Steering Through Complexity: Lessons in Calm Confidence From a Racetrack Engineer Turned Founder – Ray Minato

Make Some Noise
Steering through complexity: Lessons in calm confidence from a racetrack engineer turned founder – Ray Minato
Make Some Noise

One of the most challenging yet fulfilling parts of our job is helping our clients gain clarity and focus so that they can make good business decisions. When designing, developing, and commercializing complex medical and life science products, the magnitude of variables and choices can sometimes seem overwhelming and even impossible to navigate under considerable uncertainty.

I have often seen people freeze up when faced with too many variables or options – not knowing how to systematically approach a problem. Sometimes, they try to oversimplify by only considering parts of the problem or limiting their analysis to only the next immediate decision, without considering what might be in store after that.

As a leader of a high-performance team who carries out their work in front of clients, I’ve come to appreciate the importance of intentionally using decision-making tools to help work through complex problems. By employing these tools, we are able to arrive at solutions more efficiently and effectively. Moreover, it is crucial to present and communicate the decision-making process itself to the client, as it enhances their experience and provides them with valuable knowledge.

The act of presenting and communicating your thought process first helps you clarify your thinking; sharing your process then builds trust, earns empathy, and creates an environment of openness and transparency, which are the keys to unlocking the gates to real collaboration.

This is our approach and philosophy for delivering outstanding customer experiences at my company, Inertia, a product design and manufacturing consultancy. 

Earlier in my career, I blindly stumbled across a method (which I later found is called a decision tree) to help make decisions very quickly and with extraordinarily little information. I was fortunate to earn the opportunity to live out a childhood dream of working in professional motorsports as a race engineer. For those of you not familiar with the sport, a race engineer is the person who works with the driver and the crew to figure out how to make the car and driver faster, to be more competitive—and win.

A typical race weekend usually follows a schedule like this: Thursday is move-in and setup day, Friday is practice, Saturday is practice and qualifying, and Sunday is a warm-up and then the race itself.

Once I stepped onto the racetrack at the start of each race weekend, my pulse increased immediately and stayed elevated until the race was over. It’s a fast-paced, high-stakes, high-stress, high-ego, unforgiving environment where you are only as good as your last race. Mistakes are not tolerated, as big careers and big money are on the line.

There are hundreds of variables which can affect the performance of a racing car at any given time. There are also literally hundreds of settings you can change on a car to change its handling and performance to suit the driver and track.

On top of that, weather conditions change continually throughout a race weekend, and you’re never at the same track as the weekend before. You only get one or two 30-45 minute practice sessions in which you can work on optimizing the car’s performance, so every second of that practice time counts.

During a practice session, the driver goes out on track, warms up the tires and does a couple of laps at speed to find the limits of grip. They then steam back into the pits to relay their feelings to the race engineer about how the car is handling.

While the driver is on track, the vehicle’s onboard data acquisition system also measures hundreds of performance parameters about the car and the driver’s inputs. You have less than 30-60 seconds to digest both qualitative driver feedback and quantitative data to assess the vehicle’s performance so that changes can be made to the car’s mechanical, electronic, or aerodynamic setup.

After that, the driver goes back out on track to try to improve upon their lap times as well as evaluate the changes that were made to the car. No pressure!

When I first started out in the role of race engineer, I didn’t have much experience; there was no formal training or mentorship, and even within teams, there could be fierce competition between drivers and cars so other race engineers were not always forthcoming with advice or guidance.

I had to figure out a way to learn fast. I never considered myself to be quick on my feet in terms of decision-making at the time, and I relied on the one thing I knew I could do and the one thing I knew would help me.

That one thing was to come to the track prepared. Not only to help me get ready to make decisions on what to do, but also to allow me to feel more confident about making those decisions. You see, being a race engineer is not only about making changes to the car to help it go faster; it’s also about making sure the driver is confident in you and your approach to making the car go faster.

If they are not confident in you, they are not confident in the car, and therefore, they will be less confident pushing the car to its limits. This is especially true when they are driving at over 200mph on an oval track, where one slip could mean hitting the wall at a 75g impact, risking life and equipment.

I’ve said many times that I might be further ahead in my career if I majored in psychology and minored in engineering, instead of only studying engineering.

