R&D plays an increasingly important role in companies’ abilities to innovate and anticipate customer needs. The growth in R&D spending highlights its criticality: Between 2016 and 2021, R&D spending increased by 23% compared with a 10% decrease in sales and marketing spending over the same period.

As spending on R&D grows, executives demand more from it. In other functions, such as finance or supply chain, digitization is about making processes more efficient. In R&D, a digital transformation focuses on activities that can improve the quality of new product development and shorten time to market, such as visualization, automation, portfolio management, and product life cycle management.

For example, digital in R&D might deploy automated testing and digital twins to accelerate testing and design, speed up and improve the quality of testing, ensure product quality, and get products to market faster to gain a first-mover advantage. Bain’s experience suggests that this type of digital transformation of R&D activities can reduce engineering hours by as much as 20%, cut rework by as much as 50%, and enable cost reductions of 5% to 30%.

Identifying improvement opportunities

By identifying improvement opportunities in specific R&D use cases and assessing potential value, R&D leaders can show business leaders the value of digital transformation—for example, digitizing the processes around products, innovation, and development. Automating product data management (PDM) and the bill of materials (BOM) can reduce product development costs and accelerate time to market, making the case for investment in digital R&D tools.

These use cases take place within a wide variety of R&D business scenarios, covering a range of activities from planning, development, and launch to market exit. Scenarios can relate to product planning and R&D management, technology planning and development management, cross-project portfolio management, and product life cycle management (see Figure 1).

Demonstrating value within these scenarios and focusing on high-value opportunities is key. Business leaders justifiably will resist plans that appear to be investing in digital for digital’s sake. Prioritizing use cases that value engineering capabilities can help deliver significant benefits.

Successful digital transformations

Among companies that have decided to take a broad approach to digitizing R&D, several common mistakes often hinder success. Sometimes they frame the challenge as a broad technology transformation, when they would be better served by an incremental approach of experimentation, course correction, and scaling. Others choose a single digital tool, such as a product life cycle management system, and expect it to provide most of the benefits when success actually depends more on identifying achievable goals and applying appropriate tools for each use case. Still, other efforts bog down for reasons that commonly plague new initiatives, such as failure to enlist strong support from key stakeholders, lack of proper investment, or poor change management.

For companies that can overcome these challenges, digitization can deliver significant improvements and benefits. For example, a semiconductor foundry wanted to accelerate time to market, reduce development costs, and enhance quality by standardizing best practices across the organization and improving the management of projects and technology. It started by assessing its R&D to identify problems and set targets based on the quantifiable results it wanted to achieve. Rather than just applying technology to existing use cases, the foundry took the time to redesign processes in anticipation of digitization so that operational changes were understood and sorted out before the digital use cases were even defined. It then selected project life cycle management tools to implement the new digital use cases and rolled out the corresponding change management programs, educating people about how to use the new tools in the context of the company’s specific operations. The results improved on-time delivery of projects from 64% to 82% and tamed budget variance from 27% to 11% over 18 months. On average, it reduced time to market for new products by 13% and trimmed development costs by 8%.

In another case, a manufacturing and engineering company wanted better control over BOM costs. These costs are affected by a wide range of factors—not just the cost of materials but also other factors such as how complicated a design is, what types of materials it calls for, and how much scrap is left behind by a process. The company turned to a digital manufacturing product to help simplify its designs and reduce costs. The product could, for example, ingest computer-aided design drawings and make recommendations on how to adjust design to reduce waste. It can also model the manufacturing environment to identify opportunities to improve productivity. By applying the tool across 16 parts in three categories, the company was able to see a 10% reduction in BOM costs. These opportunities were identified, sized, and designed in just three months.

In another case, a producer of industrial equipment faced challenging market conditions and needed to scale down R&D costs without slowing its product development roadmap. The company conducted a detailed analysis of engineering activities and identified several that generative artificial intelligence could help with, including code generation, test automation, and knowledge management. For this company, the toughest challenge was materializing the increase in capacity—that is, making sure that the time saved was used productively. By encouraging development managers to produce more, the company was able to realize a 10% increase in engineering capacity.

Preparing the foundations

Once companies see the opportunities in digitizing R&D, the wheels start turning, and they’re eager to implement a digital tool or process to begin solving problems. But that can create new problems downstream if solid technology foundations aren’t in place. It can help to consider a maturity model, with initial stages focused on improving data management—specifically, improving the quality of data and integrating it successfully with core enterprise systems (see Figure 2). Once these foundations are in place, adopting and implementing other digital tools that rely on access to good data becomes much more practical and efficient.

Figure 2

R&D should build up data management foundations before investing in broad digitization

As companies look for ways to reduce costs, speed time-to-market, and make better products, investing in the digitization of the R&D function is less of a choice than a necessity at this point. Pacing is important: It's essential to get data management foundations in order first and then move into select use cases with specific, appropriate digital tools. Digital teams need to stay closely aligned with the business side on these moves to ensure that the effort delivers value as it becomes more expensive. But even within a few months, companies can make a difference in how they operate and deliver against the R&D imperative.