AI-driven engineering and design insights: Manufacturing’s next competitive edge
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An engineering team quotes a new part, projecting 100 parts per hour based on process planning and machine capability. But once the part launches, it becomes clear that the line can only achieve 50 per hour; tight specifications reduced machine output. What engineering learned from a similar part two years ago didn’t reach the quoting team because systems weren’t connected. Critical details were buried in historical production data. The result: a massive cost overrun caused by a quote that wasn’t built on historical learnings.
Scenarios like this play out more often than most manufacturing leaders realize. While Industry 4.0 investments have transformed plant floors around the world, engineering — where manufacturing costs are determined — still operates on legacy tools and siloed data. A Rolls-Royce analysis found that 80 percent of cost reduction opportunities required changes to part designs. The stakes are especially high in industries like automotive, where products have 15-year lifespans and can take five to seven years to develop. There’s not much opportunity to get it wrong when timelines are this long.
As retirements accelerate the loss of institutional knowledge, tariff volatility complicates supply chains, and supplier consolidation reduces sourcing options, manufacturers have a decision to make: connect engineering to the rest of the business and use AI to gain insight, or fall further behind global competitors that have already figured out how to streamline the process with more mature data intelligence.
Where Design and Engineering Break Down
This quoting problem — just like most engineering failures — is a symptom of how engineering works. It’s inherently iterative: test, break, redesign, test again. Engineering processes are also complex and disconnected by nature; they span years, involve multiple systems, and require collaboration across functions.
Each manufacturing team has traditionally operated in a legacy system that wasn’t designed to share information, leaving them completely unaware of what useful data was available in other siloed environments, such as similar parts produced last month, last year, or at a different facility. As such, part consolidation opportunities are also often missed.
When this data isn’t accessible across functions, the overall process breaks down:
- Procurement can’t see engineering specifications from past similar parts when sourcing new suppliers, so they negotiate without knowing what drove cost on similar components.
- Quality can’t access and consider design rationale if problems emerge on the floor, which means troubleshooting starts from zero instead of incorporating original intent.
- Quoting teams can’t draw on historical production data, so they keep repeating estimating errors.
- Sales can’t respond quickly to RFQs because they’re waiting on engineering, which means opportunities close before quotes arrive.
The same disconnect exists across multiple sites. A five-plant operation spanning one or more continents may have three stamping plants that share little data on a regular basis. The knowledge at each location stays there, and the company expends resources solving the same problems three times. Making that knowledge accessible across plants supports knowledge retention, training and faster problem-solving, ultimately mitigating the chances of the same mistake twice.
Making the right engineering and design data accessible to non-engineering functions enables teams to move faster and make smarter decisions along the way, including eliminating waste and proliferation.
Extracting Value from Current Platform Investments
Legacy systems themselves aren’t to blame for this problem. The significant investments made in product lifecycle management (PLM), enterprise resource planning (ERP), and other enterprise platforms are necessary and valuable; they’re just often underutilized. In fact, very few companies, if any, achieve maximum effectiveness from these platforms. Expert users are often the only ones who know how to extract value from them at all. What if there was a tool that could look through all these systems to connect data and surface insights from joined-up thinking?
This challenge intensifies in multinational companies: Each site runs different systems with little unification across locations. Fragmentation slows collaboration and prevents knowledge transfer across borders. Every team dedicates time and resources to solving the same problems independently.
Here’s the good news: Existing investments don’t have to be abandoned. Instead, they can be connected by a data intelligence platform that catalyzes the extraction of value from all of them. In other words, by connecting information across disparate systems and applying an AI data platform, people from all over the organization can access the necessary data to surface insights and support ideas and decisions that would otherwise remain buried or require a huge expenditure of resources, and the associated delays, to find.
There is a financial-impact dimension to this as well. A data intelligence platform that draws from PLM, ERP and other enterprise systems can reduce the number of specialist licenses an organization needs for those systems, since non-expert users can access the data they need through the connecting platform instead.
How does this work? Consider a manufacturer making similar parts for different customers across different plants. Without the ability to connect systems, no one ever knows about opportunities to consolidate costs. To spot them, teams would have to connect the dots manually amid other operational demands. A data intelligence platform connects these dots. It surfaces similar parts and cost differences and presents supplier comparisons so leaders can identify consolidation opportunities.
Giving easy and rapid access to such information enables engineers to find new solutions, avoid duplication and won’t make the same mistakes twice because they can quickly learn from what’s happened in the past. New employees get up to speed faster, bringing fresh curiosity that surfaces new insights, and the organization builds upon digitalized knowledge from veteran employees.
Competing on Quality, Speed and Connection
Connecting engineering data across the organization is especially urgent for manufacturers seeking to grow. Response speed on RFQs translates directly to win rate. The faster a quote can be built on real production data, the more accurately it can be priced and the more new business it can capture. Global manufacturers have to move faster while becoming more cost-effective in engineering and design. Chinese manufacturers have already developed vehicles at sub-$10,000 price points by accelerating development cycles and standardizing parts that don’t differentiate the product for the consumer. Competing with that in North America or Europe means getting faster and more cost-effective in development, but organizations operating with fragmented internal operations start every race behind. Accelerating momentum with less resource consumption can boost business and financial performance.
With real-time access to historical data made possible by a connected platform, manufacturers can quote faster and launch more right designs the first time. The right platform also supports organizational change by making data accessible and useful without requiring every plant to use the same systems. This flexibility matters when companies are being added to or restructured constantly.
A Measured Path to ROI
Knowing where to start fixing this issue is often the hardest part, but Subaru has a practical model for manufacturers to follow. Before working with CADDi, formal inquiries to the company’s design department were required to access drawings; responses took a week or longer. To address this, Subaru implemented CADDi in one department first. After proving ROI, they scaled the platform to 50 departments. Now, team members across the organization save thousands of hours by accessing critical engineering information directly and without distracting engineers from their primary work. By digitizing legacy data, Subaru reported nearly 2,400 hours per month in drawing-search time savings.
Whether the goal is to reduce engineering hours, compress development cycles, achieve first-time-right designs that eliminate costly churn or consolidate parts across sites, the approach is the same: Begin where pain is greatest to prove ROI, and then expand from there.
For manufacturers ready to capture the value of a data intelligence platform, the Supercharge Engineering: Efficiency Using Strategic Parts Data whitepaper outlines the implementation path with pragmatic steps to turn engineering data into competitive advantage.