Home: Issue 1 2009 › Lead Story › At a crossroads?
At a crossroads?
25/03/2009 | Channel: Information Technology, Manufacturing
Paul Lalor discusses the critical elements for managing simulation data and processes
In the manufacturing industry, design simulation technology and methods are being used to create innovative and reliable products while reducing time and costs. However, even those companies gaining significant benefits from simulation will admit that they often fial to retain their processes or manage simulation results in a manner that allows effective knowledge capture and reuse.
A 2007 survey from industry research firm CPDA reported that only 6.1 per cent of companies were using any form of a simulation data management solution, which is typically designed to handle large, complex, and highly-specialised simulation data files. Thirty-five per cent were using legacy databases or data management applications, and the largest percentage of respondents, 42.7 per cent, were keeping their simulation data on local or departmental drives. A lack of processes and data management infrastructure makes it difficult - if not impossible - for other decision-making stakeholders to access important design simulation information or even be aware of its existence. This leads to repeating the same simulations - or worse, overlooking important design performance metrics. These types of issues have created a growing industry consensus that the data, processes, and tools associated with product design and manufacturing simulation must be better managed and secured.
the need to manage simulation data management from concept to manufacturing has given rise to a new industry term: Simulation Lifecycle Management (SLM). The goal of SLM is to provide robust simulation technology and methods that enable product development companies to bring order to their simulation processes and achieve a new level of efficiency in shortening development cycles, reducing waste, and improving product quality while fostering a culture of collaboration and innovation.
Engineering organisations that wish to incorporate SLM into their workflow face several tasks, which include evaluating their current practices, integrating various design and simulation applications, developing standardised simulation methods, and incorporating these processes and tools into a managed and collaborative environment.
A robust Simulation Lifecycle Management solution should deliver a core set of extensible capabilities, including:
Collaboration
Product designs involve trade-offs between multiple performance disciplines such as strength, weight, vibration, and durability. A robust solution must be able to support cross-functional collaboration among all key stakeholders so that innovative products that satisfy wide-ranging performance requirements can be developed quickly and efficiently.
Simulation Data Management
Simulation-related data must be able to be collected, secured, managed, and associated with related product data in a central repository that maintains the relationship between engineering targets and key simulation results and provides a searchable environment for ll related data. A best-in-class solution should provide the ability to trace the history of individual simulation processes, including parameters, assumptions, and results that influence key design decisions.
Integration and Process Automation
Complete product performance analysis requires the use of a broad spectrum of best-in-class and proprietry simulation applications. A valuable SLM solution should provide the capacity to connect these various tools in an open, yet controlled manner. In addition, as organisations' management of their simulation data and processes matures, the SLM solution must provide the resources to capture, automate, and deploy approved simulation workflows to a wider group of non-expert simulation users.
Decision Support
Simulation results are used to evaluate and comunicate the predicted physical performance attirbutes of design candidates and their suitability toward meeting engineering targets. SLM solutions should provide decision support mechanisms that enable cross-functional insight and guide requirements-driven design decisions.
The ongoing industry challenges of shorter product lifecycles, higher material costs, and stricter govenrment regulations ensure that simulation will play a growing role in the product design and manufacturing processes of the future. Those companies performing simulation on a regular basis find themselves at a crossroades. They can either allow simulation processes and results to stay disconnected and unmanaged, or they can explore the emerging Simulation Lifecycle Management solutions. Organisations that implement such solutions in the near-term will be well on their way to fully leveraging their simulation expertise and resulting knowledge as a valuable corporate asset and competitive advantage.
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Paul Lalor is product manager ar SIMULIA. SIMULIA is the Dassault Systemes brand that delivers a scalable protfolio of Realistic Simulation solutions including Abaqus product suite for Unified Finite Element Analysis, multiphysics solutions for insight into challenging engineering problems, and SIMULIA SLM for managing simulation data processes, and intellectual property.
A 2007 survey from industry research firm CPDA reported that only 6.1 per cent of companies were using any form of a simulation data management solution, which is typically designed to handle large, complex, and highly-specialised simulation data files. Thirty-five per cent were using legacy databases or data management applications, and the largest percentage of respondents, 42.7 per cent, were keeping their simulation data on local or departmental drives. A lack of processes and data management infrastructure makes it difficult - if not impossible - for other decision-making stakeholders to access important design simulation information or even be aware of its existence. This leads to repeating the same simulations - or worse, overlooking important design performance metrics. These types of issues have created a growing industry consensus that the data, processes, and tools associated with product design and manufacturing simulation must be better managed and secured.
the need to manage simulation data management from concept to manufacturing has given rise to a new industry term: Simulation Lifecycle Management (SLM). The goal of SLM is to provide robust simulation technology and methods that enable product development companies to bring order to their simulation processes and achieve a new level of efficiency in shortening development cycles, reducing waste, and improving product quality while fostering a culture of collaboration and innovation.
Engineering organisations that wish to incorporate SLM into their workflow face several tasks, which include evaluating their current practices, integrating various design and simulation applications, developing standardised simulation methods, and incorporating these processes and tools into a managed and collaborative environment.
A robust Simulation Lifecycle Management solution should deliver a core set of extensible capabilities, including:
Collaboration
Product designs involve trade-offs between multiple performance disciplines such as strength, weight, vibration, and durability. A robust solution must be able to support cross-functional collaboration among all key stakeholders so that innovative products that satisfy wide-ranging performance requirements can be developed quickly and efficiently.
Simulation Data Management
Simulation-related data must be able to be collected, secured, managed, and associated with related product data in a central repository that maintains the relationship between engineering targets and key simulation results and provides a searchable environment for ll related data. A best-in-class solution should provide the ability to trace the history of individual simulation processes, including parameters, assumptions, and results that influence key design decisions.
Integration and Process Automation
Complete product performance analysis requires the use of a broad spectrum of best-in-class and proprietry simulation applications. A valuable SLM solution should provide the capacity to connect these various tools in an open, yet controlled manner. In addition, as organisations' management of their simulation data and processes matures, the SLM solution must provide the resources to capture, automate, and deploy approved simulation workflows to a wider group of non-expert simulation users.
Decision Support
Simulation results are used to evaluate and comunicate the predicted physical performance attirbutes of design candidates and their suitability toward meeting engineering targets. SLM solutions should provide decision support mechanisms that enable cross-functional insight and guide requirements-driven design decisions.
The ongoing industry challenges of shorter product lifecycles, higher material costs, and stricter govenrment regulations ensure that simulation will play a growing role in the product design and manufacturing processes of the future. Those companies performing simulation on a regular basis find themselves at a crossroades. They can either allow simulation processes and results to stay disconnected and unmanaged, or they can explore the emerging Simulation Lifecycle Management solutions. Organisations that implement such solutions in the near-term will be well on their way to fully leveraging their simulation expertise and resulting knowledge as a valuable corporate asset and competitive advantage.
')";>')";>Paul Lalor is product manager ar SIMULIA. SIMULIA is the Dassault Systemes brand that delivers a scalable protfolio of Realistic Simulation solutions including Abaqus product suite for Unified Finite Element Analysis, multiphysics solutions for insight into challenging engineering problems, and SIMULIA SLM for managing simulation data processes, and intellectual property.
