Better Hardware Design Decisions, Faster: A Lean Team’s Guide to MDO

• Hardware Development
• Optimization
• System Design

Designing hardware products today is more about navigating a tangled web of trade-offs—performance, power, thermal, mechanical, cost, software, compliance…Every decision creates ripple effects, and a single misstep in one dimension can derail progress across the board.

Multidisciplinary Design Optimization(MDO) reframes product development as a systems-level challenge, enabling teams to connect design decisions across domains, surface trade-offs early, and move faster — with fewer surprises.

While MDO is often associated with large-scale, complex systems, this article focuses on how lean teams can apply its principles in practical, lightweight ways to drive clarity, speed, and smarter decisions from day one.

What is MDO?

Multidisciplinary Design Optimization (MDO) is a methodology for solving complex product challenges by optimizing across multiple disciplines/domains — mechanical, electrical, thermal, software, cost, and beyond — simultaneously. It gives product teams a tool to anticipate the downstream impact of design decisions early, before they become costly problems, i.e., before the decision is implemented.

Unlike traditional design flows where each team works in a silo and passes their work downstream, MDO establishes a unified optimization framework that connects models — analytical, empirical, or simulation-based — and constraints from every discipline — so design decisions are grounded in the full system reality, not just a single team’s assumptions

It enables integrated trade-off studies and faster, smarter iterations, helping teams align early around key design drivers, reduce development risk, and accelerate time-to-market, without sacrificing performance or quality.

Why MDO?

If you’re working on anything remotely complex — an embedded device, a smart appliance, a connected sensor platform etc., — MDO helps:

1. Align Teams Around Data– Not Opinions

With MDO, you can visualize and quantify trade-offs between disciplines. Want to cut weight without tanking thermal performance? Want better battery life without exploding costs? MDO lets you simulate, tweak, and balance those decisions across the entire system.

2. You Avoid the “Throw-It-Over-The-Wall” Problem

Traditionally, each team works in its bubble. Mechanical hands off to electrical, electrical passes to firmware, and so on. If someone discovers a problem down the line, it loops back — rework. With MDO, those handoffs shrink, and issues surface early because all domains are optimized together. Essentially allowing you to;

• Catch conflicts early, before they explode during integration.
• Reduce iteration loops and the dreaded late-stage rework

3. Scalable decision making — from Concept to Production

During early-stage development, MDO helps explore the design space rapidly. During refinement, it supports fine-tuning with tighter constraints. And when you’re close to launch, it gives you confidence that the product you’re shipping is the best compromise across performance, cost, manufacturability, compliance, and other dimensions.

Why Now?

Traditionally, MDO has been reserved for industries where design complexity is inherently high — aerospace, automotive, or high-performance computing — projects with big budgets and highly specialized teams. But the landscape has changed:

• “Basic” products are now complex: 
  Your toaster might now send emails, and your vacuum now needs to map your room and perform obstacle avoidance. This explosion of features has blurred the line between “basic” and “complex,”such that irrespective of what you are building, you are facing multi-domain, multidisciplinary trade-offs (battery life vs performance, cost vs durability).
• Model-Based Development is going mainstream:
  Model development and coupling are becoming easier. Python libraries, Excel modeling, and tools like Simulink and Modelica are making it easier than ever to build and couple models across domains for small teams.
• Compute is cheaper and more available.
  Optimization runs that used to take hours now take seconds on a laptop — or can be parallelized in the cloud.
• AI is helping with surrogate models and predictive analytics: 
  With the rise of AI-driven modeling techniques (like surrogate models or machine learning regression), we can now create mid to high-fidelity models without waiting hours for CFD or FEA simulations.

MDO is no longer an exotic luxury. It’s a competitive advantage — and increasingly, a necessity- and small teams can leverage these advancements to build useful optimization workflows that lead to smarter design decisions.

Simple MDO Implementation Framework with a Case Study

Let’s walk through a simple approach to implementing MDO, step-by-step, using a real product example to define the guide:

Consider A consumer drone with the details provided below;

Design Goal: Design a lightweight, camera-equipped drone that can fly for 30+ minutes, shoot stabilized 4K video, meet sub-500g regulations, and retail for under $1,000.

This example will show how a small team — say, a startup or a hardware team inside a larger org — can do MDO without an army of analysts or advanced software licenses.

Step 1: Map the Disciplines

Start by identifying the core disciplines involved in your product, their key design variables, outputs, and constraints. Even if they are soft constraints, they matter; put them on the map.

Pro Tip: Create a simple table or mind map. This makes the system complexity visible and gives you a feel for the coupling between disciplines.

