The Untapped Potential of MDO in Industry

Multidisciplinary Design Optimisation (MDO) is a transformative methodology that empowers engineers to rigorously and mathematically optimise complex systems. By addressing multiple disciplines simultaneously, MDO identifies trade-offs and achieves optimal designs that balance performance, cost, weight, and other critical factors. This holistic approach is crucial for developing cutting-edge systems that excel across diverse metrics.

Evidence of MDO’s impact can be seen in sectors such as aerospace, automotive, and renewable energy, where it has led to lighter, more efficient designs and better resource utilisation. In today’s competitive landscape, where systems are growing increasingly complex and interconnected, the need for MDO has become more pressing than ever.

Yet, MDO remains largely confined to academic research and niche applications, struggling to gain traction in broader industrial use. Many organisations perceive it as resource-intensive, overly complex, or difficult to integrate into existing workflows. These misconceptions ofter deter adoption, causing industries to miss out on the innovations that MDO could unlock — solutions that have the potential to reshape markets, cut costs, and maximise system performance.

But this doesn’t have to be the case! In this article, we’ll explore five common misconceptions that hold organisations back from adopting MDO in their complex system development processes. More importantly, I’ll show how these challenges can be addressed, demonstrating that MDO is not only practical but also accessible for organisations ready to embrace its transformative power.

The Five Common Misconceptions of Implementing MDO in Industry

Having worked extensively on introducing MDO in different engineering projects, I’ve encountered a recurring set of misconceptions that often stand in the way of its adoption. These aren’t abstract ideas or theoretical barriers — they are real concerns voiced by engineers, managers, and stakeholders who are navigating the complexities of integrating MDO into their workflows.

Each misconception reflects a specific challenge I’ve faced when helping organisations transition from traditional design approaches to an MDO-driven methodology. Whether you are just starting your journey with MDO or looking for ways to overcome hurdles in adoption, I hope these insights can help you navigate the process with greater confidence and clarity.

Misconception 1: MDO Happens Separately from the System Development Cycle

A common misconception is that MDO is a standalone process performed at the end of system development — a kind of “icing on the cake” to fine-tune an already designed system. This perception limits the impact MDO can have, as it is often applied too late to influence foundational design decisions. When treated as an afterthought, MDO can only optimise within the constraints of a pre-established design, yielding minimal overall improvements. To truly harness the power of MDO, it must be integrated into the development cycle from the very beginning.

For MDO to have maximum impact, it must be deeply integrated into the Systems Engineering process. Better still, pairing MDO with Model-Based Systems Engineering (MBSE) creates a unified framework where optimization can be automated and continuously applied throughout the system’s lifecycle. When MBSE models are combined with MDO, any iteration or change to the system can trigger automated optimisation, ensuring the design remains optimal at every stage. This holistic, integrated approach enables organisations to achieve far greater efficiency and innovation in their system development processes.

Misconception 2: MDO Cannot Solve Problems With Complex Objectives

Another common misconception is that MDO cannot be applied when objectives are complex, conflicting, or difficult to define. While it’s true that MDO will not be able to provide meaningful results when objectives are unclear or poorly defined, this issue is not a limitation of MDO itself — it’s a challenge with the system development process as a whole. Objectives are often scattered across different levels, such as a business focus on revenue at the organisational level versus performance and efficiency concerns at the product level. Without a clear alignment or understanding of these objectives, it becomes nearly impossible for any optimisation process, including MDO, to yield actionable results.

Rather than viewing unclear objectives as a roadblock to MDO, organisations can use MDO as an opportunity to formalise and prioritise their objectives. By establishing clear, structured objectives across teams and levels, MDO becomes a tool to balance these objectives strategically. For example, MDO can help optimize at the product level by fine-tuning specific performance metrics, then roll these insights into higher-level trade-offs that align with broader business goals. This structured approach not only enhances the effectiveness of MDO but also improves the overall coherence of the system development process, ensuring that both technical and business needs are met in an optimized, integrated design.

