Why Design optimisation Matters

In a previous article, we explored how Multidisciplinary Design optimisation has pushed the boundaries of engineering design across industries such as aerospace and energy production this year alone. The potential of design optimisation to significantly improve performance, reduce costs, and set products apart from competitors is immense.

However, many organisations still view optimisation as a final fine-tuning step or treat it as an independent study. This approach often leads to missed opportunities for quality innovation. The real challenge lies in integrating optimisation seamlessly into the system development workflow, making it a sustainable, iterative process rather than a one-off task.

In this article, I’ll walk you through a step-by-step guide on how to run a successful optimisation project — from planning and execution to long-term integration — ensuring your organisation reaps the full benefits of this powerful technique.

Step 1: Clarify and Align Your Objectives

Understanding your objectives is the foundation of any successful optimisation project. Objectives often vary depending on the level of abstraction you’re working at. Take aircraft design as an example:

• At the component level, such as optimizing the wings, your objective might be to maximise the lift-to-drag ratio.
• Zoom out to the aircraft level, and the focus shifts to a trade-off between payload and range, requiring a multi-objective optimisation approach.
• Zoom out further to the product as a whole, and the challenge could be determining which optimal design best meets customer needs and maximises revenue.

Recognising that design optimisation can occur at multiple levels and understanding how these levels interact are crucial. An effective strategy is to integrate optimisation efforts across levels to ensure alignment with the ultimate goal, whether that’s performance, cost efficiency, or customer satisfaction.

Step 2: Select the Appropriate optimisation Strategies

One of the most common pitfalls in optimisation projects is applying an algorithm simply because it’s the latest trend or state-of-the-art. This approach often overlooks the unique characteristics of the design problem at hand, leading to suboptimal results or inefficiencies. Instead, before diving into specific algorithms, take the time thoroughly to explore your design and solution space and understand the mathematical model you’re working with.

After narrowing down the feasible optimisation algorithms and solvers based on your problem’s characteristics, you can then choose the most appropriate approach by balancing other factors such as computational cost and required accuracy. If the problem is smooth and continuous, gradient-based methods offer efficiency, though they require accurate derivatives. For non-smooth, noisy, or discrete problems, gradient-free methods are appropriate, though they may converge slower. Choosing the algorithm based on your problem’s characteristics ensures a more efficient and effective optimisation process.

Techniques such as surrogate modelling can also be employed for computationally costly function evaluations. Understanding areas of your model that may benefit from an improvement in speed at the cost of accuracy using surrogates can greatly enhance the overall efficiency of the optimisation process. Additionally, uncertainty-based optimisation methods account for real-world variability, ensuring your solution remains robust and reliable under stochastic conditions.

Want more tips on the execution of design optimisation? Check out our previous article here.

Step 3: Integrate Design optimisation into System Engineering

Finally, the secret recipe for a successful and sustainable design optimisation lies in its seamless integration into the broader system development workflow. In many engineering organisations, optimisation is often handled by specialists or siloed teams, limiting its full potential. Instead, optimisation should be an integral practice adopted across all levels of the organisation, from component engineers to chief architects and executives, ensuring a shared commitment to continuous improvement.

Incorporating Model-Based Systems Engineering can significantly enhance this integration. By embedding design optimisation processes into system engineering activities such as conceptual design, validation and verification, and trade studies, organisations can consistently develop optimal products that meet evolving customer needs and requirements. MBSE enables optimisation to be a fluid part of decision-making, helping to ensure that top-level objectives are always aligned with design refinements at every stage of development.

Moreover, system models can help orchestrate optimisation across subsystems, even when specialised tools are used for different components. Leveraging visualisation tools, such as business intelligence (BI) platforms, can further bridge the gap between technical teams and business stakeholders. This fosters better communication and decision-making, ensuring that optimisation not only improves technical performance but also supports strategic business objectives.

At OptimiSE, we specialise in helping organisations develop a SE-oriented design optimisation workflow and upskilling their teams to ensure long-term success. If this resonates with your needs, book our free initial consultation today to find out more!

Final Thoughts

Successfully integrating design optimisation into system engineering is not just about applying the right algorithms or specialised software — it’s about building a culture that embraces continuous improvement at every level. From understanding your objectives to selecting the appropriate strategies and embedding optimisation practices into your workflow, each step plays a critical role in creating performant, innovative systems that meet the demands of both today and the future. When optimisation is viewed as a key component of the system development process rather than a separate task, its potential to drive innovation and competitive edge is fully unlocked.

Incorporating design optimisation into the broader system engineering process, particularly through Model-Based Systems Engineering, ensures that optimisation efforts are aligned with overall business objectives. This holistic approach allows organisations to address complex challenges more effectively, adapt to changing requirements, and ultimately deliver superior products that exceed performance expectations. By adopting these best practices, your organisation will be well-positioned to not only optimize designs but also to continuously evolve and lead in an ever-competitive landscape.