Data as a foundation for measuring sustainability performance in supply chains Open Access

Sustainability is no longer optional for businesses due to converging regulatory, market, environmental and social pressures (Le and Govindan, 2024). In this context, supply chains are critical arenas for translating sustainability into measurable outcomes, since performance measurement depends on continuous data flows across multiple tiers of suppliers and operational actors (Krasikov and Legner, 2023; Paasivirta et al., 2024). At the same time, organisations pursue sustainability with varying motivations, ranging from short-term profit maximisation (Friedman, 1970) to long-term viability (Kurznack et al., 2021). Regardless of their strategic orientation, companies adapt to external and internal realities in which sustainability has become increasingly central.

The foundations of long-term business success can be described through three interdependent elements: external drivers (Prashar and Sunder, 2025), organisational resilience (Florez-Jimenez et al., 2024) and internal and external capabilities (McDougall et al., 2022). Value creation and survival depend on a company’s ability to deliver products and services that meet customer demand (Dai et al., 2024), comply with regulatory constraints and leverage both internal competencies and supply chain networks as enablers of sustainability performance. When viewed through this lens, sustainability is not an abstract ideal but a measurable, data-driven domain whose realisation depends on aligning strategy with data-enabled performance measurement in supply chains.

This foundational model also applies directly to sustainability. While sustainability may carry intrinsic value for some organisations, its strategic importance is typically shaped by the degree to which firms must respond to regulatory and market demands (Adolph and Beckmann, 2024). In many cases, sustainability is not pursued for its own sake but emerges as a function of external pressures and the organisation’s ability to adapt. As such, the role and weight of sustainability goals are defined at the strategic level according to how these pressures are perceived and prioritised (Baumgartner and Rauter, 2017). This constellation of regulatory, market and organisational factors aligns with the notion of the business case for sustainability, where companies adopt sustainability measures not only for ethical reasons but also to enhance competitiveness, reduce risks and meet stakeholder expectations (Schaltegger and Burritt, 2018; Schaltegger et al., 2012; Cool et al., 2024). Framing these drivers as elements of a business case makes their connection to performance outcomes more explicit, clarifying how sustainability becomes measurable and strategically embedded.

Today, companies are increasingly expected to demonstrate environmental, social and governance (ESG) performance, not only to meet regulatory requirements but also to satisfy growing expectations from customers, investors and employees (Zumente and Bistrova, 2021). Despite this pressure, many organisations struggle to translate sustainability ambitions into measurable and actionable outcomes (Engert and Baumgartner, 2016). One critical reason is the difficulty of aligning sustainability with the data required to define, track and validate them (Krasikov and Legner, 2023; Paasivirta et al., 2024). The data perspective is often underemphasised, contributing to a widespread disconnect between strategic intent and implementation.

Although sustainability is widely regarded as essential for long-term viability, it often remains disconnected from core decision-making processes, especially when data continuity and granularity are crucial (Jarvenpaa and Essén, 2023). Frameworks like the United Nations Sustainable Development Goals (SDGs) and ESG standards provide high-level direction (Tsalis et al., 2020), but they are often too abstract for operational integration, particularly in complex global value chains. As a result, sustainability strategies are frequently based on limited or inconsistent data, with no clear mechanism to validate progress or adjust goals based on performance. This is especially evident in supply chains, where measurement of sustainability performance depends on data continuity across multiple tiers of suppliers and operational actors. This gap between strategic ambition and data-enabled execution undermines impact and exposes companies to reputational risks such as greenwashing. Despite much discussion surrounding sustainability, measurement, carbon emissions and related calculations, organisations often lack the systematic means to manage the data needed to effectively quantify and address sustainability goals.

This study addresses that challenge by exploring how data supports the realisation of an organisation’s sustainability goals. This study argues that sustainability must be treated as a structured data domain, on par with financial, customer or product data, if it is to be embedded effectively in business strategy and operations. Drawing on existing literature and an empirical case study, this study presents a conceptual framework that integrates sustainability drivers, organisational capabilities and data requirements across both product and operational domains.

Against this backdrop, there is a clear need to examine how supply chain data and capabilities underpin sustainability performance measurement and management. This study responds to that need by focusing on the role of sustainability data, both at the product level and supply chain level, as a foundation for defining, measuring and managing sustainability performance. In supply chains, companies depend on upstream sustainability data from suppliers while also generating and disseminating sustainability data downstream to customers and regulators, highlighting their role as data hubs in sustainability performance measurement.

The study is guided by the following research questions:

Using a qualitative case study of a global manufacturing company, this research contributes to both theory and practice. It highlights the need to align sustainability ambitions with practical data capabilities across supply chains and demonstrates how strategy, systems and data must co-evolve to enable supply chain sustainability performance measurement and management in response to growing regulatory and market expectations.

To avoid ambiguity and ensure terminological clarity throughout the manuscript, the following definitions are applied. In this study, sustainability data refers to environmental, social, economic and governance–related data required to define, measure and validate sustainability performance across supply chains. ESG data are used specifically when referring to regulated or stakeholder-mandated sustainability data that comply with external reporting standards. Sustainability master data refers to sustainability-related data that are structured, governed and system-embedded, such as material composition, emission factors, cost structures or product-level attributes, enabling consistent sustainability measurement across product and operational domains. In line with established definitions of master data (Silvola et al., 2019; Hannila et al., 2020), sustainability master data denotes data used across business processes and subject to defined ownership, quality rules and cross-functional governance. These definitions ensure terminological precision and provide a consistent basis for analysing how data supports supply chain sustainability performance measurement.