Misconception 3: MDO Cannot Be Performed Across Different Software Platforms

Very often, organisations mistakenly believe that implementing MDO requires all teams to operate within a single specialised software platform. This misconception is particularly prevalent in organisations developing complex systems, where different disciplines rely on specialised tools tailored to their unique needs. In fact, you don’t need a specialised proprietary MDO software, nor do you need to make all teams use the same platform like MATLAB or Python to successfully perform MDO.

In reality, MDO can be implemented in a distributed architecture, enabling domain models to be analysed within their most suitable specialised computational environments. The key lies in establishing a clear and consistent data transfer protocol between these environments to ensure accuracy and integrity throughout the optimisation process.

This microservice approach fosters autonomy by empowering individual teams to retain authority over their specialised tools and workflows, ensuring that they can operate autonomously within their domains of expertise. At the same time, it facilitates seamless collaboration by integrating these specialised components into a cohesive optimisation framework.

Misconception 4: MDO Is Too Computationally Intensive to Be Practical

It’s true that MDO can sometimes be computationally expensive, especially for complex systems with high-dimensional design spaces or when detailed, physics-based simulations are involved. However, this does not mean MDO is impractical. Techniques such as surrogate modelling offer powerful solutions to significantly reduce computational demands while maintaining sufficient optimisation accuracy. These methods enable organisations to perform MDO efficiently, even when resources are limited or system complexity is high.

Surrogate-based optimisation works by replacing computationally expensive simulations with lightweight mathematical approximations that mimic the behaviour of the original models. These surrogates — created using techniques like polynomial regression, Kriging, or neural networks — can be trained on a small set of simulation or experimental data and then strategically refined and infilled in critical regions of interest. By using surrogates, optimisation can be performed iteratively and quickly, enabling faster convergence to optimal solutions with significantly reduced computational effort.

Additionally, the choice of optimization algorithm plays a critical role in managing computational intensity. Depending on the project’s needs, the granularity of the analysis, and the maturity stage of the system, you can strategically select algorithms that balance cost and accuracy. For example, heuristic methods like genetic algorithms may be used for early-stage exploration, while gradient-based methods can refine solutions in later stages. By leveraging these techniques and strategies, organisations can overcome computational challenges and unlock the full potential of MDO.

Misconception 5: MDO Does Not Address Risks or Uncertainties

Organisations developing systems that must operate across diverse conditions or include design variables with inherent variability may question whether MDO can effectively address such complexities. This doubt often leads to concerns about the practicality and reliability of the optimal designs produced through MDO, undermining confidence in its outcomes and reducing organisational willingness to adopt the methodology.

In fact, Optimisation Under Uncertainty (OUU) is a well-established and widely applied extension of mathematical optimisation, designed specifically to handle risks and uncertainties. OUU works by properly propagating the uncertainties from the input parameters or design variables to the objectives and constraints, then re-formulating the optimization problem to account for these uncertainties. Unlike traditional approaches, OUU offers a more sophisticated and informed approach to managing uncertainties and quantifying their probabilistic impact on the design. The result is not only an optimal solution, but one that is also robust and reliable.

Final Thoughts

Multidisciplinary Design Optimization has the potential to revolutionise how complex systems are designed, delivering optimal, efficient, and innovative solutions that balance performance across multiple disciplines. However, misconceptions like those outlined in this article often stand in the way of adoption. By addressing these misunderstandings, organisations can fully unlock the transformative potential and value that MDO has to offer.

For organisations looking to adopt MDO effectively, expertise and tailored strategies can make a significant difference. Whether it’s aligning MDO with your systems engineering process, establishing the necessary infrastructure to perform MDO, or upskilling your teams with effective MDO techniques, OptimiSE is here to support you. If you’re ready to elevate your system design capabilities, consider booking a free consultation call with us to explore how we can help!

¹ Pedersen, M. M., & Larsen, G. C. (2020). Integrated wind farm layout and control optimization. Wind Energy Science, 5(4), 1551–1566. https://doi.org/10.5194/wes-5-1551-2020