Every vehicle program starts with its own unique set of ambitions. While a top-of-the-range luxury sedan will prioritize comfort and refinement, a high-performance sports car will focus on agility and performance. Every vehicle design sets out a specific set of characteristics that ultimately define the experience of driving the car.
The difficulty is that these characteristics are not quantifiable engineering targets. They are descriptions of an intended customer experience. Unless they can be translated into measurable, technical requirements, they remain little more than aspirations.
This is where attribute target cascading comes in, enabling engineers to translate the desired characteristics of the vehicle into usable, measurable targets. Using our own in-house-developed software tools, we convert subjective attribute requirements into objective engineering targets. This provides the link between what a manufacturer wants customers to experience, and the requirements engineers must deliver at vehicle, system and component level. This addresses one of the most persistent problems in vehicle development: optimizing subsystems at the expense of the complete product. And with greater vehicle complexity, this issue is becoming more prevalent among automotive, aerospace and defense OEMs alike.
Engineering teams naturally focus on their own areas of expertise. Steering engineers, suspension specialists and tire suppliers all tend to focus on their own areas of the vehicle. The development of these systems in isolation may seem rational when viewed at a component or technology level, but customers never experience a steering system, damper or tire independently. They experience the finished vehicle.
The automotive industry has developed a comprehensive range of tools and techniques to measure vehicle performance. The challenge in a time-constrained development plan is ensuring that the right things are being measured at the right point in the process.
The attribute target cascade process addresses this challenge by establishing a clear hierarchy of intent. High-level corporate and customer-led attribute requirements are translated into full vehicle targets, which are cascaded to the subsystem and component level. The process is evident in the way manufacturers preserve distinct product characteristics across generations of vehicles. A brand renowned for driver engagement will prioritize attributes such as steering precision, transient response and body control. A manufacturer focused on refinement may place greater emphasis on isolation, stability and ride quality. These characteristics do not emerge by chance. They result from thousands of engineering decisions aligned around clearly defined objectives.
The vehicle dynamics attribute illustrates this point well. Ride, handling and steering feel are often discussed as separate attributes, yet engineers understand that they are inseparable. Suspension kinematics can influence steering performance. Compliance affects both ride quality and handling precision. Tires shape transient response and refinement simultaneously. Increasingly, software determines how these systems interact.
As a result, vehicle development is less about achieving perfection within individual systems and more about managing trade-offs intelligently. The objective is not to maximize every performance metric. It is to deliver the right balance of characteristics for the intended application.
This challenge is becoming more acute. Electrified powertrains, advanced driver assistance systems (ADAS) and software-defined architectures have introduced additional layers of complexity into the vehicle. Development teams have become more specialized, while simulation tools allow decisions to be made earlier than ever before. These advances undoubtedly improve efficiency, but they also increase the risk of local optimization. Sophisticated simulation cannot compensate for poorly defined targets. If the attribute targets are wrong, higher-fidelity models simply enable engineers to arrive at the wrong answer more quickly.
The same caution applies to benchmarking. Competitive analysis remains an essential development tool, but copying isolated performance metrics without understanding the broader vehicle concept can be counterproductive. Characteristics that contribute positively within one architecture may undermine another.
Successful products are rarely defined by the excellence of individual components alone. They succeed because every subsystem contributes to a set of coherent overall objectives. As vehicles continue to evolve, that principle will become increasingly important. The temptation will be to optimize whatever can be measured. The discipline lies in remembering what customers actually perceive.
Great cars are not created by assembling outstanding components. They emerge when every engineering decision supports a clearly defined product vision. In an era of increasing technical complexity, maintaining that systems-level perspective may prove to be one of the industry’s most valuable engineering capabilities.