Sustainability alters organisational goals by introducing environmental and social priorities that require measurable verification. Companies must demonstrate progress to satisfy stakeholder expectations and manage regulatory and reputational risks (Isaacs et al., 2024). This requires clear goal-setting, a defined baseline (Rossi et al., 2022) and systematic data collection and reporting processes (Krasikov and Legner, 2023; Wings and Harkonen, 2025). Because sustainability reporting can also create incentives for superficial claims or greenwashing (Liu et al., 2025; Oppong-Tawiah and Webster, 2023), reliable data becomes essential for credible performance measurement.

Organisational strategy determines how sustainability is prioritised (Amini and Bienstock, 2014) and provides the foundation for defining sustainability goals and key performance indicators (KPIs) (Dienes et al., 2016). Prior research describes a linear progression from strategy to goals, KPIs and the associated data requirements (Gillan et al., 2021; Vilminko-Heikkinen and Pekkola, 2017). Recent work further shows that reporting requirements shape the master data structures organisations must maintain, reinforcing the link between strategy and data (Paasivirta et al., 2024). However, these studies largely treat data availability as an operational concern rather than a strategic capability.

While frameworks such as the SDGs and ESG pillars provide reference points, their breadth makes them difficult to operationalise, particularly for smaller firms (Fonseca et al., 2024; Smith et al., 2022). Organisations must therefore translate these broad aspirations into goals that align with product portfolios, operational processes and reporting practices (Farkas and Matolay, 2024). What remains less clear in the existing literature is how sustainability goals translate into concrete data requirements and how organisations manage the data infrastructure needed to support performance measurement.

Organisations that treat sustainability as a strategic priority must align their sustainability goals with the data structures, processes and systems that support measurement. Prior research shows that strategy provides the foundation for aligning business processes and performance management (Hristov and Searcy, 2024; Iden et al., 2024), and that data architectures must reflect product, information technology (IT) and market perspectives (Hannila et al., 2020; Ofner et al., 2013; Silvola et al., 2019). This alignment is shaped both by the external forces that drive sustainability efforts and the internal capabilities that determine how those efforts can be implemented.

Regulatory requirements and market demand are widely acknowledged as the primary external drivers of corporate sustainability (Fritz et al., 2017; Qiu et al., 2020; Hartmann and Long, 2025). These are complemented by internal motivations such as corporate purpose, risk management, technological development and access to capital (Wenzig et al., 2023; Haywood, 2022; Ullah et al., 2024). While many drivers influence sustainability adoption, the most critical are those that create measurable and actionable requirements, such as regulatory disclosure obligations and customer expectations for transparency (Beckmann and Feige, 2023).

Organisational and supply chain capabilities determine how effectively firms can respond to these drivers. Internal capabilities shape how sustainability is designed into products and processes (Salim et al., 2019), while supply chain capabilities constrain or enable access to sustainable materials and data (Beske, 2012; Yin, 2024). Without sufficient technological, financial and stakeholder engagement capabilities (Dwivedi et al., 2019; Govindan et al., 2021; Watson et al., 2018), sustainability strategies remain aspirational rather than operational. These capabilities form the operational foundation through which firms translate sustainability drivers into measurable outcomes.

Together, these drivers and capabilities form the organisation’s sustainability strategy (Gruchmann et al., 2021; Peyravi and Jakubavičius, 2022), which shapes the requirements for product sustainability (e.g. material selection, design and use-phase performance) and operational sustainability (e.g. facility emissions, logistics and energy use) (Dyllick and Rost, 2017; Behr et al., 2021). Both domains generate static data (e.g. hazardous substances and factory infrastructure) and dynamic data (e.g. use-phase emissions and real-time energy consumption) (Scheidt and Zong, 1994; Silvola et al., 2019). This distinction is crucial because static data establishes baselines for sustainability targets, while dynamic data enables continuous validation and target adjustment (Buchert and Stark, 2019; Maia et al., 2022).

What becomes apparent across this literature is that sustainability strategies rely fundamentally on the availability, structure and continuity of data. Product and operational sustainability data are interdependent: operational processes generate inputs for product-level assessments, while product design shapes operational requirements. Yet existing research often treats this data foundation as implicit and unproblematic. The literature does not explicitly conceptualise the structuring, governance or continuity of sustainability data across the supply chain, even though these factors determine whether performance indicators can be calculated reliably and constitute the foundational capability upon which existing sustainability measurement frameworks implicitly depend.

This analytical gap highlights the need to understand sustainability data as a structured domain within the broader sustainability strategy. By clarifying how sustainability drivers and capabilities translate into data requirements, this study positions sustainability data as a central mechanism through which strategies become measurable and actionable.