But what does “getting prepared” even look like? How can you prepare for the not-yet-known feedback and information coming at you from the driver and the car, let alone prepare for the decisions you will inevitably need to make within seconds? Over time, you can build up experience to help make split-second decisions, but I didn’t have time for that. I needed to produce successful results from the start of my career.

While I didn’t realize it at the time, I was preparing by coming up with systems and methods to help digest and react to a massive amount of information, hopefully with calm and confidence.

Only after you’ve done it for a while (and after reflecting on a career) can you identify these systems or patterns of the things you do. As the late, great innovator Steve Jobs once said, “You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future.”

For me, one system I used, unknowingly at the time, was the “decision tree”.

I found this definition in the Cambridge Dictionary, which I think best illustrates my point.

Decision Tree: a drawing, consisting of lines and boxes, that shows the different choices that are available to people before they make a decision, and the possible results of these choices:

The first thing I want to call attention to is the word “drawing” in the definition. When evaluating and solving complex, multivariable problems, I believe the typical human brain (at least my brain) can only synthesize a few variables at a time. After that, things can quickly become unmanageable and overwhelming.

By drawing out complex problems visually, you can organize and arrange the problem for easier absorption, understanding, and synthesis of a solution.

In addition to dealing with complexity, when faced with even a series of simple decisions, it can become difficult to keep track of the potential outcomes of each decision. As a result, most people stop at the first decision point and don’t look beyond that. That is where drawing a decision tree really helped me to tackle complexity and see beyond the first decision point.

Here’s how I tackled making decisions as a race car engineer: I was simply mapping the likely responses the driver might tell me about the car’s performance at each corner of the track (the corners are where the performance of the car can be most optimized).

From there, I mapped out at least two likely but different responses, along with what I thought was the most appropriate solution/change I would make to the car’s setup, depending on the response. If, during a practice session, the driver told me the car was doing X, I would have a short list of changes (A, B or C) I could make to the car’s setup to solve the deficiency in performance.

If the driver told me the car was doing Y, I would change the car by adjusting D, E or F, and so on. I drew these “what-ifs” on top of an aerial view of the track map. For each corner, I would prepare a potential reaction/countermeasure for each of the typical three segments of a corner on the racetrack – corner entry, mid-corner, and corner exit.

It was all prepared and mapped out on one sheet of paper for quick reference and quick decision-making. At that point, it didn’t feel so much like decision-making but rather simply “solution selection,” as the decisions were already prepared in advance using the decision tree I had mapped out.

If the change I made to the car allowed the driver to go faster, I might make further adjustments of that same setting in the same direction. If the change I made to the car hurt performance, then I might try a change in the opposite direction, as sometimes results could be counterintuitive.

Whatever the result (positive or negative), the important thing was that I always learned something from that exercise. And because I was carrying this out in a pre-planned, systematic way, I could learn even more quickly than had I just shot from the hip.

Decision trees are commonly used in software development and machine learning models. So, it’s no surprise that during that time (over 20 years ago), we were also developing mathematical models and computer simulation methods to work through a large volume of more complex analysis and optimization methods in racing. These methods have since become commonplace in the sport.

The decision tree preparation allowed me to work and learn quickly. It also helped me feel more confident about my plan rather than having to react in real time, potentially leading to uncertainty or allowing me to be caught out by emotional responses from the driver or other team members.

This further helped me build confidence with my driver and teammates—knowing I always had a plan and an answer no matter what came at us. When a driver is risking their life to extract every ounce of performance, they need to have 100 % confidence in their equipment and their team. Otherwise, they will hold back and not fully commit or, worse, become hesitant and make a mistake.

Later in my career, after starting Inertia, I continued using the decision tree tool (among others) to help me think through complex, multivariable product design problems and present my thought process to my team and clients.

I found it effective not only in helping me solve problems but also in helping me draw others into my thought process, include them in the decision-making process, and give them agency and ownership of the solution. To me, that is the definition of how to truly collaborate and deliver outstanding customer experiences.

If you’d like to learn more about where and how you might use a decision tree, check out this helpful blog by Miro. At Inertia, we often use Miro to gather ideas and thoughts and collaborate with our team and clients.

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