For our drone example, the stakeholder map/table is provided below:

Step 2: Create Parametric Models

Create parametric, reduced, or first-order models that reflect key relationships and tie design inputs to performance outputs.

Don’t forget they don’t need to be perfect (high fidelity) the first time — they just need to give you meaningful signals about trade-offs.

Models related to our drone example are provided below:

• Battery vs Weight vs Flight Time
  Use a basic physics model:

<span class="pre--content"><span class="hljs-title class_">FlightTime</span> ≈ (<span class="hljs-title class_">BatteryCapacity</span> × <span class="hljs-title class_">MotorEfficiency</span>) / (<span class="hljs-title class_">DroneWeight</span> × <span class="hljs-title class_">Gravity</span>)</span>

• Thermal Model
  Estimate steady-state junction temps using power dissipation and thermal resistance:

<span class="pre--content"><span class="hljs-attr">T_junction</span> = T_ambient + (Power × θJA)</span>

• Cost Model
  Sum component costs from your early BOM + manufacturing overhead:

<span class="pre--content"><span class="hljs-attr">TotalCost</span> = Σ(Component_i × UnitCost_i) + Assembly + Test + Packaging</span>

Your models can be implemented in Excel, Python, MATLAB or similar software programs, whatever lets you link inputs and output across different models used by the various disciplines.

Step 3: Define Your Objective & Constraints

Frame your actual optimization problem. What’s your design trying to optimize? Set clear objectives and define constraints from each discipline.

For our Drone example:

Objectives:

• Maximize flight time
• Maximize video quality (CPU/GPU throughput)
• Minimize total weight
• Minimize BOM cost

Constraints:

• Total weight < 500g
• Processor < 75°C at max load
• Battery must be < 100Wh (for air travel compliance)
• Passes EMI, drop, and RF range tests
• Retail price target = $999 max

Step 4: Build a System-Level Design Space

With your objectives and constraints clearly spelled out, build a spreadsheet or script where you can adjust inputs (motor size, battery type, frame material, processor spec) and instantly see how outputs shift.

For our example;

Link each parameter to the models from Step 2. Try various combinations and find the optimal outputs.

Sample implementation with Python is provided below;

<span class="pre--content"><span class="hljs-keyword">import</span> itertools
battery_range = [<span class="hljs-number">3000</span>, <span class="hljs-number">4000</span>, <span class="hljs-number">5000</span>]
motor_range = [<span class="hljs-number">900</span>, <span class="hljs-number">1200</span>, <span class="hljs-number">1600</span>]
frame_materials = [<span class="hljs-string">'plastic'</span>, <span class="hljs-string">'carbon_fiber'</span>]
processor_power = [<span class="hljs-number">4</span>, <span class="hljs-number">6</span>, <span class="hljs-number">8</span>] <span class="hljs-comment"># Watts</span>
combinations = <span class="hljs-built_in">list</span>(itertools.product(battery_range, motor_range, frame_materials, processor_power))
<span class="hljs-built_in">print</span>(<span class="hljs-string">f"Evaluating <span class="hljs-subst">{<span class="hljs-built_in">len</span>(combinations)}</span> combinations"</span>)</span>

Step 5: Explore the Trade Space

This doesn’t need to be rocket science. Depending on the problem, you can use some of the design exploration techniques described below to test different decisions or value combinations:

• Use gradient-based methods for continuous variables
• Try evolutionary algorithms (like genetic algorithms) for discrete, nonlinear problems
• Apply design of experiment methodologies (DOE) to visualize feasible regions
• Manual sweeps (e.g. pivot tables in Excel)
• Monte Carlo simulations with random inputs
• Pareto front analysis (Identify the non-dominated values on the Pareto front)

What you’re looking for: non-obvious trade-offs.

Examples of tradeoffs for our Drone project include:

• Going from a 4000mAh to 5000mAh battery only adds 4 extra minutes of flight time, but pushes weight over the legal limit.
• A higher-end processor gives smoother video but generates too much heat without active cooling — adding fans increases weight and cost.

