There’s been a lot of discussion on whether artificial intelligence (AI) in design engineering cycles can better the fortunes of design engineers. A recent Forrester Research survey of 163 engineering managers suggests that without AI, many companies would likely fall behind their competitors in getting products to market on time and achieving the product quality to stay competitive in an increasingly cut-throat market.
Some of the study’s key findings follow below.
Adopt AI or Risk Becoming Uncompetitive
The study also found 67% of engineering leaders feel pressure to implement AI in design engineering cycles in their engineering workflows to avoid losing competitive advantage.
Product Delays Are Costly
The use of AI in design engineering cycles can save engineers time and costs later in the process, particularly in testing and validation. The study suggested that inefficient testing exposes manufacturers to immense financial risks. A one-month product launch delay could cost an organization millions or even billions of dollars, according to 82% of surveyed engineering leaders. Nearly half (48%) are expecting millions or billions in cost each time they create an accurate, data-driven model late in the process (after testing a prototype) or if they must recall a product due to quality issues.
AI Reduces Time-Consuming Modeling
The study concluded that engineering managers risked losing market share without AI solutions to stay competitive (71%). Reducing the number of modeling iterations by extracting the most from test data can help them achieve this. However, traditional tools are inadequate at analyzing large amounts of data. Fifty-five percent of respondents report that existing virtual validation tools are not reliable enough to guarantee that designs pass validations.
Time To Market, Design Efficiency Suffer Without AI
One long-time challenge in design engineering is getting a product to market is the process of testing and validating new, complex products. The study found that meeting project deadlines and product launch dates is the top challenge for interviewed engineering leaders (55%). Also, 51% felt that they are not getting the insights they need to design the right product in spite of running a lot of tests.
Efficiency for engineering teams is also an issue. According to Forrester, problems included no time for innovation (55% of those surveyed) and creativity (44%) and a lack of trustworthy data (46%) that is properly recorded and stored (54%)─ all factors that prevent leaders from finding complex, critical patterns and insights. Late-stage design changes that risk budget and schedules are often the result (50%), jeopardizing the manufacturer’s ability to respond competitively and in a timely manner.
Don’t Ignore the Data You Collected
While one of the advantages of using AI in design engineering cycles the ability to gather and track data, the study found that the many firms did not take advantage of this. On average, only 50% of surveyed engineering leaders use AI to analyze test data from current or upcoming products, and only 29% use it to analyze test data from historic products. Half of all respondents don’t analyze historic data at all, according to the report.
AI’s Benefits Are Clear
All surveyed engineering leaders see benefits of implementing AI in design engineering cycles to support product testing and validation, and some of the benefits go beyond that work group. Forty-seven percent report that their company experiences higher revenue, profitability, and competitiveness as a result of implementing AI. Leaders say that engineers armed with AI tools are more productive (55%) and creative (45%). Having better product and testing insights (52%) enables them to avoid wasted design efforts and accurately predict the time to market for new products (44%).
Overcoming Labor Shortages
The study also concluded that AI in design engineering cycles helps engineering leaders bridge the gap in the talent pool: More effective use of design and test resources (45%) and increased retainment and transfer of knowledge and expertise (35%) can help overcome this barrier that hinders the efficacy of testing and validation. This is particularly important in recessionary times when teams become leaner.
Spencer Chin is a Senior Editor for Design News covering the electronics beat. He has many years of experience covering developments in components, semiconductors, subsystems, power, and other facets of electronics from both a business/supply-chain and technology perspective. He can be reached at [email protected]