Are Legacy Engineering Processes Costing You More Than You Think?

Engineering teams today are navigating a perfect storm: an explosion of data, an increasingly complex web of software platforms, and relentless pressure to deliver faster than ever before. But many firms are attempting to meet these evolving challenges using outdated tools and legacy processes that don’t meet modern demands.  

The automotive supply chain is facing novel technological challenges – more data being created and consumed than ever before, an increasingly complex web of software tools and platforms, and relentless pressure to deliver faster.

In our recent webinar, our Technical Director James Smith explored the impact of this disconnect – how applying yesterday's toolsets to tomorrow’s challenges can be a drain on resources, a barrier to innovation, and a direct hit to the bottom line. You can access the full webinar recording here: Digital Transformation in Engineering – Enhancing Efficiency Through Automation  

For more in-depth discussion on challenges in automotive data management, browse our schedule of upcoming webinars  

When doing nothing is the worst option

The phrase "if it’s not broken, don't fix it" can be a dangerous mantra in the fast-evolving world of engineering. The unfortunate reality is that in many cases, legacy systems and outdated workflows are broken, and the cost of not fixing them is far higher than you might realise.  

As highlighted in our webinar, engineers can spend a staggering 40% of their week chasing down data, manually repackaging CAD files, hunting for the correct revision, or reworking files that were supposedly finished, rather than focusing their efforts and skills on core engineering tasks.

The problem here is not just the waste of time – it’s wasted momentum, wasted expertise, and ultimately wasted budget. Research suggests that 15-25% of program delays can be directly attributed to issues with data exchange, costing thousands or even millions of pounds each month due to overruns, rescheduling, and supply chain disruptions.  

But what’s the root cause? Usually, it boils down to misaligned or poorly integrated systems, unstructured data, and a lack of a “single source of truth” that all stakeholders can rely on. We've seen this firsthand – from suppliers missing critical milestones because they were working from an incorrect CAD file, to OEMs running approval processes through informal channels like email or Microsoft Teams simply because there’s no structured way available to validate data before release.

Data disarray and the domino effect  

Modern engineering projects are incredibly complex, particularly in the automotive sector, where a typical new vehicle program can involve hundreds of suppliers, tens of thousands of parts, and millions of files. At this scale, a single slip-up in data management – one incorrect file version, one missing part, or one file with incomplete metadata – can have a catastrophic knock-on effect impacting the entire project.  

Legacy processes, such as sending files over email or Dropbox, weren’t designed to support complex interconnected processes like these. Far too commonly, reliance on tools that are not fit for purpose leads to lost information, data arriving broken or misinterpreted, and endless cycles of rework. The domino effect begins – the necessary rework causes delays, which impact launch timelines, put strain on contracts, erode trust between partners, and ultimately cut into profit margins.

The longer these fundamental issues with data consistency and fragmentation are left unaddressed, the wider the gap becomes between what's expected and what's actually achievable, leaving teams perpetually playing catch-up.

Good intentions, meet complex realities

Automation is often touted as a magic bullet to solve process inefficiency. But as we discussed in the webinar, it’s not a universal fix, especially when dealing with the high variability inherent to engineering data. Attempting to fully automate processes without understanding these variables can lead to an "automation mirage"—a solution that looks promising but ultimately fails to deliver. For example:

• CAD data originates from different design tools, coordinate systems, and local standards.  

• The data itself might be encoded in numerous different languages or character sets.  

• OEM requirements can change frequently, sometimes on an almost fortnightly basis.  

• There’s always potential for human error, like sharing incorrect or outdated files.  

To develop a truly robust, fully automated system that can handle this level of unpredictability would require years of development and might never reach a state of static reliability. It’s therefore vital to get the data foundations in order before attempting to build automation on top, or you risk increasing the chaos rather than addressing it.  

Scaling chaos and being “confidently wrong”

Following on from this, a critical point we made in the webinar was that AI amplifies what you feed it. If your underlying data is fragmented, inconsistent, unstructured, or riddled with errors, AI will not magically fix it; it will scale that chaos, leading to flawed analyses and potentially disastrous decisions. And the potential consequences are significant:

• AI initiatives can stall or fail entirely, wasting considerable investment.  

• AI systems can exacerbate existing problems, leading to false confidence in flawed data  

• AI tools can inadvertently create new data silos if not managed strategically, further complicating data governance.  

Preparation is key. Before embarking on ambitious AI projects, engineering firms need to ensure that their data is "AI-ready", that it's well-structured, accurately tagged, and interoperable between systems. Ignoring these foundational elements is a shortcut to increased risk and spiralling costs.

Overcoming these challenges

The challenges we highlighted in our webinar, from the crippling costs of legacy systems and data disarray to the deceptive simplicity of automation and the pitfalls of adopting AI without the proper preparation are significant hurdles for engineering firms. Overcoming them requires long-term thinking and the right expertise to ensure that foundational issues are solved before new systems are implemented.  

If you’re looking for a strategic partner to help navigate the roadmap to effective automation, Majenta has the experience, expertise and knowledge to help. Get in touch using the link below.  

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