Product Development "Systems Engineering is the Tool to Manage Complexity"

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The challenges in product development are increasing, that's nothing new. Why companies should absolutely rely on systems engineering to avoid falling behind is explained by Christian Zingel, Head of Systems Engineering at Palfinger, in an interview.

Since when and why has systems engineering been used in product development at Palfinger?

The first steps were taken by Palfinger back in 2020, before my time, with the launch of the Global Palfinger Organization. The goal was to better integrate the various business units into the group by harmonizing, standardizing, and centralizing processes. One aspect at that time was also the establishment of Systems Engineering, as the complexity of Palfinger's products is constantly—or rather, exponentially—increasing.

What challenges had to be overcome during the implementation?

These were and still are challenges that persist to some extent even today. For example, it involves creating awareness that the need truly exists. There was also a lack of understanding of what it means to implement Systems Engineering (SE), which is not a small subject area but one that affects more or less all disciplines. The goal of SE is to work in a structured and systematic manner, which accordingly also affects non-technical processes and their stakeholders, such as support or management processes. As described in its process framework by ISO 15288, SE is a vast and complex topic and, beyond that, a kind of mindset—a collection of principles that brings about a complete cultural shift. The implementation of SE results in the introduction of numerous new, dedicated methods and tools in various areas, particularly in the development organization.

What did Palfinger start with?

With system architecture modeling. At the time, a tool was selected that we still use today. The tool IQuavis offers significantly more possibilities to represent the model compared to the well-known SysML editors, such as tree structures of data artifacts that can be linked. However, it also allows the use of familiar diagrams such as flowcharts, block diagrams, or state diagrams to model the system. The tool also has some very smart features, such as navigation through models, which significantly improve user acceptance and ease of use. The basic principle is: if a new method is to work, the tool and methodology must also be accepted by the designated users.

How was the new methodology received?

Very differently. Those who seriously engaged with the system architecture modeling module accepted it well. However, the process context was often still missing. If the system architecture model is not linked to the rest of the development process and the information processed within it, it does not offer much value yet. Some were already considering potential for further or repeated use, but naturally, in the initial phase, it was still challenging to adopt the new methodology as long as it was not an integral part of the other work. It was often viewed with skepticism, as it was not yet clear at the time to what extent the methodology and tool would be integrated into Palfinger's development environment.

About Dr. Christian Zingel

Christian Zingel has been Head of Competence Cluster Systems Engineering at Palfinger since 2022. In this role, he is responsible for the development and implementation of systems engineering methods and IT tool solutions. He also supports employees in the global Product Line Management & Engineering organization.

Zingel has been an enthusiastic systems engineer for almost 20 years and is committed to science, research, and the industrial implementation of Model-Based Systems Engineering (MBSE). Before joining Palfinger, he led the MBSE consulting team at :em Engineering Methods AG and worked as Lead Engineer at AVL List GmbH.

Zingel earned his doctorate at the Karlsruhe Institute of Technology (KIT) and holds a degree in mechanical engineering with a focus on automotive engineering from the University of Karlsruhe, Germany.

Is it already a fully integrative component today?

No, I want to be completely honest. In general, a lot is happening at Palfinger, and starting with architecture modeling, we have addressed additional areas. Pilot projects were conducted successfully, and the potential was recognized. However, there are still open issues, such as responsibilities. This is still a work in progress because Systems Engineering is inherently not a small topic. It affects many departments and employees, and the more people you need to bring on board, the more challenging it becomes.

Where has systems engineering proven itself?

We have successfully supported a few major development projects, such as with the company Aker BP. This involves developing remotely controllable cranes for offshore oil and gas drilling platforms, which are even expected to operate autonomously in the future. In such a highly complex project, Systems Engineering is particularly helpful because system understanding needs to be built as structured as possible right from the start. After all, an application like this involves an incredible amount of new technology and, with it, uncertainty. Accordingly, Systems Engineering is especially beneficial because the methods and tools allow for a unified understanding of the system use cases to be established and explicitly documented. As a result, boundary conditions, problems, and requirements can be identified, and the scope can also be better defined—truly useful measures for the initial steps in the ideation phase.