A sample Python implementation for our example is provided below;

<span class="pre--content">import matplotlib.pyplot as plt
import numpy as np

<span class="hljs-comment"># Electrical - Power Model</span>
battery_capacity = 5000  <span class="hljs-comment"># mAh</span>
motor_current = 15  <span class="hljs-comment"># Amps</span>
efficiency = 0.85
flight_time = (battery_capacity * 3.7 / 1000) / (motor_current / efficiency)  <span class="hljs-comment"># hours</span>

<span class="hljs-comment"># Mechanical - Weight Model</span>
frame_weight = 150  <span class="hljs-comment"># grams</span>
motor_weight = 40 * 4  <span class="hljs-comment"># 4 motors</span>
total_weight = frame_weight + motor_weight + 150  <span class="hljs-comment"># battery and electronics</span>

<span class="hljs-comment"># Thermal - Processor Temp Model</span>
power_dissipation = 6  <span class="hljs-comment"># watts</span>
thermal_resistance = 10  <span class="hljs-comment"># °C/W</span>
ambient_temp = 25  <span class="hljs-comment"># °C</span>
t_junction = ambient_temp + power_dissipation * thermal_resistance

<span class="hljs-comment"># Business - Cost Model</span>
component_costs = {<span class="hljs-string">"frame"</span>: 20, <span class="hljs-string">"motors"</span>: 60, <span class="hljs-string">"battery"</span>: 25, <span class="hljs-string">"pcb"</span>: 30}
bom_cost = sum(component_costs.values())
retail_price = bom_cost * 2.5


battery_range = np.arange(2000, 6000, 250)
flight_times = []
weights = []
temps = []

for cap in battery_range:
    flight = (cap * 3.7 / 1000) / (motor_current / efficiency)
    weight = frame_weight + motor_weight + cap * 0.015
    temp = ambient_temp + (6 + (cap - 2000)/1000) * thermal_resistance

    flight_times.append(flight * 60)  <span class="hljs-comment"># convert to mins</span>
    weights.append(weight)
    temps.append(temp)

plt.figure(figsize=(10, 6))
plt.plot(battery_range, flight_times, label='Flight Time (min)')
plt.plot(battery_range, weights, label='Total Weight (g)')
plt.plot(battery_range, temps, label='Processor Temp (°C)')
plt.axhline(500, color='red', linestyle='--', label='Weight Limit')
plt.axhline(75, color='orange', linestyle='--', label='Max Temp')
plt.legend()
plt.xlabel('Battery Capacity (mAh)')
plt.title('Trade-off Analysis in Drone Design')
plt.grid(True)
plt.show()</span>

Step 6: Make Informed Architecture Decisions

Armed with trade-off data, your team can now make confident design decisions.

For example, analysis of the design space lets you see the value in decisions like:

• Select a mid-tier processor with passive cooling to balance video quality and thermal performance.
• Choose carbon fiber for the frame to shed weight and leave more room for the battery.
• Drop flight time from 34 mins to 30 mins to stay under cost and weight limits — because customer perception of “30+ mins” is already a win.

The most important and significant benefit of MDO is that these decisions are made at the architecture phase, before you start design implementation, not after your prototype fails its thermal soak test or EMI scan.

Challenges of MDO Implementation and How to Prevent Them

At its core, MDO is about integrating models from different domains into a single optimization workflow. These models can be analytical, empirical, or simulation-based, and that’s where things start getting tricky. Some of the challenges of MDO implementation include;

1. Model Development Challenges

• Building reliable models takes time, especially for things like EMI, drop performance, or supply chain volatility.
• First-order models are easier to build but less accurate. High-fidelity models are accurate but time-consuming.
• AI and machine learning can help accelerate this by learning behaviors from early prototypes or simulations.

Use first-order model estimates. Treat them like agile code: get it working, then refactor.

2. Model Integration and Coupling

• Each team/discipline may own/use different tools. While we have used Python for all disciplines in our example, that may not be the case for your situation.
• Teams sometimes use closed-source, third-party models or tools that are too complex to couple easily, and interfaces may not be compatible, leading to the need for additional data pipelines.
• Units of measurement may be different across models, leading to the need for conversions.

Define shared variables, consistent units, and data exchange formats. The trick is to start with loose coupling and refine integration fidelity over time.

3. Too Many Variables, Not Enough Insight

This is very important. Without structure, MDO becomes a simulation swamp. To solve this, prioritize sensitivity analysis. Use the design of experiments (DOE) to reduce the space. Focus on the 20% of parameters that drive 80% of outcomes.

Without structure, MDO becomes a simulation swamp

Tools like OpenMDAO or ModelCenter are usually used in large organizations, but even simple Excel-based integrations can be powerful when done right.

4. Lack of Cross-Team Buy-In

You can’t MDO in a silo. Everyone needs to contribute models and constraints. Align the team early. Frame MDO as a tool to make their life easier, and not just as a new process.

Closing Thoughts

Multidisciplinary Design Optimization empowers teams to make informed, system-level decisions before committing resources — reducing risk, surfacing trade-offs early, and aligning design intent across functions. Beyond optimization, it cultivates a culture of systems thinking, model-based collaboration, and early integration.

For lean teams operating under pressure, even a modest implementation of MDO can unlock outsized impact — driving better products, faster decisions, and fewer late-stage surprises.

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