can help cell phone manufacturers support collaboration among their key stakeholders so that innovative devices satisfying wide-ranging performance requirements can be developed quickly and efficiently.

Simulation Data Management

Management of simulation data imposes the kind of discipline on simulation that has proven effective in other facets of product development, such as computer-aided design and bill of materials. Central data repositories provide a consistent, safe, and searchable environment for valuable data. A coherent data management structure gives engineers and designers quick access to existing intellectual property that can help them with their current work. It also supports the definition of best simulation practices through automated workflows that ensure data integrity and repeatable processes while eliminating wasteful labor.

Integration and Process Automation

Integration of a diverse set of simulation applications and subsequent process automation creates a unified simulation environment that facilitates toggling between various systems to complete a task. Essential, best-in-breed simulation capabilities from relevant outside resources must be integrated with simulation data and processes. Once all of the necessary resources exist in the same environment, simulation experts can link them together in automated workflows. Automating key processes adds accuracy and repeatability to simulation, which in turn improves quality and reduces time to market. It also allows the simulation community to expand to design engineers, freeing up the time of simulation experts to develop the next generation of robust simulation methods.

Decision Support

Providing capabilities for decision support is about ensuring a product will meet its functional requirements, possess optimal cost, weight and durability; and be designed, manufactured and iterated in the least possible amount of time. To support these ends, SLM solutions must provide management tools for hitting design targets -- in other words, making sure designs work as intended -- and anticipating post-production performance. Those capabilities enable engineers to peer into the future, anticipate problems, and make the appropriate decisions early in the design process to ward off late-stage problems.

Key to SLM Success

Ultimately, the key element for SLM success will be the strength of conviction held within organizations that see simulation as an important, yet isolated component of their operation -- and for them to recognize, take control of and leverage the significant IP associated with simulation know-how, process, data, and resulting decisions. The role of an SLM solution is to make this approach to realistic simulation a common and natural occurrence in the overall development process.

Companies that embrace SLM will position themselves as leaders in the future and stand out in this highly competitive environment. Their ability to create virtual worlds that closely mirror the physical world will enable them to optimize performance, reduce material use, and detect and correct errors at a level almost inconceivable with today's physical prototyping.

Paul Lalor is pa roduct manager for SIMULIA brand of Dassault Systemes. For more information see

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