Keywords

7.1 Introduction

In an age characterised by rapid technological changes and ecological challenges, the interplay between digital technologies, circularity, and sustainable development gains significant attention. This chapter explores this nexus with a particular focus on sustainable product management (SPM). SPM represents an umbrella term that includes several established concepts and strategies underpinning a comprehensive sustainability-oriented management on the product level (Rusch et al., 2023). Those concepts comprise, among others, sustainable supply chain management, eco-design and design for sustainability, sustainability assessments, and in particular, the circular economy (Rusch et al., 2023). The circular economy is described as an economic system aimed at minimising waste and making the most of resources, representing a shift from the traditional linear model of ‘take, make, dispose’ to a more sustainable approach of reuse, repair, recycle, and regenerate (Reike et al., 2018).

Digital technologies such as Artificial Intelligence (AI), Big Data, the Internet of Things (IoT), and blockchain are central to the current wave of technological advancements. They offer innovative ways for SPM as they can track, analyse, and optimise material and energy flows and resource use along a product’s life cycle, thereby supporting the idea of circularity and sustainability. This chapter delves into the research question: How can digital technologies support sustainable product management, i.e. help to improve sustainability and circularity of products along their life cycle?

The practical application of these technologies is varied and profound. From enhancing efficiency in practice to playing a crucial role in Life Cycle Assessment (LCA), these technologies offer a new lens through which sustainability and circularity can be viewed and managed. An interesting and new application is the development of digital product passports. In this chapter, we illustrate this through a case study on electric vehicle batteries.

The remainder of this chapter, which is based on the research activities and specific publications of the Christian-Doppler-Laboratory for Sustainable Product Management, is structured as follows. Sections 7.2 and 7.3 provide an overview of the application of digital technologies in manufacturing companies and in LCA, respectively. Then, Sect. 7.4 presents the potential of digital product passports and illustrates this through a case study on their use for SPM in the context of electric vehicle batteries. Finally, Sect. 7.5 concludes the chapter with a discussion of some of the ethical and sustainability concerns relating to the use of digital technologies in SPM and some potential avenues for future research.

7.2 Application of Digital Technologies for Sustainable Product Management and Product Service Systems

As outlined above, digital technologies have considerable potential to facilitate the transition to a more sustainable and circular economy. However, to leverage the full potential of these technologies, it is paramount to understand their individual and combined benefits and use cases. Rusch et al. (2023) provide a comprehensive mapping of current and potential examples of AI, Big Data analytics, IoT, and blockchain technology in the context of sustainable and circular product management. The authors focused on these four digital technologies because they are perceived as essential enablers for accelerating the transition to more circular value chains and the dematerialisation of the economy (European Commission, 2020a). In their systematic review of the scientific literature,146 examples were identified in 186 scientific papers where digital technologies are or could be applied to SPM. Of the 146 examples, 66 of them featured a case study or a real-life example (Rusch et al., 2023). The other 80 examples were only conceptual descriptions of potential applications of digital technologies for SPM. The study highlights that the potential of digital technologies covers the entire product life cycle, from the beginning to the end-of-life phase (Rusch et al., 2023). Most of the examples presented in Rusch et al. (2023) relate to IoT, followed by Big Data analytics, blockchain, and AI.

As can be seen in Fig. 7.1, most studies only describe the general potential that digital technologies can offer to SPM (i.e. the first line in the figure). Less often, the examples could be assigned to one of the following four areas of SPM: supply chain management, (sustainability) assessment, product design, and business modelling. The technologies also vary according to the benefits they offer to SPM with IoT, Big Data, and AI mostly focusing on increasing the efficiency of existing processes, while blockchain applications aim to increase transparency and trustworthiness in exchanging information along value chains (Rusch et al., 2023).

Fig. 7.1
4 horizontal bar graphs plot the frequency of 4 digital technologies by 5 application areas and 4 improved outcomes. General assessment tops for artificial intelligence, S P M general efficiency tops for big data and internet of things, and supply chain management transparency tops for blockchain.

