Design space exploration: Justifying the investment

The foundational business value of design space exploration is the ability it confers on engineering teams and organizations to gain more complete, higher-fidelity visibility into product performance earlier in project schedules than was possible or practicable with older technologies and approaches. In essence, it does this by enabling more efficient, effective and revealing application of simulation, analysis and digital prototyping assets—tools, expertise, methods, work processes—to the perennial business drivers for any organization’s investments in those assets:

  • To become more competitive by gaining increased capability to explore, create and innovate.
  • To apply that capability to create better performing products.
  • To improve product quality and reliability—yielding expanded opportunity and customer appeal at the same time as lowered warranty expenses, liability exposure and lifecycle costs.
  • To control or, better yet, reduce product development schedules and budgets by supplanting costly, time-intensive physical testing with digital prototyping.


For practitioners working to justify investments in design space exploration, there is of course a fundamental challenge built into this hierarchy of business drivers: the greater and more strategic the value, the more elusive can be the case to convince colleagues and management that it would not be realized without the new tools and methods, or would be much less. This is true both in prospect—justifying investment proposals—and in retrospect, working to document the sources of project success, or lack thereof.

Best practices for qualifying and quantifying payback and ROI of design space exploration need much more thorough investigation, something we are undertaking. In the meantime, these notes toward a justification framework seem to find support in a sampling of industry case studies:

Impact on performance and innovation

Impact on quality

  • Requirements adherence is improved—Rolls Royce: Aircraft engine robust design
  • Product reliability is increased—Rolls Royce: Aircraft engine robust design
  • Design functions as entry point to corporate Six Sigma program—Rolls Royce: Aircraft engine robust design
  • Automating engineering processes removes manual errors—AAM: Automotive driveline NVH analysis
  • Practical simulation of hundreds of variations establishes which is most robust—Freudenberg: Wind turbine bearing gasket robust design
  • Robust design techniques yield improved gasket design, less sensitive to processing and manufacturing imperfections and to variances in operating conditions—Freudenberg: Wind turbine bearing gasket robust design
  • Optimization of liner hanger design meets increased hanging capacity and reliability prediction—Baker Hughes: DOE-based optimization and reliability prediction of an expandable liner hanger
  • Ability to take account of size and shape variation and uncertainty is improved in evaluation and optimization of weatherstrip seal performance—Cooper Standard: Optimization of automotive weatherstrip seal
  • Traditional optimization techniques tended to over-optimize, producing solutions that performed well at the design point but showed different results under off-design conditions; new robust-design algorithms solve this by allowing one variable and three constants to be defined as stochastic—BMW: Optimization of fatigue life of diesel engine crankcase and gasket
  • Using RSM approach to run virtual robust optimization with thousands of computations, then running ABAQUS to validate the virtual results, improves fatigue safety factor by 15% while constraining variation of measured output to less than 1%—BMW: Optimization of fatigue life of diesel engine crankcase and gasket

Impact on schedule

  • Traditional 4- to 12-week process to optimize ride/handling cut to 1-2 weeks—Pratt & Miller: Defense ground vehicle engineering
  • Cycle times are reduced—Rolls Royce: Aircraft engine robust design
  • Redesign is reduced—Rolls Royce: Aircraft engine robust design
  • Time to conduct each driveline NVH analysis iteration is reduced by 75%—AAM: Automotive driveline NVH analysis
  • Run-time for each analysis iteration cut from 30 minutes to 0.1 second; total analysis computational time cut from 16,300 hours to 100 hours—Freudenberg: Wind turbine bearing gasket robust design
  • Design of automotive structures is improved through automating the iterative information exchange between FEA and design activities, shortening development cycles and reducing manual labor—Van-Rob Kirchhoff Automotive: Optimization of a family of supporting frames under multiple analysis constraints

Impact on cost

  • Product development costs are reduced—Rolls Royce: Aircraft engine robust design
  • Control over manufacturing costs is tightened—Rolls Royce: Aircraft engine robust design
  • Annual six-figure savings in engineering labor costs per site are realized—AAM: Automotive driveline NVH analysis
  • New approach to auto body aerodynamic optimization based on RSM (response surface models) yields lower CFD calculation costs—Honda: Automotive aerodynamic design exploration

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