noble™ Enabling Artificial Intelligence for mobility.

Executive summary

In this white paper we introduce noble™, the product sustainability manager, we discuss how Kuona Engineering’s new product overcomes the manifold challenges facing sustainability and research and development (R&D) teams worldwide. We also share application examples in which noble™ can be used to provide valuable insight, optimise products, and reduce resource intensity and time to launch drastically.

Artificial Intelligence (AI) and Machine Learning (ML) can support organisations greatly by learning from existing data sets and creating predictive models that capture the high-dimensional, non-linear relationships that exist between a vehicle’s specifi cations and its overall environmental impact. Such models can then be used to explore alternative business models and vehicle confi gurations, to identify potential trade-offs between business wants and environmental needs. This allows teams across the globe to focus on impact reduction activities. But machine learning methods can struggle where data is sparse, as it often is in sustainability. These methods are also difficult for domain experts to apply and interpret. And their use is constrained by concerns about trustworthiness and transparency.

By leveraging the computational power of AI and ML based solutions and combining this with known and trusted data sets, organisations can reduce their experimental efforts by ~80 percent and save hundreds of thousands in R&D resource costs [1]. This will enable companies to get their product to market faster whilst avoiding the complex tasks of manually calculating and estimating their environmental impact.

A lack of available data, expertise and time mean that many organisations are not able to decarbonise their products and processes within the timeframe of a given project. Enter noble™, a machine learning tool that is set to revolutionise sustainable product management across the value chain.