Classification of potential and application examples of digital technologies by area of application and improved SPM outcome (n = 146) (Rusch et al., 2023)

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Figure 7.2 presents more details on the specific SPM activities that can be supported by one or more of the four digital technologies (Rusch et al., 2023). A total of 23 specific activities were identified in the study (Rusch et al., 2023). AI appears to be related to only four of these activities, namely supplier selection, Life Cycle Inventory (LCI) modelling, condition monitoring, and R-strategies (i.e. Reuse, Repair, Refurbish, Remanufacture, or Recycle). IoT was most often discussed concerning its use for (predictive/preventive) maintenance, followed by its use for condition monitoring of products and processes, the collection of data relevant to R-strategies, or for monitoring energy demands (Rusch et al., 2023). Big Data analytics is often discussed and used in conjunction with data collection from IoT sensors, such as in the case of maintenance (Rusch et al., 2023). However, it is also used on data from other sources, such as in the case of trend mining or risk assessment (Rusch et al., 2023). Finally, while blockchain can add a layer of trust to processes in which other technologies are involved, it also has individual applications, such as in compliance-related data exchange along value chains or incentives (Rusch et al., 2023).

Fig. 7.2
A dendrogram of the relative share of 23 specific S P M activities by 4 digital technologies. I o T, big data, blockchain, and A I have decreasing shares. Maintenance of human rights and S P M activities related to A I have the largest Euclidean distance.

Relative share of specific SPM activities by digital technology (n = 120) (Rusch et al., 2023)

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In summary, Rusch et al. (2023) highlight that digital technologies have considerable and wide potential for facilitating SPM practices. To date, most applications have primarily resulted in incremental improvements (e.g., increased efficiency of existing processes), with more radical forms of improvement remaining relatively uncommon. Thus, there is room for a wider and effective utilisation of digital technologies in various areas of SPM to accelerate the transition towards a more sustainable and circular economy.

The ways in which digital technologies are leveraged for sustainable business practices is highlighted by another review by Neligan et al. (2023). The authors report the findings of a representative survey of 583 German companies. The study shows that the degree of digitalisation of a company correlates positively with the adoption of Product Service Systems (PSSs) for resource efficiency. PSSs refer to combined product and service offerings (Ingemarsdotter et al., 2020). They can enable reduced resource use as respective business models are based on access rather than ownership (Ingemarsdotter et al., 2020). Thus, one product may satisfy many customers’ need for a specific function which is in particular of interest in case of products that are seldomly used (Ingemarsdotter et al., 2020). As can be seen in Fig. 7.3, the use of PSS for resource efficiency increases with the degree of digitalisation in general and of the business model in particular. While only around a third of computerised companies (i.e. that use information and communication technology and/or electronic data processing) use PSS, considerably more (approximately three out of five of fully digitalised firms—i.e. firms with virtualised products) use PSS for resource efficiency. The same can be seen when comparing companies according to their business model, where those with data-driven business models (BMs) considerably more frequently employ PSS than those with computerised or traditional BMs. One reason why PSS for resource-saving become more common with an increasing degree of digitalisation is that additional services to a product often depend on the exchange of data and digital networking (Neligan et al., 2023). In addition, company size also plays an important role as PSS for resource-saving is considerably more often used in large firms than small to medium enterprises (SMEs).

Fig. 7.3
A horizontal stacked bar graph of 8 product service systems for resource efficiency by 5 degrees of use. It includes data driven B M at the top for the category, to a high degree of use classical B M tops for not yet and not suitable, and digitalized for the category, to a low degree.

Degree of use of product service systems for resource efficiency. Question: To what extent does your company use the following ways/options to use resources efficiently? (N = 583) (Neligan et al., 2023)

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One common takeaway from the two empirical studies by Schöggl et al. (2023) and Neligan et al. (2023) is that companies must prevent potential lock-ins and economic and environmental rebound effects in their digitalisation efforts. This entails more explicit recognition of the specific purposes for which digital technologies may be applied. In relation to this, Sect. 7.3 will provide deeper insights into the potential of digital technologies in the context of LCA and Sect. 7.4 regarding digital product passports.