Key insight from the literature: in theory, product and operational sustainability data can be distinguished: the former relates to design, materials and use-phase impacts, while the latter concerns manufacturing, logistics and facilities. At the same time, these domains are interdependent. Operational processes generate data (e.g. emissions and energy use) that is allocated to product-level reporting, while product design choices (e.g. materials and recyclability) shape operational requirements. Clarifying this distinction and overlap is essential for understanding how sustainability data supports performance measurement.

Figure 1 synthesises the preceding discussion by illustrating how sustainability drivers and organisational and supply chain capabilities translate into requirements for product and operational sustainability data. The figure emphasises that sustainability strategies become actionable only when supported by structured data that reflects both internal processes and supply chain conditions. Because regulatory and market drivers increasingly require data originating upstream, the focal company operates as a supply chain data hub, mediating between supplier inputs and downstream disclosures. This perspective highlights that sustainability data is not a by-product of operations but the mechanism through which strategy execution can be monitored, validated and adjusted. The figure therefore provides the conceptual foundation for analysing how sustainability data structures shape the feasibility and credibility of sustainability performance measurement.

In supply chains, companies rely on upstream suppliers for sustainability data inputs (e.g. material composition and emissions) while at the same time being expected to provide downstream disclosures to customers and regulators. This dual role positions companies as data hubs, making sustainability performance measurement dependent on continuous and reliable data flows. Against this backdrop, supply chain sustainability performance measurement has been studied through several established frameworks. The balanced scorecard, for instance, has been adapted to integrate environmental and social indicators alongside financial and operational metrics (Reefke and Trocchi, 2013). In supply chains specifically, this adaptation often takes the form of a “Sustainability Balanced Scorecard” (SBSC), which embeds ecological and social objectives into the traditional four-perspective schema (Hansen and Schaltegger, 2016). The triple bottom line (TBL) framework (Seuring and Müller, 2008) and research in green supply chain management (GSCM) (Zhu et al., 2008; Beske, 2012) similarly emphasise metrics such as eco-efficiency, resource recovery and supplier collaboration, reflecting efforts to capture environmental, social and economic outcomes in supply chains. In addition to these sustainability-oriented approaches, the supply chain operations reference (SCOR) model provides a structured way to evaluate efficiency and responsiveness, and has also been extended to include sustainability metrics (Akyuz and Erkan, 2010).

While these frameworks have advanced the understanding of how sustainability can be evaluated across supply chains, they often treat data as readily available and reliable. In practice, performance measurement is undermined by fragmented, inconsistent or missing information, particularly across upstream suppliers and downstream reporting requirements (Raj et al., 2023; Schäfer, 2023). This dual challenge reflects the supply chain reality in which firms depend on upstream sustainability data from suppliers while also being expected to generate and disseminate reliable data downstream to customers, regulators and other stakeholders. Without continuity across both directions of data flow, sustainability performance measurement remains incomplete. Recent studies stress the importance of data transparency, quality and traceability for effective sustainability performance management (Prashar and Sunder, 2025). However, empirical evidence indicates that companies still lack systematic ways to ensure data quality and continuity across the supply chain (Kokkinou et al., 2025).

Whereas prior frameworks tend to treat data as a given input or by-product of measurement, this study positions sustainability data as a master domain. Conceptualising data as the structured foundation for supply chain sustainability performance measurement and management allows us to extend existing literature while addressing the persistent gap between conceptual frameworks and practical execution, particularly the challenge of ensuring continuity across upstream and downstream data flows.

A qualitative research approach was adopted, using a single empirical case study. The case company is a typical global manufacturing organisation that develops, manufactures and delivers industrial solutions through an established supply chain. The case was selected because of the company’s energy- and material-intensive processes, and more importantly, due to the availability of prior data and the researchers’ strong access to both primary and secondary data sources. A more detailed description of the case organisation is provided later in this section.

The focal company was purposefully selected using a theoretical sampling logic typical of qualitative case studies (Creswell, 2021). The aim was not statistical representativeness, but the selection of an information-rich and theoretically relevant case for examining how sustainability data supports strategy execution in supply chains. The company is a large, global manufacturer with energy- and material-intensive operations, extensive sustainability reporting obligations and a complex multi-tier supply chain, conditions under which sustainability data requirements are especially salient. In addition, the researchers had strong access to interview participants and internal documentation, enabling depth of inquiry and data triangulation. These characteristics make the case particularly suitable for generating analytical insights into the role of sustainability data in supply chain sustainability performance measurement.

Initially, a conceptual analysis framework was developed based on existing literature to connect sustainability and sustainability-related data to the organisation’s strategic goals (Figure 2). This framework enabled a focus on real-life data requirements and facilitated exploration of practical challenges in aligning sustainability objectives with the availability and use of data.

The empirical data consist of interviews with company executives as the primary source and internal documentation as the secondary source (Creswell, 2021). The interviews and documentation directly support the empirical analysis for RQ2 and RQ3, while RQ1 is addressed through the conceptual analysis framework developed from the literature. Using the conceptual framework as a foundation, a top-down qualitative content analysis approach was applied to develop a data capability model in the context of sustainability, highlighting how data can support and enable the organisation’s sustainability goals.

Data collection began with secondary data through an analysis of the company’s sustainability report to gain an overview of its sustainability drivers, requirements and overall approach. This also served to validate the suitability of the case company. Secondary data were later used to confirm findings from interviews, enabling triangulation and increasing validity.

