CPG R&D: the next big opportunity for digital transformation
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Consumer Goods and F&B enterprises are hitting a critical innovation bottleneck. Traditional R&D strategies are failing to keep pace with market dynamics—product portfolios are growing more complex, ingredient costs are climbing, and sustainability pressures are mounting. One thing is clear– incremental improvements are no longer enough. This situation demands reimagining R&D through technological enablement. Organizations can transform complex challenges into strategic opportunities by combining advanced data analytics, artificial intelligence, and deep domain expertise. This is where technology becomes more than a tool—it’s a catalyst for breakthrough innovation.
R&D and the art of mastering complexity
Managing a complex set of interdependent, cross-functional tasks is a significant challenge. A prominent European CPG company, after analyzing the complexity of its product and packaging-related activities, discovered that a single product line could involve dozens of concurrent activities. It is not hard to see why, even for top-performing organizations, maintaining an optimal balance between delivered value and cost across the entire product and packaging portfolio is extraordinarily difficult.
Typically companies have been trying to address this challenge by optimizing alignment of R&D with partner functions, through enterprise processes like stage gate for innovation or structural integration of R&D in marketing and/or supply chain. Many global CPGs have introduced highly centralized innovation organizations with operational support located in markets.
However, it is becoming increasingly evident that organizational efficiency alone is insufficient for R&D to handle the complexity of balancing innovation, productivity, ESG, and QFS requirements while ensuring sustainable competitiveness of product portfolios. More specifically, innovation growth is progressively hard to realize with ingredient and packaging costs often already exceeding 60% of COGS in CPG or F&B companies.
This leaves little or no space for additional investment in, for example, more personalized offerings in Beauty care or alternative protein technology in Foods, to name just a few. Net zero ambitions and extended producer responsibility schemes are adding further complexity and cost to the equation: raw materials, ingredients and packaging are estimated to drive over 60% of emissions by some companies and the high cost and relatively limited availability of sustainable packaging solutions are challenges for waste and EPR [Extended Producer Responsibility] fees reduction.
Why R&D needs a digital overhaul
Sophisticated enterprise processes like stage-gate investment pipelines and project portfolio management were traditionally set up for innovation development but today they must integrate Productivity, ESG and QFS requirements. These processes are heavily data-sensitive at many levels. In a typical CPG R&D organization, data is constantly exchanged across a large variety of interfaces both within the function as well as with other functions, markets, suppliers and 3rd parties like auditors or regulators.
A large CPG company found that R&D associates operated at any moment in time across at least 30 different major interfaces enabling the development and delivery of many hundreds of projects.
With this high level of complexity required for competitiveness, R&D operations require effective data management and data science solutions to step-change their contribution to the business. With the advent of AI technology, there is the prospect of associates working through high complexity challenges aided by AI for insight generation and decision-making with an impact exceeding the capability of even the most optimized processes and organizations. Hence, the digital transformation of R&D can profoundly impact the success of CPG companies.
Interestingly, while functions like marketing, supply chain, and finance have made significant strides in adopting digital technologies for automation and data-driven decision-making, R&D teams often still lag in leveraging these tools. Recognizing this gap, CPG companies are increasingly prioritizing investment in R&D digital transformation as a critical step to unlock the full potential of value creation across all initiatives.
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%.1
– [Source: Bain]
Key steps to effective digital enablement of R&D
There are many components to building effective digital strategies in R&D but the following common steps can become a good starting point for ensuring digital readiness:
- Ensure R&D digital roadmaps link to enterprise value creation
- Prioritize R&D sub-processes with measurable impact on the value creation process
- Engage R&D Experts early to ensure that data products deliver
Successful teams start with Enterprise Value Creation goals in mind when designing R&D Digital Roadmaps. Too often we see R&D teams and their digital partners ignore this critical requirement and focus on R&D productivity tools instead. As a consequence, R&D digital pipelines can feel disconnected from the business reality and fail to get prioritized in already over-called data and analytics budgets. Value creation goals can be linked to any of the levers impacted by R&D: innovation, productivity, ESG and quality & food safety.
For example, an R&D client is seeking to substantially improve the NPS impact of global brand launches by reducing the time to first launch for each target market while still meeting critical performance, regulatory and cost parameters in each market. Another global CPG client is seeking to optimize the balance between investments in sustainable packaging solutions and eco-modulated fees under extended producer responsibility schemes.
Using the innovation process as an example, R&D impact is typically measured by product launch readiness to project brief, competitive product performance in the market and the generation of distinctive, competitive claims. Similar measurement approaches apply across productivity, ESG and QFS. What these outcomes have in common, is that they depend on many, highly complex, interdependent tasks and data sets. Selecting the right data and analytics targets from R&D sub-processes is not as straightforward and takes time.
In our work on packaging portfolio value optimization and extended producer responsibility, a thorough assessment highlighted that a phased digital pipeline build starting with essential reporting and analysis capability across different functions is the right starting point for the organization. The digital pipeline explicitly includes development of AI pilots and solutions as the technology matures and organizational and data readiness evolves. The expectation is that these products will enable increasingly sophisticated product scenario planning, supplier selection and insights for innovative packaging solutions, opening up new avenues for value creation.
Fig.1.– Example of R&D sub-processes complexity
Too often, digital and R&D teams fail to collaborate effectively in defining which data products to develop or purchase. This functionally siloed approach not only results in wasted resources but also undermines R&D teams’ confidence in digital enablement, and “tool fatigue” amongst associates. A more effective approach is to co-create value-driven solutions using the steps as described above. This is specifically true for the identification of the relevant R&D sub-process and data targets for data product development. The roles of R&D and Digital experts are interdependent in this process: R&D domain expertise drives ideas for technology solutions, similarly, new technology development specifically in AI can stimulate ideas for new value creation opportunities in R&D.
Typically, we observe that for more immediate, scalable impact, products that help improve data access, transparency and interpretation help R&D associates build confidence in using and improving their extensive portfolio of data sources. This approach also helps R&D identify selected subprocesses for piloting technologies with potential step-change impact on value creation. Emerging examples are the use of AI to boost the creative process by improving insight generation and validation as well as replication of the physical world of consumer product experience and process development based on manufacturing digital twin technology.
Digital transformation in R&D is not about implementing technology, but about creating collaborative ecosystems where R&D domain expertise and digital innovation intersect. The most powerful solutions emerge when we break down functional silos and co-create value-driven technologies that amplify human insight and creativity.
Conclusion
As organizations navigate increasingly complex portfolios, sustainability demands, and data-intensive processes, the traditional approaches to R&D are proving insufficient. Success requires a fundamental shift: aligning digital capabilities with enterprise value creation, focusing on high-impact processes, and fostering genuine collaboration between R&D and digital teams.
With a unique blend of technology expertise and deep R&D domain knowledge, Sigmoid is positioned to bridge the gap between digital potential and real-world impact. We partner with leading CPGs to co-create transformative solutions that can elevate R&D operations from being cost centers to strategic innovation engines—driving efficiency, sustainability, and market leadership in a complex global marketplace.
About the author
Michel Oostwal, PhD, is the Founder and CEO of Orange Transformation Group (OTG), an executive consultancy focusing on enhancing R&D effectiveness in global CPG companies, particularly through AI. He is also a senior advisor to Sigmoid and other global CPG organisations. Before founding OTG, Michel held SVP/CRDO roles at Mars in North America and the Asia-Pacific region.
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