7.3 Application of Digital Technologies for Life Cycle Assessment

LCA is a methodological framework that allows one to estimate and assess environmental impacts linked to the life cycle of a product (Finnveden et al., 2009). The distinct feature of LCA is the applied life cycle perspective (i.e. from cradle-to-grave) to assess the impacts of a product, thus avoiding burden shifting (Finnveden et al., 2009). LCA studies comprise in general four phases. First, the goal and scope of the LCA study need to be defined (Finnveden et al., 2009). This phase comprises a description of the product system under study in terms of system boundaries and a functional unit (Rebitzer et al., 2004). The functional unit enables the comparison between alternative goods and services (Rebitzer et al., 2004). This phase is of importance as it influences methodological and data choices for subsequent LCA study phases (Rebitzer et al., 2004). The second phase is the Life Cycle Inventory (LCI) analysis (Finnveden et al., 2009). The LCI comprises a compilation of the inputs (i.e. resources) and outputs (i.e. emissions) of the product system of interest; those inputs and outputs are in relation to the previously defined functional unit (ibid.). The third phase is the Life Cycle Impact Assessment (LCIA) and is designated to interpret the inventory results of the LCI analysis phase (Finnveden et al., 2009). This phase involves the selection of impact categories and classification, the selection of characterisation methods and characterisation, normalisation and weighting (ibid.). The fourth phase is entitled interpretation (Finnveden et al., 2009). This phase is designated to evaluate the results from the previous study phases in relation to the goal and scope, enabling to reach conclusions and recommendations (Finnveden et al., 2009).

As briefly mentioned earlier, digital technologies can enhance the accuracy and efficiency of conducting LCA. This specific potential is analysed by Popowicz et al. (2024) in a recently published systematic literature review of 104 peer-reviewed papers at the intersection of IoT, blockchain, AI, Big Data, and LCA research. These were categorised across the four phases of LCA according to the ISO 14040/44 standard (ISO, 2006): (1) Goal and Scope, (2) LCI, (3) LCIA, and (4) Interpretation.

With regard to IoT devices, Popowicz et al. (2024) find that their use occurs predominantly in the collection of (real-time) data in the LCI phase. For example, IoT sensors can be used to collect manufacturing process data (Garcia-Muiña et al., 2018), such as machine’s electricity consumption easing the collection of accurate primary data (Tao et al., 2014). LCA-related data can also be stored directly on components and combined with data from decentralised IoT sensors and data from centralised repositories (Van Capelleveen et al., 2018).

While blockchain is less often discussed than IoT, it has the potential to increase the transparency and reliability of the primary data collected in value chains (Popowicz et al., 2024), benefitting all four phases of an LCA. Specific applications identified in the literature encompass, among others, data reliability, data traceability, data collection, data exchange, and data validation in LCA (Popowicz et al., 2024). One example from the literature refers to the use of a blockchain for carbon footprint tracking in food supply chains based on IoT data collected from trucks (Shakhbulatov et al., 2019). Another example comes from Rolinck et al. (2021), who propose a blockchain-based data management approach for LCA in aircraft maintenance and overhaul.

Of the four technologies studied by Popowicz et al. (2024), AI was discussed most often in the sample, and a wide range of potential applications in all four phases of LCA were identified (Popowicz et al., 2024). With regard to the goal and scope phase, one of the reviewed studies demonstrated how relevant aspects, such as the lifespan of buildings, can be predicted using machine learning (Ji et al., 2021). In the LCI phase, AI can, for instance, help estimate missing unit process data, as shown by Zhao et al. (2021), who use a decision tree-based approach, or Khadem et al. (2022), who predict impact data using neural networks. In the LCIA phase, characterisation factors can be estimated, uncertainties quantified, or results predicted (Popowicz et al., 2023). For instance, Hou et al. (2020) illustrate how machine learning can be used for estimating eco-toxicity characterisation factors and specifically hazardous concentration levels. Dai et al. (2022) developed a framework for obtaining best-fit secondary data, employing Gaussian process regression (GPR) models to predict secondary data based on covariance functions. Concerning the interpretation phase, Romeiko et al. (2020) demonstrate how machine learning can be used to identify key contributors among various factors to the life cycle impacts.

Lastly, Popowicz et al. (2024) find that Big Data analytics can facilitate the second, third, and fourth phases in an LCA: in the LCI phase, Big Data analysis helps in extracting and managing large datasets. An example is a data-mining-based approach for obtaining data for the foreground system from scientific articles (Belaud et al., 2022). During the impact assessment, it can be used for uncertainty reduction and enhanced analysis, for instance of highly granular data from a product’s use phase (Ross & Cheah, 2019).