The primary data were collected through semi-structured interviews (Clifford et al., 2016). Respondents received the interview questions in advance to allow them to reflect on the topics beforehand. The interviews focused on strategy creation, sustainability goal selection, reporting requirements and data collection. The questions were designed based on the analysis framework and followed a directed qualitative content analysis approach.

A structured interview protocol was developed to ensure methodological rigour. The protocol consisted of four thematic blocks derived from the conceptual analysis framework: (1) strategy formulation and sustainability priorities, (2) sustainability goal selection and KPI logic, (3) data availability and reporting practices and (4) product- and operations-level data requirements. The protocol included open-ended questions followed by targeted probes to elicit concrete examples (e.g. “Can you describe a recent instance where supplier data affected your ability to report emissions?”). All interviewees received the protocol in advance to enhance reflection and response quality. Consistency across interviews was ensured through the use of the same structure, phrasing and sequencing of questions. All interviews, covering key executive roles (Table 1), were recorded, transcribed verbatim and stored for subsequent analysis.

Interviewees were selected based on their roles and responsibilities aligned with the analysis framework, specifically those involved with sustainability, data and strategic development. In total, six executives were interviewed (Table 1). On average, each interview lasted 65 min and was conducted via Microsoft Teams. This platform enabled efficient access to informants and flexible scheduling. However, limitations associated with virtual interviews are acknowledged, particularly regarding the interpretation of nonverbal cues. All interviews were recorded and transcribed into Word documents for further analysis.

The data were analysed using the analytical framework developed in the literature review. The unit of analysis is the case company, focusing on its sustainability practices, products and operations, over time. Qualitative content analysis and textual analysis methods were used. Textual data were examined to identify codes and organise them into relevant themes, guided by the elements of the analytical framework. The coding process was both theory-driven and data-driven, rather than relying solely on a purely inductive approach.

The coding and analysis followed a structured multi-stage process to increase reliability and transparency, consistent with established qualitative coding practices (Saldaña, 2021). Firstly, researchers independently conducted an initial round of open coding on the interview transcripts to identify sustainability drivers, capabilities and data requirements. These codes were then compared, reconciled and synthesised into a shared codebook. Secondly, axial coding was applied to group codes into higher-level themes aligned with the conceptual analysis framework (e.g. regulatory drivers, upstream data dependencies and static vs dynamic data). Thirdly, selective coding was used to consolidate themes into the data capability model. Analytical validity was strengthened through triangulation across interview data, sustainability reports, internal documents and cross-researcher validation of coding decisions.

To strengthen the credibility and analytical rigour of the study, the research team included both external and internal researchers. The lead author and two co-authors were external to the case organisation, providing analytical distance and reducing the risk of organisational bias in data interpretation. One co-author was affiliated with the case company, which enabled deeper contextual understanding, supported accurate interpretation of company-specific terminology and facilitated access to interviewees and internal documentation. Interview protocols and analysis procedures were designed to ensure that the involvement of an internal researcher did not influence respondent behaviour or compromise data interpretation. This combination of insider insight and outsider perspective aligns with recommendations by Yin (2018) and Creswell (2021) regarding researcher triangulation in qualitative case studies, thereby enhancing both confirmability and credibility of the findings.

Initially, business sustainability drivers and organisational capabilities related to sustainability strategy were identified, along with product- and operations-level sustainability data requirements. These insights were derived from the coded interview transcripts and are categorised and presented in Chapter 4 (see Tables 2 and 3). Finally, based on the results of the analysis, a data capability model was developed within the sustainability context to illustrate how data supports the organisation’s sustainability goals.

The case organisation, referred to here as the company, is a global manufacturing enterprise with operations in over 40 countries and more than 16,000 employees. The company reports annual revenues exceeding €3bn. Representatives from the organisation were interviewed to provide insights into the role and impact of sustainability on its operations, strategy and data requirements. Within its supply chain, the company acts as the focal firm: it depends on upstream sustainability data from suppliers (e.g. material composition, emissions and compliance) and generates and disseminates sustainability data downstream to customers, regulators and other stakeholders. This dual role highlights its position as a data hub, making it a suitable case for examining how supply chain sustainability performance is measured and managed.

Sustainability has long been part of the company’s operational practices, but it has now been formally integrated into its corporate strategy. The current sustainability strategy was developed as part of a broader corporate strategy update, published in 2023. In developing the strategy, the organisation considered global trends, emerging market opportunities and potential future risks. Key drivers include increasing customer demands, evolving regulatory requirements and a strategic desire to act as a responsible and reputable company.

Customer expectations regarding sustainability have grown, particularly in terms of transparency into the environmental characteristics of products. While regulatory oversight is often associated with consumer-facing business-to-consumer (B2C) markets, the size of the business unit, rather than a business-to-business (B2B)/B2C orientation, determines the extent of regulatory scrutiny. Larger business areas are subject to more stringent requirements compared to smaller ones.

Sustainability pressures vary across the company’s business segments. In areas where products are already powered by electricity, customer expectations around sustainability remain modest. However, in segments where a significant share of products still rely on diesel power, external pressure to reduce emissions and increase transparency is much stronger. The company’s largest emission category stems from the use phase of its products, underscoring the importance of product-related sustainability initiatives.