Do you have examples or figures that demonstrate the advantages of systems engineering in the product development process?

In my opinion, the measurability of the advantages of systems engineering is not reliably possible. One could devise KPIs, but is the context comparable? Do the figures have statistical evidence and thus meaningfulness?

The key directional decisions are made in the early stages of a project. These are difficult to correct later and often come with high costs. Alternatively, the error might only be discovered when the product is already on the market and has to be recalled. This is exponentially more expensive than identifying the error during the concept phase. However, all these statements are difficult to quantify in numbers. Yet, simply by the fact that Systems Engineering places focus on the early phases, where fundamental decisions are made, many risks are likely identified early on. This saves time and costs, and improves quality.

Simply by the fact that systems engineering places focus on the early phases, where fundamental decisions are made, many risks are likely identified at an early stage.

How do you assess the relevance of systems engineering for designers and developers in mechanical and plant engineering?

Increasingly so. This is because Systems Engineering only makes sense when there is a certain level of complexity, where many challenges converge and numerous unknown factors are present—something that is increasingly the case in mechanical and plant engineering. Unknown factors, in particular, lead to uncertainty and thus risk. Having a systematic approach like Systems Engineering is becoming ever more important. The industry is characterized by steel, kinematics, mechanical design, hydraulics, and, to some extent, pneumatics—essentially the classical mechanical engineering disciplines. However, electronics is also making its way in, with a clear upward trend. The topic of software is also becoming increasingly important. In automotive engineering, we talk about Software Driven Vehicles—perhaps one day we'll speak of Software Driven Machines as well.

A Palfinger crane surely also features sophisticated electronics, I assume?

Of course, most of our products now also feature control units. Modern cranes typically no longer use simple PLCs but rather powerful control units, sometimes even multiple ones, complemented by extensive sensors and actuators to implement numerous assistance, automation, and autonomy functions. The three megatrends from the automotive sector—electrification, connectedness, and digitalization—are also present in other industries. Perhaps with some delay, but the curve seems to be even steeper when you consider that we can suddenly utilize artificial intelligence (AI). AI significantly enhances the capabilities of embedded software, as the more data is made available through sensors, the more information can be processed, enabling functions to be realized through AI-driven software.

The more digital the world becomes, the more relevant this also becomes for mechanical and plant engineering. Data is the new oil, as they say, and AI is the new engine! When a company systematically leverages AI by working digitally and systematically, it can reap the benefits much more quickly.

Systems engineering is, in the context of digitalization, the ultimate tool for managing complexity. I wouldn't say that one can master it; rather, it is the capabilities enabled by systems engineering that allow this complexity to be effectively utilized.

AI is a good keyword—is there also a symbiosis between systems engineering and AI, similar to what exists in the field of simulation?

Of course. First of all, we are already demonstrating what is possible when a system architecture model or a platform architecture model is linked with other data or models, creating digital continuity—the so-called digital thread. When this is done, the potential of AI becomes strikingly evident. On one hand, AI can be used to automatically review and improve the quality of data and models, starting, for instance, with consistency checks. I’ve already used extensive review functions in modeling tools some time ago, but depending on the complexity of the dependencies regarding consistency, AI can achieve a lot. A nearly classic application of AI is the decomposition of requirements documents and digitization into requirements models.

Additionally, AI can provide valuable support with other tasks. AI assistance enables rapid efficiency gains by independently categorizing and structuring requirements. When formulating requirements oneself, AI can help make them SMARTer, meaning specific, measurable, accepted, realistic, and time-bound.

Do you have tips for getting started?

In principle, it would be beneficial to have a characteristic, meaning challenging, project to serve as a pilot project. However, it must not be urgent, meaning there should be no time pressure right from the start. Additionally, if you assemble a team of people who think systematically and structurally by nature, you can begin evaluating initial Systems Engineering methods and tools. Training on the methodologies provides a solid foundational skillset. Then you start—initially even with paper and pencil; it doesn’t necessarily have to be a tool immediately. The most important thing is to actively get started and develop a functioning approach step by step. Only in this way can you determine which method suits which purpose and what functions a potential modeling tool needs.

Thank you for the conversation!

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