How to make product design in consumer products Sustainable

R&D is at the forefront of innovation, relentlessly developing new technologies, formulating new products, designing new packaging, and improving production methods.

However, R&D is also under pressure to balance multiple, often conflicting needs – including superior product performance, better sensory acceptance, and easier-to-use packaging against the desire to launch to market faster and reduce costs – all while maintaining compliance with market regulations. And lastly, there is the ambition to develop eco-friendly products and packaging.

With so many criteria in play, finding the sweet spot of sustainable products that also meet cost and convenience criteria is hard. A key way to ensure that your R&D team make better decisions is to help them synthesize large amounts of data from a wide variety of sources – which they can turn into meaningful product insight.

Unfortunately, data generated from years of research is often stored in a siloed, fragmented, or inaccessible fashion, which reduces its potential efficacy, especially for teams working across functions. Data transfers are often conducted manually, a slow process that is prone to mistakes.

The good news? Digital technologies are a proven solution to this data challenge, transforming traditional R&D ways of working to a ‘hybrid intelligence’ model. Here, an integrated data ecosystem, powered by AI and digital tools, augments human ingenuity to harness large volumes of data to create better and more sustainable products, faster.

Thus, we believe introducing the following three steps will help to rewire the R&D system for more sustainable innovation:

1. Systematically include sustainable criteria in product development

Have you considered combining an intelligent formulation tool with a built-in environmental performance calculator? This could give formulators visibility of the environmental impact of their products, and the ability to simulate alternative formulations to reduce the environmental footprint, through ingredient substitution, green chemistry, or energy-efficient manufacturing processes. Here, products could be developed and optimized through data-based predictive models to minimize chemical waste, improve speed to market, and forego the cost of running full batch experiments every time.

2.  Build an integrated engineering R&D ecosystem across all disciplines, connecting each development step to enable seamless and circular data flow from product concept, prototyping, scale up to manufacturing and consumer feedback; as well as seamless connections along the supply chain. Harness data through AI-enabled models to improve product performance, improve sustainability, speed-to-market, reduce costs and more.

3. Provide environmental information to the end-user, allowing them to make an informed decision. Consumers are tired of greenwashing; they want to see action. Telling a story about the choice you made to consumers will instill the transparency that they crave – and develop trust in R&D as a force for good.

To deliver this, partner with a specialist digital innovation expert who deeply understands the R&D space. The skills required to build a successful R&D data ecosystem go beyond general IT knowledge. A partner should have expertise in the scientific domain, cutting-edge technologies, proven methods to drive R&D data maturity transformation, and the ability to integrate a supporting stream of activities; like project management, vendor management, and change management, in order to deliver holistic projects.