The purpose of this qualitative case design is analytical, not statistical, generalisation, consistent with established case study methodology (Yin, 2018). Rather than claiming that findings represent all manufacturing firms, the analysis generalises the underlying mechanisms, such as supplier data gaps undermining KPI validity, the dual role of firms as supply-chain data hubs and challenges of treating sustainability as a structured data domain. These mechanisms are theoretically transferable to other organisations with similar upstream–downstream data dependencies. This logic aligns with case study research, where generalisation occurs through the development of concepts, explanations or models rather than through sample representativeness (Yin, 2018).

This section empirically examines business sustainability drivers and capabilities, product- and operations-level sustainability requirements and the company’s strategic sustainability orientation, aligning them with the conceptual analysis framework.

Before presenting the empirical findings, it is important to clarify how sustainability dimensions are treated in this study. Consistent with the TBL perspective, environmental, social and economic aspects constitute the core dimensions of sustainability outcomes. These dimensions are analytically interdependent and are therefore not examined as isolated categories, but through their combined manifestation in product- and operational-level sustainability requirements. Governance is not treated as a fourth sustainability dimension, but as a cross-cutting enabling mechanism that structures how sustainability goals are defined, how data is governed and how performance is measured across organisational and supply chain boundaries. Accordingly, the empirical analysis does not apply a fixed cross-classification of sustainability dimensions and data elements; instead, sustainability dimensions are reflected implicitly through product- and operations-level data requirements and associated performance indicators, while governance structures shape the availability, quality and continuity of that data.

Business sustainability drivers and capabilities are critical because they help companies remain competitive, manage risks, meet stakeholder expectations and create long-term value, both financially and environmentally.

The regulatory environment is perceived as a major influencing factor. Due to the large size of the company, it is subject to more stringent regulatory requirements than smaller businesses. For instance, complying with the upcoming Corporate Sustainability Reporting Directive (CSRD) requirements demands substantial investment. Emerging regulations not only define the type of data the company must collect from its operations but also influence which data must be retained for future reference. Anticipating future demands, the company has already recognised the value of storing extensive historical sustainability-related data, as this may become essential for regulatory compliance. A central challenge is that many of these requirements depend on upstream supplier data (e.g. material composition and emissions), meaning that regulatory compliance cannot be achieved without reliable supply chain data flows. This illustrates how regulatory drivers translate directly into supply chain performance measurement challenges:

There are several long-term risks and opportunities associated with the regulatory environment. For example, continued climate warming increases the likelihood of stricter environmental regulations. At the same time, rising temperatures may lead to more frequent and severe weather events, such as storms, which pose physical risks to factories and service operations. New regulations may also introduce operational risks by altering the existing business environment. Furthermore, limited visibility into the supply chain can elevate risks related to cybersecurity and human rights.

The regulatory landscape is also highly fragmented across regions, increasing compliance costs and resource demands for multinational companies. Regulations can significantly influence investments. For instance, in the USA, government policies that support the oil industry over green investments have made it economically unfeasible to install solar panels on the roof of the company’s factory in Texas.

The sustainability data collected by the company is retained permanently due to the potential for future regulatory changes. In the event of such changes, the organisation must be able to access historical data to ensure compliance. Currently, Scope 3 emissions reporting and analysis are conducted using Excel. With the CSRD requirements taking effect at the beginning of 2025, the company has already taken steps to prepare. These include initiating external auditing processes to meet the new standards.

To strengthen its internal capabilities, the company has started recruiting a Sustainability Architect to address sustainability-related needs in system design. Additionally, the organisation is seeking a new resource to provide hands-on support for various sustainability programmes and day-to-day operational tasks:

Data regarding modernisation and retrofit projects is reported within the framework of the circular economy. While regulatory compliance is a significant driver, the primary objective is to collect data that generates value for the customer. For instance, the company reports the amount of recycled steel used in its projects, even in the absence of a regulatory requirement to do so.

Market demand significantly influences the company’s product portfolio decisions. According to the company, the main driver behind the electrification of one of its product lines was the need to improve its positioning in the Chinese market, where customers increasingly expect products powered by electricity. Customers’ willingness to pay for green products also varies by region. Although the company is committed to reducing CO₂ emissions, it must also remain profitable. If customers are not willing to pay a premium for sustainable alternatives, the company’s ability to act is limited.

Local market dynamics play a major role in guiding product development and marketing strategies. For instance, in China, the relevant product segment is fully electrified, unlike other regions. In the USA, green steel is not in demand, as suppliers sell it at the same price as regular steel. In contrast, in Europe, the same product is marketed and sold as green steel at a premium price. This creates a constant balancing act, particularly in procurement, between meeting customer expectations and achieving sustainability objectives.

The sustainability data requested by customers varies significantly. Some customers are primarily interested in corporate-level data, while others seek information specific to their individual product deliveries. To address these differing needs, the company provides sustainability reports at both the corporate and product levels, with the latter typically structured around product families. The most detailed reporting, at the level of individual products, is considered ideal, but it requires access to component-level data from suppliers. Thus, product sustainability requirements underscore the dependence on upstream supply chain data: without reliable supplier inputs, product-level performance measurement remains incomplete. In contrast, corporate-level reporting relies on more generic, aggregated data.