7.4 Digital Product Passport for Electric Vehicle Batteries

A digital product passport (DPP) is described as an electronic record that resumes the function of a unique product identifier and product life cycle data carrier (European Parliament, 2023). Consequently, a DPP can be envisioned as a digital technology-based tool that can support the establishment of circular information flows along value chains (Berger et al., 2023a; Jensen et al., 2023). This instrument holds promise to enhance the sustainability and circularity of various industries. For example, in the building industry DPPs are perceived to contribute to greater circularity as those tools could support the end-of-life management (e.g. reuse, recycling) of buildings via recording, storing, and sharing information about incorporated materials and components (Cetin et al. 2023). Considering the electronics and information and communication industry, DPPs enhance transparency along the value chain by enabling the support of audits and verification of sustainability claims, contributing to greater trust among stakeholders (Navarro et al., 2022). Similar potential benefits (i.e. increased transparency, verification of sustainability claims) are also anticipated for the textile industry (Jaeger and Myrold 2023). Furthermore, by including detailed material compositions, a DPP could support sorting and selecting textile waste more accurately, as well as support the identification of appropriate recycling pathways (Niinimäki et al., 2023).

Due to the previously described potential to bridge data gaps, the idea of DPPs has recently received increased attention. This is mirrored in policy papers (European Commission, 2020a, 2020b), upcoming regulation (European Commission, 2022; European Parliament, 2023), industry initiatives (Battery Pass Consortium, 2023; Global Battery Alliance, 2020), and sustainability research (Adisorn et al., 2021; Berger et al., 2022; Jensen et al., 2023). In particular, batteries have received increased attention as regulatory bodies are demanding the deployment of DPPs for this particular product group (European Parliament, 2023). This increased interest is founded in the perception that DPPs can support the establishment of a sustainable European battery ecosystem (European Commission, 2022; European Parliament, 2023). This is of interest because an increase in demand of electric vehicle batteries (EVBs) is projected due to the electrification of powertrains (Neumann et al., 2022). When pursuing SPM for EVBs, actors along the product life cycle have different established strategies and concepts at their disposal (Berger et al., 2022). As discussed earlier, these include sustainable product development, life cycle management, sustainable supply chain management, or the circular economy (Berger et al., 2022; Rusch et al., 2023). The concept of the circular economy has received particular attention as it comprises value-retention strategies such as repurposing and recycling (Kiemel et al., 2020). As the listed concepts and strategies affect different levels of the EVB production system (Huamao & Fengqi, 2007), it can be argued that respective decision situations are characterised by high complexity (Rusch et al., 2023). Thus, decision-makers require high-quality product life cycle data for respective decision support (Rusch et al., 2023). As previously discussed, persistent data gaps along the product life cycle pose a challenge when pursuing SPM. This has also been found for the EVB life cycle (Berger et al., 2023a). Such data gaps could be bridged by a DPP if it were to provide seamless product life cycle data allowing relevant actors to derive information needed to support SPM (Berger et al., 2023a).

7.4.1 Conceptualisation of a Digital Product Passport for Sustainable Battery Management

The conceptualisation and development of a DPP for sustainability-oriented EVB management requires consideration of a holistic life cycle perspective (Berger et al., 2022; Rusch et al., 2023). Thus, the entire life cycle of an EVB needs to be considered when pursuing strategies and concepts for improving its sustainability and circularity (Berger et al., 2023a). Furthermore, a comprehensive life cycle perspective is required to identify decision-makers and their respective SPM-related decision situations along the EVB life cycle (Berger et al., 2023a. This allows one to derive corresponding data needs and requirements that a DPP needs to fulfil to support SPM (Berger et al., 2023a). The EVB life cycle can be partitioned into four phases: the beginning-of-life (BoL), middle-of-life (MoL), end-of-life (EoL), and battery second use (B2U). For illustration purposes, four corresponding value chain actors have been selected to highlight their specific SPM use cases and current data management challenges.

