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David Sjödin
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Research projects will support AI-driven circular business models in industry

Published: 15 December 2020

There are already technical AI solutions that can be used in industry to develop more resource-efficient production processes, to the benefit of both the environment and companies.
– The main challenge is no longer technology development; it is to use technology in concrete business model applications that enables circularity and sustainability gains, says David Sjödin, associate professor of entrepreneurship and innovation.

In traditional linear business models, the responsibility for a product is gradually transferred from the manufacturer to the customer, who in the end is often the one responsible for the product's operation, maintenance and reuse. A basic idea in circular economy is that the responsibility for the product remains with the manufacturer for a longer part of its life cycle through the sale of advanced services where the revenue is based on the results achieved. There is therefore an incentive for the manufacturer to create circular business models where an economically and environmentally sustainable use of resources is built into the business right from the start, for example through key performance indicators (KPIs) with a focus on reduced energy consumption or increased resource utilization.

AI extends machine life

There are a number of uses for AI technology that could improve resource efficiency in the industry. For example, Swedish manufacturing companies such as ABB and Sandvik are investing in AI to create new services for their customers to, for example, discover whether machines are breaking down or being used inefficiently. In this way, preventive measures can be taken at an early stage, which prolongs the life and productivity of the machines.

–There is great potential for new services and business models through AI. It is important to start using data in a structured way and use algorithms to provide new insights with the potential to identify and solve problems in the customer's business, David Sjödin explains.

Halves energy consumption

There is even greater potential when AI is used to optimize an entire industrial production environment. An example is mines.

– Both financial gains and sustainability gains can be very large with fairly simple AI solutions. For example, ABB has developed services to optimize ventilation in a mine so that fresh air is supplied where the work takes place, instead of in the entire mining system. It halves energy consumption and provides safer working conditions.

In a four-year research project funded by Formas with SEK 3.6 million, David Sjödin and his research colleagues will try to answer questions such as: How can industrial manufacturing companies transform their organizations and benefit from AI through advanced services and circular business models? What routines and abilities do you need to build in different parts of the organization to take advantage of AI in your daily work? and how do business models need to be developed to ensure increased value creation and circular gains from AI between actors in industrial ecosystems?

Stimulate data sharing

One problem that needs to be solved is data sharing. How to find contracts and revenue models that adapt incentives and stimulate cooperation, data sharing and win-win relationships between suppliers, customers and other actors in ecosystems such as small and medium-sized enterprises.

The empirical material will consist of interviews with leading industrial suppliers, including ABB, Sandvik, Volvo and their customers such as Boliden, as well as suppliers of AI technology, such as IBM and Microsoft. The researchers will also study the interaction between companies in ecosystem collaborations, for example how and to what extent they share data with each other. The goal is to help the industry transform

– We also see many pilots and promising examples in the industry. But I would say that overall, only a few companies have really managed to implement AI on a large scale. What is interesting is to get an overview of the contexts in which one has succeeded, but also to draw lessons from failures. For example, sophisticated AI algorithms are not enough. You also need to keep track of your data and not least an ability to find the right applications where both financial and sustainability gains can be created, says David Sjödin.