The process of obtaining sustainability data and compiling related reports is currently time-consuming. To address this, the company has launched a pilot project where CO₂ data is embedded directly in the product’s bill of material (BOM). While the supplier contract formally requires all suppliers to provide the necessary data, acquiring this information in practice remains a significant challenge.

Data storage presents another hurdle. The current product data management (PDM) and enterprise resource planning (ERP) systems lack defined fields to support the collection and storage of sustainability-related data. For example, product design processes still rely on manual calculations, and sustainability considerations are often introduced too late. The goal is to shift sustainability analysis to the beginning of the product development process, with the required material data available from the outset. Early access to information, such as the presence of hazardous substances like lead, within the PDM system would support better initial material selection and enhance sustainable design practices.

The supply chain plays a critical role in the development and execution of the company’s sustainability strategy, particularly in terms of supply chain capabilities. The company must continuously balance the cost of sustainable raw materials and components with what the market is willing to pay for its products. Increasingly, the practices of the company’s vendors, such as how they source materials and labour and how they comply with environmental regulations, are becoming essential factors that demand greater transparency. These supply chain capability gaps directly affect the company’s ability to measure and report sustainability performance, especially Scope 3 emissions.

To ensure alignment with its sustainability goals, the company requires all vendors to sign a contract that commits them to adhering to the company’s sustainability requirements:

The company has made continuous improvements to its internal capabilities to enhance sustainability. These efforts include technology upgrades, investments in data collection systems and procurement of green electricity, all of which contribute to reducing operational emissions. Extensive safety procedures and awareness campaigns promote employee well-being during the workday, while inclusive hiring practices and a range of employee training programmes provide opportunities for career development.

Management support is critical for the success of sustainability initiatives. In this case, upper management, beginning from the chief executive officer (CEO), has demonstrated a strong commitment to sustainability, setting the ambition for the company to excel across all key sustainability dimensions:

The desired state is full transparency: the company communicates only what it is genuinely doing in the field of sustainability to avoid any concerns about greenwashing. This commitment to authenticity and openness is also leveraged in marketing and sales efforts.

Table 2 summarises the business sustainability drivers and capabilities that support the company’s sustainability strategy, as derived from the empirical findings of this study.

Product and operational sustainability requirements are significant because they generate valuable data, both static and dynamic, that help validate the sustainability strategy and enable ongoing adjustment of goals to stay aligned with real-world performance and evolving expectations.

Product design, pricing and marketing are influenced by the sustainability requirements outlined in the company’s sustainability strategy. These product requirements also define the types of data that need to be collected.

Static product sustainability data includes information that remains unchanged throughout the product’s lifecycle. For example, the company tracks hazardous materials used in the manufacturing of product components and provides this information to its customers.

Dynamic product sustainability data refers to information that can be collected throughout the product’s lifecycle. For example, CO₂ emissions during the use phase can vary depending on the intensity and duration of usage. Capturing such dynamic product data also depends on feedback loops with customers and the downstream supply chain. Without reliable usage-phase data from external actors, sustainability performance measurement risks being incomplete or misleading. The company has developed technology that enables the collection of this data from customer equipment in near real time, allowing for ongoing analysis and insights.

Operational processes such as manufacturing, logistics and facility management are guided by the sustainability requirements defined in the company’s sustainability strategy. These operational requirements also determine the types of data that must be collected to monitor and improve sustainable performance.

Some sustainability-related consumption and its associated data creation occur independently of manufacturing volumes. For example, a factory continues to consume electricity for emergency lighting and district heating to maintain basic operational readiness. Data related to this type of consumption can be estimated using baseline values derived from vendor invoices. These invoices, however, originate in the supply base, again showing how upstream vendor data directly conditions the accuracy of operational sustainability performance measurement:

As manufacturing volumes fluctuate, so does the consumption of materials and supplies. For example, the quantity of chemicals required in the manufacturing process is directly correlated with the number of units produced.

The company stores both static and dynamic product and operational sustainability data in various databases for reporting and product design purposes. KPIs are used to validate the sustainability strategy and adjust targets, but their validity depends on consistent data collection across supply chain tiers. Data gaps or delays at the supplier level propagate downstream, undermining both corporate-level reporting and customer-specific disclosures. Beyond compliance, these data also support capability development, for example, by pushing suppliers to improve component-level inputs, encouraging recycled material use and preparing for upcoming requirements such as the CSRD.

In practice, the availability of sustainability data also varies significantly across sustainability topics. Interviewees emphasised that quantitative environmental data, such as GHG emissions, are generally more readily available due to established calculation standards and regulatory reporting requirements, whereas social sustainability data related to labour conditions and human rights in upstream supply chains are often more fragmented, qualitative or difficult to verify. This uneven data availability further constrains comprehensive sustainability performance measurement and reinforces the need for structured data governance across sustainability domains.

At the operational level, product and operational data are interdependent: factory-level emissions (operational) are allocated to product footprints, while material choices in the BOM shape operational requirements such as energy use and waste handling. This overlap shows that product and operational data must be treated as interconnected elements of supply chain performance measurement. Recognising these interdependencies is also crucial for external reporting, where fragmented links between the two could undermine transparency. These empirical findings demonstrate that environmental, social and economic sustainability aspects are tightly interlinked in practice, as product design choices, supplier practices, cost structures and regulatory compliance simultaneously influence sustainability performance across these dimensions.

