The packaging industry is facing considerable pressure to transform: increasing and stricter environmental regulations, rising raw material prices and growing demands for sustainability along the entire value chain. AI especially in combination with a more sustainable circular bioeconomy offers decisive approaches for making material cycles more efficient, conserving resources and optimising processes. This article outlines how AI can specifically accelerate the transformation of the packaging industry towards a more sustainable, circular bioeconomy and what opportunities this presents for companies and the environment.

By Alina Kleiner, Mara Strenger und Prof. Dr. Markus

Prevailing linear forms of production, consumption and disposal have ecological, economic and social consequences that are becoming increasingly relevant, particularly in light of a growing world population, which justifies the need for a transformation. An alternative form of economy is the circular economy, which, however, is subject to losses in terms of raw materials and does not contribute to independence from fossil resources when using them as an energy source, and must therefore be complemented by the bioeconomy, which relies on renewable biogenic resources. As a combination of the principles of the circular economy and the bioeconomy, the circular bioeconomy represents an option for addressing the necessary transformation of the packaging industry. The aim is to keep materials and products in biological or technical cycles for as long as possible in order to minimise resource consumption and reduce waste.

The transformation to a circular bioeconomy is particularly necessary for the packaging of short-lived products, such as many foods, as the short utilisation phase means that more packaging and therefore more packaging waste is generated over time than for products with a longer utilisation phase.

At various stages of the value chain, AI can help to increase the sustainability of the packaging industry and drive the transformation towards a more sustainable, circular bioeconomy:

As early as the design phase, AI can support the selection and optimise the use of materials, for example with regard to the use of biomass or residues and by-products from the food and agricultural industries. By accessing large amounts of data, AI can make it possible to predict the properties of novel materials. In addition, life cycle analyses can help to consider the potential environmental impact of packaging at an early stage. For this purpose, AI can be used as a tool to support the calculation of a carbon footprint. AI can also be used to identify the optimisation potential of existing packaging.

In production, AI can increase efficiency, for example by monitoring and optimising energy consumption. Predictive maintenance systems can detect anomalies in systems at an early stage, minimising downtimes and reducing costs. AI can also raise quality control to a higher level. AI-based quality control can integrate visual inspection systems into the process that not only ‘see’ defects, but also ‘understand’ them thanks to intelligently processed data.

By using AI, companies can analyse extensive data sets from sources such as social media and purchase histories to gain valuable insights into consumer trends. In addition, AI's predictive capabilities extend to demand forecasting and inventory management. This enables optimised inventory management, avoids overproduction and reduces excess stock. Such data-based management therefore not only brings economic benefits, but also helps to save resources.

In the area of recycling, AI is showing its full potential to contribute to the transformation of the packaging industry. The prerequisite for material recycling is the separation of materials by type, which AI can optimise through automated sorting systems, as the plastics can be identified and sorted based on colour, material composition or even food contact. Sorting can be supported by AI-supported robotic arms. This precision in identification and sorting is crucial for improving recyclate quality and increasing recycling rates.

One example of the AI-based optimisation of plastic packaging with recycled content is the ‘KIOptiPack’ research project (FKZ 033KI123) funded by the German Federal Ministry of Education and Research, in which the Sustainable Packaging Institute (SPI) at the Albstadt-Sigmaringen University is a project partner. The aim of the project is to provide AI-supported tools that enable companies to efficiently design plastic packaging with a high proportion of recycled materials. Collaboration with industrial partners ensures that the solutions are practical and take into account the requirements of the market.

Besides its many opportunities, the implementation of AI also faces a number of challenges. One of the biggest challenges is the (un)availability and (un)security of data. Many AI models require large amounts of high quality data in order to work effectively. The integration of AI technology into the packaging value chain requires significant upfront investment in infrastructure, software and training. In addition, qualified personnel are required for design, implementation and maintenance. Nevertheless, AI-based solutions are likely to lead to cost savings in the long term and increase the competitiveness of companies.

The use of AI offers enormous opportunities for the packaging industry to become more sustainable and resource efficient. Data-based innovations can help to use materials more efficiently, reduce emissions and optimise recycling processes. Projects such as ‘KIOptiPack’ show how research and industry can work together to develop innovative solutions. AI plays a key role in realising the vision of a more sustainable, circular bio-economy for the packaging industry. The SPI supports the packaging industry on its way to a circular bio-economy through projects such as ‘KIOptiPack‘ or ‘PackMit‘ by not only researching AI tools for more circularity, but also transferring knowledge to packaging experts.