Applying in combination circular economy principles and artificial intelligence (AI) is predicted to radically transform the way manufacturing firms create, deliver, and capture value. However, in seeking concrete sustainability benefits, many manufacturers struggle to successfully incorporate AI and circularity into their business models. This paper provides a blueprint detailing how AI can enable circular business model innovation and what steps can be taken to accelerate the transition.
The current climate change crisis is calling on industrial manufacturers to take greater responsibility for the transition to a more sustainable industry. To address this challenge, artificial intelligence (AI) has been portrayed as an important transformative force to make Swedish industry more sustainable and competitive. Indeed, those companies that put sustainability issues at the top of the corporate agenda are also investing in AI capability development to enable circular business models (CBMs), which focus on creating value by implementing solutions that reduce, reuse, and recycle material and energy resources. In fact, many Swedish companies have a strong interest in implementing advanced service-based CBMs where value is created by optimizing resource use through AI technologies and where revenue is generated based on delivered outcomes (e.g., performance-based services). For example, ABB has recently launched the SmartVentilation solution that reduces energy consumption by 54%, contributing to a healthy, safe, and energyefficient working environment for Boliden’s Kankberg mining operations.
However, most large manufacturing firms have failed to scale AI applications beyond initial proof of concept for circular offerings. Although AI technology can provide the foundation for successful CBMs, simply spending money on digital infrastructure, technologies, and data is not enough. New routines, skills, operational processes, ecosystem collaboration and business model innovation are required to make use of AI and circularity principles so that sustainable value is created for customers.
To address these challenges and understand how AI can facilitate progress towards CBM implementation, we conducted in-depth interviews with senior managers from multiple industrial manufacturers and their customers in diverse Swedish industries. We summarize our insights in this article and present an AI-driven circular business model innovation framework (see Figure 1). The framework describes i) how AI enables diverse types of CBM, and ii) the steps needed to accelerate the process of realizing sustainable benefits.
AI-enabled circular business models for manufacturers
We view AI as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019, p. 17). Appreciating the potential benefits of adopting AI for circularity requires an understanding of the types of CBM that can be applied. We identify three main CBMs as the key focus areas adopted by leading Swedish manufacturers and detail how AI can contribute to their effectiveness in realizing sustainability benefits.
First, a sharing business model is where heavy equipment providers, such as Volvo Construction Equipment, make their products available to customers under rental, leasing, or performance-based contracts but retain ownership. One particular example of this type guarantees the availability of the equipment for a predefined contractual period during which the provider receives periodic payments. AI can magnify the competitive strength and reduce risks associated with sharing business models, such as product-as-a-service and leasing. By combining real-time and historical data from products and users, AI can improve the effectiveness of sharing business models through pricing and demand prediction, predictive maintenance, and smart inventory management. The sustainability benefit is higher resource utilization, which comes from extending the lifecycle of products, lowering operational costs, increasing productivity through higher product availability, and removing system-level waste.
Second, an optimization business model is where heavy equipment providers, such as Sandvik Mining, use digital technologies to offer preventive maintenance, fleet management, or even site optimization contracts. A specific example is “site optimization” where whole production sites are optimized by leveraging data from connected equipment to reduce inefficiencies, waste, and emissions in the mining and construction industries. The core idea is to ensure availability of “the right equipment for the right task with the right operator”. Thus, AI can enable increased process efficiency through continuous analysis of operational data, facilitating the identification of process–performance bottlenecks that can then be eliminated. As our informant noted, optimization business models can leverage AI to ensure decreased equipment downtime, optimized capacity, and reduced mean time to repair, to name only some of the potential benefits.
Third, a loop business model is where manufacturing firms, such as the industrial motor division of ABB, add service contracts to extend the lifecycle of their products, including a take-back system in cooperation with industrial recycling actors – Stena recycling in the case of ABB. After recycling, products or components can either be used by ABB in the manufacture of new motors or resold to third parties to be used in their products. A key focus is on designing products and solutions with higher product utilization in mind, and ensuring an all-encompassing coverage of recycling, remanufacturing, and reusing materials and components. AI can facilitate the simulation of product configurations in different usage scenarios, the identification of equipment or parts in need of replacement, and the activation of corresponding work processes for recycling.
We therefore contend that, if manufacturing firms can develop and effectively utilize AI for circular business model implementation, they are able to make a significant contribution to sustainability – namely, by hitting all the pillars in the so-called triple bottom line, they can derive benefits not only economically but also socially and environmentally.