The company applies a rigorous approach to external sustainability reporting. It publicly discloses only those sustainability areas where the underlying data can be fully verified. For example, the annual sustainability report includes a dedicated section at the end that details the data sources behind the reported figures and statements. The systematic collection and management of product and operational sustainability data play a central role in ensuring transparency, credibility and accountability in the company’s sustainability practices:

Table 3 summarises the product and operational sustainability requirements for sustainability data.

These empirical findings also validate and refine the conceptual logic outlined in Figure 1. The sustainability drivers and capabilities identified in Table 2 correspond directly to the left side of the framework, demonstrating how regulatory and market pressures shape the company’s data needs. Likewise, the product and operational sustainability requirements in Table 3 map onto the right side of the model, confirming that both static and dynamic data creation processes determine what data is actually available for performance measurement. Across all interviews, managers emphasised gaps between these conceptual requirements and the data that could realistically be collected, reinforcing the baseline and data-dependency mechanisms illustrated in Figure 1. This alignment shows how the empirical results both support and concretise the conceptual framework, providing the basis for the data capability model developed in the Discussion.

The company’s sustainability strategy is shaped by both business demands and capabilities. Together, these elements define sustainability requirements for products and operations. A sustainability requirement can encompass one or several goals, such as reducing carbon dioxide (CO₂) emissions by a specific percentage. The success of the strategy, and the need for alignment, is evaluated by comparing the data generated from products and operations to the goals defined in the strategy:

When selecting goals, market perspectives play an important role. For example, within the company, safety is considered just as important a goal as CO₂ reduction. However, in the case company’s primary markets, customer and investor interest is currently more strongly focused on CO₂-related data, even though safety remains a critical internal priority. This emphasis is particularly evident in the context of regulatory reporting and product-related disclosures. Measuring progress can be challenging when markets demand data that cannot be captured through established practices. The company has aimed to select science-based targets, with CO₂ reduction seen as the most suitable candidate. Both investors and customers have also emphasised the importance of CO₂ emissions reduction:

Goal selection is also influenced by market conditions. For instance, while the company would prefer to purchase green steel, limited availability prevents it from doing so:

Market dynamics can also shape organisational behaviour. Intense price competition from China pressures the company to remain competitive. Some competitors lower prices by using lower-quality materials. In addition, sudden external shocks can act as change drivers. For example, when the war in Ukraine began and the risk of Russian natural gas shortages increased, it became suddenly financially viable to invest in green energy.

Various internal stakeholders contribute to the formation of the sustainability strategy. In the case of material purchasing, for instance, procurement and upper management align to define guidelines on what types of materials to acquire. In this way, the sustainability strategy serves to guide departmental behaviour. Nonetheless, conflicts between sustainability strategy and business operations still occur. While sustainability is included in decision-making, it can be overruled by managerial guidance.

The execution of the company’s sustainability strategy generates both product and operational sustainability requirements. Interviews concluded within the company revealed that the main factors informing the strategy are the regulatory environment, market demand, supply chain capabilities and internal capabilities. These elements collectively form the business sustainability antecedents.

The strategy is applied across both product and operational domains, each of which produces static and dynamic sustainability data. Execution, however, depends on both upstream supplier data and downstream reporting requirements, reinforcing the role of the company as a supply chain data hub. Incomplete or inconsistent data flows across the chain directly challenge sustainability performance measurement. This data are used to validate and refine the strategy and to recalibrate the drivers and capabilities upon which it is built.

The process aligns with the conceptual framework presented in Figure 2, which supports the company’s need for fact-based reporting. To be practically usable, sustainability data must meet specific criteria. The following interview insights summarise the requirements for sustainability data (see Table 4). More detailed content requirements can be found in Tables 2 and 3, while the technical requirements for data and data management are presented in Table 4.

In supply chains, the ability to measure sustainability performance depends fundamentally on the integrity and continuity of data across upstream and downstream actors. Sustainability measurement is therefore shaped not only by the quality of indicators, but by the strength of the data infrastructures that support them. When data is incomplete, inconsistent or not aligned across tiers, the validity of sustainability metrics deteriorates and organisations are unable to capture the environmental and social impacts embedded in their products and processes. This makes data continuity a central performance enabler in supply chains.

The case findings illustrate a broader pattern in supply chain sustainability measurement: organisational ambition is constrained not by the availability of measurement frameworks, but by the availability and structure of the underlying data. Sustainability objectives can only be meaningfully defined when organisations understand what data is obtainable internally and from suppliers. This dependency positions sustainability data as a strategic resource that determines the scope and credibility of performance assessment across the supply chain.

Persistent challenges in obtaining supplier-specific information, such as Scope 3 emissions, recycled content, hazardous substances or material-level characteristics required during product design, reflect common discontinuities in multi-tier supply chain data flows. These discontinuities weaken KPI reliability, limit product-level sustainability assessment and create exposure to reporting inaccuracies. Such dynamics confirm that sustainability performance measurement is fundamentally a supply chain coordination problem, where upstream transparency, data governance and inter-organisational information exchange determine the feasibility of credible measurement.