7.4.1.1 Battery Designer and Developer

The product design is critical for incorporating sustainability and circularity aspects in an EVB (Diaz et al., 2021). To address sustainability issues, product design-affiliated actors require information about the sustainability performance of an EVB. This is currently challenging due to the lack of primary data that is needed for the assessment (Buchert et al., 2015; Diaz et al., 2021). Thus, DPPs of in-use and retired EVBs could serve to establish information feedback to the early design stage, providing designers with information about (dynamic) sustainability performances based on primary product life cycle data. Furthermore, information feedback of B2U and EoL process efficiencies (e.g. encountered challenges during EVB disassembly) could support the consideration of circularity aspects in future EVB designs.

7.4.1.2 Original Equipment Manufacturer

To identify and support suitable SPM strategies and concepts, an original equipment manufacturer (OEM) requires information about the EVB’s sustainability performance from cradle-to-grave (Berger et al., 2022). This would allow the OEM to identify life cycle hotspots and thus, to define appropriate strategies for improvement (Berger et al., 2022). The current challenge lies in the lack of high-quality product life cycle data to support sustainability assessments. In this case, a DPP of in-use, as well as retired EVBs, would be beneficial as it could provide either product life cycle data needed for sustainability assessments or could even directly provide information about an EVB’s sustainability performance. In addition, if a DPP were to provide value chain actor information an increase in value chain transparency could support the identification of those value chain actors that require support to improve upon the sustainability of their value-adding activities.

7.4.1.3 Third-party Actor Focusing on Repurpose

To identify suitable EVBs, or rather EVB modules for B2U applications information about their state is vital (Berger et al., 2023b). For this purpose, at a minimum, information about an EVB’s state-of-health is required (Nigl et al., 2021). However, additional in-use battery data is also beneficial to make more accurate statements about battery health. The current challenge lies in the inaccessibility of battery in-use data by third-party actors that want to establish B2U business models (Berger et al., 2023b). Furthermore, disassembly instructions are required to produce B2U applications and support an efficient production process (Berger et al., 2023b). Consequently, a DPP could prove valuable if containing battery in-use data, as well as information about EVB disassembly.

7.4.1.4 Recycler

To ensure safe EVB handling and storage recyclers need information about the EVB status in terms of safety (i.e. how dangerous is the EVB at hand) (Berger et al., 2023b). This requires information about the EVB’s state-of-health or even control over battery in-use data (Nigl et al., 2021). However, such information is not transferred from the MoL to the EoL phase (Berger et al., 2023b). Furthermore, information about the material composition is of interest to support the design of efficient recycling processes (Berger et al., 2023b). This concerns the composition of the battery chemistry, as this allows to design recycling processes that can recover battery-grade secondary material (Berger et al., 2023b). In addition, disassembly instructions are considered highly valuable for recyclers, as they facilitate the design of the recycling process (Berger et al., 2023b). Consequently, a DPP that could transfer such product and product status data from the MoL to the EoL phase would prove useful.

7.4.2 Digital Product Passport Concept for Sustainable Product Management

In light of the SPM use cases presented above and the consideration of a holistic life cycle perspective, the sustainability-oriented management of an EVB requires control over four major information categories (see Fig. 7.4):

Fig. 7.4
An illustration of the conceptual framework for the Digital Product Passport of an electric vehicle battery. It includes battery passport at level boundary 1 with 4 key information categories like diagnostics and value chain actors at level 2 boundary.

Overview of a Digital Product Passport concept for sustainability-oriented Electric Vehicle Battery management (Berger et al., 2022)

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  • Product information—this category contains information that allows the decision-maker to clearly identify the product of interest. Thus, it ranges from general information (e.g. battery chemistry, battery type, manufacturer) to more specific information (e.g. performance-related information, electrical engineering-related properties, material-related properties).

  • Value chain actor information—this category contains information that enables clear value chain actor identification and, thus greater value chain transparency. As well as general information, such as value chain actor name or type, it includes information about the chain of custody (e.g. for materials and components).

  • Sustainability and circularity information—this category includes information about the sustainability and circularity properties of an EVB. Regarding sustainability properties, information includes both social and environmental sustainability performance data. Furthermore, inventory data, applied assessment, and calculation methods are considered enabling greater understanding of respective key performance indicators. Regarding circularity properties, as well as information about the circularity performance, information about the product design is included in terms of disassembly and repair options.