Steps for succeeding with AI-driven circular business models
Understanding the types of CBM and the benefits of applying AI is only an initial step towards sustainable transformation. Indeed, initiating CBMs on a wider scale often fails, particularly in the case of traditional manufacturing firms, because implementation requires a radically different way of thinking about the business and the alignment of incentives with industrial ecosystem actors, such as providers, partners, and customers, than the current modus operandi. Indeed, the dominant business models employed by manufacturing firms are still rooted in the take-make-waste paradigm. Clearly, there is a need for novel principles to innovate the way firms collaborate to create, deliver, and capture value in a more sustainable way. In particular, we find that firms need to actively leverage the application of digitalization and AI within their business models by considering changes to their underlying processes, organizational structures, and usage of AI technologies in business activities and relationships. We find that, accelerating AI usage for CBMs can be instigated by three fundamental steps.
As a first step, we suggest that firms must involve customers in AI-enabled co-creation processes, which can lead to profitable CBM implementation. Specifically, optimization business models that have been co-created with customers through iterative and close development cycles and targeting specific operational pains are likely to generate greater customer value and sustainability over time. Our respondents stressed that defining value propositions for AI-enabled CBM requires an understanding of the unique operational needs and contextual data originating from customer sites in order to identify areas for improvement.
Access to such customer data is a critical perquisite for co-creating solutions, benchmarks and for visualizing the circular offering. Furthermore, when the data pipeline is being established, AI algorithms can be used to introduce dynamic changes and adjustments, which can lead to obtaining real sustainable benefits.
As a second step in accelerating the transition, we encourage manufacturers to engage ecosystem partners in increasing scalability to capitalize on the rapid growth of AI applications. A critical element is to facilitate increased data flow to stimulate the ability of business units, customers, and ecosystem actors to co-produce new offerings and capture value from AI in a more rapid and scalable manner. For example, ABB viewed the Synerleap startup network as a critical part of its digitization and sustainability transformation journey. Indeed, our informants noted that extended ecosystem partnerships are playing an increasingly prevalent role as they move towards CBM and AI application. Indeed, as manufacturers advance along the route to CBM transformation, there is great potential in using niche digital partners to spark innovation in novel circular offerings. For example, specialized AI-driven startups and SMEs can play vital roles in emerging circular ecosystems by optimizing energy and material use in specific areas of operation (e.g., predictive maintenance, and building energy optimization).
As a final step, manufacturers need to engage more actively with AI, which means expending greater efforts to transform their business into a data-driven organization. In point of fact, progressive manufactures are embracing data as a driver for decision making and value delivery process improvements to ensure that the potential sustainability benefits of circular offerings are achieved in practice. In particular, analyzing data insights can transform customer interaction processes by actively using data-driven insights powered by AI. For example, manufacturers, such as Sandvik and ABB, have set up dedicated remote data monitoring centers to enable real-time support for customer and internal service operations through their circular offerings. Such active monitoring and use of AI are key in creating a data-driven culture where circularity principles are used for the purpose of optimizing both customer-facing and internal processes. In the provision of circular offerings, embracing data can lead to effective operational and strategic decisions, which ensure that continuous improvement, learning, and innovation are achieved.
We view AI and circularity as key enablers for a more sustainable industry. We call on managers in traditional manufacturing companies to act on this potential. We offer a brief description of the types of circular business model that can be offered, and how AI can amplify their sustainability impact by identifying opportunities to reduce, reuse, and recycle material and energy resources. To facilitate the transition to AI-driven CBMs, we recommend that manufacturers invest in AI capabilities and deploy them in a three-step process. Specifically, we recommend increased efforts to co-create AI-enabled offerings with customers in an iterative way. We foresee vast potential in using circularity principles as a template to identify opportunities for increased efficiencies. Additionally, we stress that the transition to a circular economy is not something a company can achieve on its own – a broader integration of ecosystem partners is required. Balancing the incentives and data sharing among multiple actors will be key in realizing sustainability in practice. Finally, to ensure that circularity benefits are achieved in actual fact, we further recommend that managers should prioritize investment in data-driven delivery processes to monitor and coordinate the realization of sustainable benefits.
We gratefully acknowledge the financial contributions from Vinnova, Formas, NRC and PiiA which made this research possible.
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