🚀 5 Engineering Strategies to Accelerate Software-Defined Product Development 🚀

[Semih Asil]

Software-Defined Products and the Transformation in Engineering​

Today, software-defined products are fundamentally changing what manufacturers expect from their engineering departments. Development cycles are shortening, while product complexity is increasing. This puts significant pressure on engineering operating models and toolsets.

Engineers spend approximately half of their workday on tasks outside of core engineering duties, such as documentation, reporting, and information searching. Accenture estimates this leads to an annual global productivity loss of $39 billion.

Here are five engineering principles that will help manufacturers adapt their engineering capabilities to the demands of software-defined products:

1. Stop Running the V-Model Like a Relay Race 🏁​

Following a product requirement from initial idea to specifications, hardware, and code has always been a core part of the engineering process. With software and data increasingly defining features and performance, the need for traceability has become more critical than ever. As manufacturers must provide continuous updates, upgrades, and fixes, simply doing the work and handing it off to the next person is no longer sufficient.

BMW demonstrates what happens when traceability becomes an operating capability. The automaker needed faster access to data from development vehicles and a better way to track signals throughout prototyping cycles. It developed a Mobile Data Logger solution that monitors and records over 10,000 signals twice per second across a fleet of 3,500 development vehicles. The company then added an AI co-pilot that allows engineers to query this vehicle data in natural language. Now, this solution delivers data to engineers approximately 10 times faster and supports analysis.

2. Automate Verification and Compliance 🤖​

A related challenge is engineering verification. Demand is growing faster than testing capacity, making verifying and validating frequent software releases, new hardware, and compliance in regulated or safety-critical environments one of the biggest challenges.

Tier 1 automotive supplier Daiichi Electronic demonstrates the advantages of having verification and compliance run within the development stream, not at its end. As automaker expectations shifted towards software-defined vehicles, Daiichi Electronic had to raise its standard of proof without slowing down development speed. It used PTC's Codebeamer to create a workflow that linked requirements, tests, defects, and releases, which reduced the core application effort from nearly a year to two months. With traceability and defect tracking integrated from the start, teams found issues earlier and reduced the amount of rework often caused by late-stage proof collection.

3. Shift to Simulation-Driven Development 💻​

An engineering leader told us: "When we build a circuit, we may need to rebuild that circuit three to four times, and each time it takes two and a half months. If we get it right the first time, we save almost half a year's worth of work." AI-powered process planning, simulation, and optimization are expected to deliver at least a 20% productivity increase.

Siemens Energy demonstrates the benefits of simulation and virtual product development. To build hydrogen-capable gas turbines and reduce CO2 emissions, it created an engineering digital thread that integrates all product data and engineering tools like Siemens Teamcenter and Simcenter. This helped engineers visualize, simulate, and optimize turbine designs across global engineering teams. As a result, Siemens Energy's engineering team completed 26 design iterations (four times more than traditional methods) and delivered the world's first 100% hydrogen gas turbine system in record time.

4. Let AI Empower How Engineers Work 🧠​

Product engineering is rapidly becoming an AI-powered profession. Technology and tools enable the reduction of routine tasks, allowing engineers to spend more time on judgment, creativity, and problem-solving instead.

ABB Robotics offers a concrete example of this shift. It integrated NVIDIA Omniverse libraries into RobotStudio, its simulation tool used by over 60,000 engineers, for teams to design, program, test, and validate automation cells before deployment. This approach achieved 99% simulation-to-reality correlation, reduced deployment costs by up to 40%, accelerated time-to-market by up to 50%, and decreased installation and commissioning by up to 80%.

5. Streamline Partner Collaboration 🤝​

Product development is a team effort. As a senior aerospace engineering executive told us, 70% of an aircraft is built by the supply chain. Yet many programs still manage partners through email, inconsistent data, and late integration tests. This approach fails when suppliers design major subsystems and proof needs to remain robust across organizational boundaries.

When collaborating with external partners, engineering departments must establish clear roles, data-sharing rules, and access controls from the outset. Everyone should use the same approved requirements, interfaces, and change records instead of constantly reconciling different files. Controlled digital environments help partners collaborate securely, protect intellectual property, and detect integration issues earlier. Standard onboarding, checklists, and escalation paths make it easier to bring suppliers into the process without slowing down work. AI can support this by classifying data, flagging oversharing, identifying interface conflicts, and routing proof to the right people. This accelerates co-development and verification and leads to fewer late-stage problems, as projects can only move as fast as the slowest partner.