Discussions of how to bring simulation to bear starting in the early stages of product development have become commonplace today. Driving these discussions, we believe, is growing recognition that engineering design in general, and conceptual and preliminary engineering in particular, face unprecedented pressures to move beyond the intuition-based, guess-and-correct methods that have long dominated product development practices in discrete manufacturing. To continue meeting their enterprises’ strategic business imperatives, engineering organizations must move more deeply into applying all the capabilities for systematic, rational, rapid design development, exploration and optimization available from today’s simulation software technologies.
Unfortunately, discussions of how to simulate early still fixate all too often on 3D CAE methods such as finite element analysis and computational fluid dynamics. This reveals a widespread dearth of awareness and understanding—compounded, in our view, by some fear, intimidation and avoidance—of system-level physical modeling and simulation software. This technology empowers engineers and engineering teams to begin studying, exploring and optimizing designs in the beginning stages of projects—when product geometry is seldom available for 3D CAE, but when informed engineering decision-making can have its strongest impact and leverage on product development outcomes. Then, properly applied, systems modeling tools can help engineering teams maintain visibility and control at the subsystems, systems and whole-product levels as the design evolves through development, integration, optimization and validation.
The white paper from which our last three Research Briefs were excerpted—(1) System-level physical modeling and simulation: Accelerating the move to simulation-led, systems-driven engineering; (2) Adoption constraints; (3) Potential adoption accelerators—is part of our ongoing research and reporting intended to help remedy the low awareness and substantial under-utilization of system-level physical modeling software in too many manufacturing industries today. The project that resulted in this white paper originated during a technology briefing we received in late 2015 from Maplesoft, the Waterloo, Ontario-based developer of the MapleSim system-level modeling and simulation toolset and the Maple mathematical modeling and computation software. The company had noticed our commentary in industry and trade publications expressing the views set out above, and approached us to explore what they saw as shared perspectives.
From these discussions, we proposed that Maplesoft commission us to further investigate these issues through primary research among expert practitioners and engineering management, with emphasis on the off-highway equipment and mining machinery industries. In this research, focused not on software-brand-specific factors but instead on industry-wide issues, we interviewed users of a broad range of systems modeling software products including Dassault Systèmes’ Dymola, Maplesoft’s MapleSim, The MathWorks’ Simulink, Siemens PLM’s LMS Imagine.Lab Amesim, and the Modelica tools and libraries from various providers. Interviewees were drawn from manufacturers of off-highway equipment and mining machinery as well as some makers of materials handling machinery.
At the outset, we worked with Maplesoft to define the project methodology. Ora Research then executed the interviews, analyzed the findings and developed our white paper independently of input from Maplesoft. That said, we believe the findings of this project strongly support and validate Maplesoft’s vision and strategy for what it calls model-driven innovation, details of which can be found on the company’s web site.