  • Diagnostics, maintenance, and performance information—this category comprises data points such as state-of-health and state-of-charge. In addition, information about the maintenance history (including triggers for needed maintenance actions) are included in this category whice can support value-retaining strategies enabling a B2U.

While the vision of DPP functioning as product life cycle data carrier has great potential for SPM support, possible challenges regarding DPP deployment need to be acknowledged. One of the most prominent challenges concerns insufficient willingness to share product life cycle data by value chain actors’ (Bergeret al., 2023a, 2023c). This may be explained by perceived intellectual property rights concerns, loss of business integrity and reputation, competitive disadvantages, or lack of data sharing incentives (Berger et al., 2023c). Some of those barriers could be overcome by selecting suitable digital technologies or machine learning approaches that enable confidentiality-preserving data exchange (Berger et al., 2023a). Furthermore, upcoming data spaces and ecosystems (e.g., Catena-X (2023) and Gaia-X (2023)) offer potential infrastructure to share data in a “trustworthy” environment.

7.5 Conclusion

The nexus between digital technologies, circularity, and sustainability is a fertile ground for innovation, offering both transformative opportunities and significant challenges. As one delves into this complex relationship, it is essential to recognise the multifaceted roles that technologies like AI, Big Data analytics, IoT, and blockchain can play in this arena.

AI and Big Data analytics have emerged as critical drivers in the realm of sustainable development. These technologies facilitate the analysis of large datasets to uncover patterns and insights that can lead to more efficient resource use. For example, in the realm of waste management, AI algorithms can predict resource and energy consumption as well as waste generation of production processes, enabling companies to increase their environmental performance significantly. Big Data analytics aid in designing products for longevity and recyclability, consistent with the principles of sustainability and circularity. IoT has revolutionised the way resources, processes, and machines are monitored and managed. By equipping objects with sensors and connecting them through networks, resource flows can be tracked in real-time. This visibility is crucial in identifying inefficiencies and leaks in systems. The data generated by IoT devices support decision-making processes that prioritise sustainability and circularity, enabling a more responsive and responsible approach to SPM. Blockchain’s contribution to the circular economy and sustainability is predominantly in enhancing transparency and traceability. This ability to create secure and immutable records makes it ideal for tracking the life cycle of products. In the context of recycling, blockchain can trace the journey of materials from production to end-of-life, ensuring that materials are responsibly sourced and recycled. This level of traceability is vital in building trust in circular economy practices and promoting more sustainable consumption patterns.

While the potential for sustainability and circularity of these digital technologies is immense, it is important to acknowledge and address the challenges they pose. Concerns around data privacy, cybersecurity, and ethical implications of AI decision-making are paramount (Ashok et al., 2022). Furthermore, the environmental impact of the technologies themselves (Schöggl et al., 2023; Bohnsack et al. 2021), such as the energy demands of data centres and the generation of e-waste, must be considered. Addressing these challenges will require a coordinated effort from different actors including corporate actors, innovators, policymakers, and civil society to ensure that the digital transformation aligns with sustainable and ethical principles.

The future of digital technologies in sustainability seems promising, with advancements enabling more efficient and autonomous systems for SPM. Innovations in blockchain could provide even greater transparency in supply chains (Kouhizadeh et al., 2021), facilitating the circular movement of materials. Advancements in IoT technology could lead to smarter production and consumption networks where resource flows are optimised for minimal environmental impact (Ren et al., 2019). Future research could address empirically whether the potential benefits of digital technologies for sustainability and circularity, which are often derived from case studies, really materialise in business practice. Additionally, it could be analysed how these digital technologies can enable radical sustainability strategies aiming for net zero environmental impacts in practice. Finally, future research could address the implementation of digital technologies and their potential for enabling radical sustainability solutions.

In summary, this chapter underscores the transformative potential of digital technologies in advancing the circular economy and sustainability. The future of sustainability in the digital age is not just about the technologies employed but how they are used responsibly and inclusively. Embracing these technologies while addressing their inherent challenges is pivotal in our common journey towards a more sustainable and circular world.