Reliable sustainability data therefore contributes to performance measurement not only by enabling indicator calculation but also by strengthening competitiveness, reducing reputational risks and supporting trust throughout the supply chain. Prior work has emphasised how credible sustainability information underpins the business case for sustainability by reducing risk and reinforcing stakeholder confidence (Schaltegger and Burritt, 2018; Schaltegger et al., 2012; Cool et al., 2024). In a supply chain context, accurate data enhances transparency for customers, credibility for external stakeholders and coordination with suppliers through clearer expectations regarding data quality and disclosure. Sustainability data thus functions as a shared value-creating resource, rather than solely an internal reporting input.

Measurement frameworks such as the SBSC, TBL, SCOR and GSCM offer useful structures for organising sustainability indicators, but they implicitly assume that reliable data is available. In practice, however, the absence of structured, consistent and governed sustainability data creates a structural limitation that these frameworks do not address. Their effectiveness collapses when the data foundation they implicitly rely upon does not exist. Frameworks typically focus on what should be measured, while treating the availability, quality and continuity of data as implicit and unproblematic. Yet in supply chains, data availability is neither stable nor assured: fragmented upstream data, inconsistent supplier disclosures and dynamic product-level requirements systematically undermine measurement validity.

In this sense, conceptualising sustainability data as a master domain does not merely complement existing sustainability frameworks; it exposes a missing architectural layer that these models rely on but do not theorise, thereby challenging their underlying assumption that data is readily available, structurally aligned and analytically compatible across the supply chain. This reconceptualisation requires a redefinition of sustainability measurement capability itself, from the ability to select indicators to the ability to ensure the data integrity, continuity and governance structures that make those indicators meaningful.

Conceptualising sustainability data as a master domain helps address this overlooked foundation. Figure 3 illustrates the data capability model that captures this requirement by distinguishing the structured data assets, governance mechanisms and cross-tier flows that enable sustainability goals to be defined, operationalised and assessed throughout the supply chain. Each component of the model reflects recurring patterns in the empirical data: supplier data gaps informed the upstream visibility dimension; missing ERP and PDM fields informed the structured data architecture dimension; and inconsistent KPI ownership across functions informed the governance and accountability components. These links ensure that the model is not conceptual only, but directly grounded in the observed practices and challenges of the case company. Without such a data capability, even sophisticated performance frameworks cannot function as intended.

To further clarify the internal logic of the model, the foundational elements are elaborated below. In the model, the distinction between “data that is collected” and “data that can be collected” reflects a recurring empirical finding: the company’s reporting ambitions consistently exceeded the data actually available from suppliers or internal systems. The “baseline” element captures the gap between required and available data, which interviewees repeatedly identified as the starting point for defining realistic sustainability targets.

Viewing sustainability data as a master domain shifts attention from the selection of indicators to the conditions that enable indicators to be validly calculated. Measurement becomes a function of data architecture: structured definitions, cross-functional ownership, quality controls and tier-to-tier data continuity. When these architectural elements are weak, sustainability performance measurement collapses, regardless of the measurement methodologies used. Conversely, when organisations invest in robust data governance, they are better positioned to demonstrate credibility, withstand scrutiny and mitigate the risks of greenwashing. This strengthens value creation for customers, regulators, investors and suppliers by enabling transparency and enhancing the reliability of disclosures.

Taken together, these insights indicate that sustainability performance measurement in supply chains derives its value not only from the indicators chosen but also from the integrity of the data systems that support measurement across organisational boundaries. Reliable sustainability data improves transparency, enhances reputational credibility and strengthens supplier collaboration. Positioning sustainability data as a master domain therefore reframes supply chain sustainability performance measurement as a data-governance challenge. This reframing highlights the structural data prerequisites necessary for effective use of existing frameworks. Specifically, it shows that measurement breaks down when data continuity between upstream suppliers, internal operations and downstream customers is not maintained. By reconceptualising sustainability performance measurement as a function of data architecture, supported by structured definitions, quality controls and cross-functional governance, supply chains can move from indicator-centric to data-centric sustainability management, thereby adding measurable value to sustainability supply chain performance.

The primary theoretical contribution of this study lies in clarifying the relationship between sustainability strategy, data and supply chain performance measurement. It highlights how data supports the realisation of an organisation’s sustainability goals and positions sustainability data as a structured domain, thus extending the literature on supply chain sustainability performance measurement. Prior research has offered frameworks such as the balanced scorecard (Reefke and Trocchi, 2013; Hansen and Schaltegger, 2016), SCOR (Akyuz and Erkan, 2010), GSCM metrics (Zhu et al., 2008; Beske, 2012) and the TBL (Seuring and Müller, 2008) for evaluating sustainability performance, but these approaches often assume that reliable data is readily available. The findings of this study demonstrate that this assumption is problematic and has remained theoretically unaddressed: fragmented upstream supplier data, inconsistent downstream reporting requirements and the absence of shared data definitions frequently undermine measurement validity.

By linking data-enabled measurement to the business case for sustainability (Schaltegger and Burritt, 2018; Schaltegger et al., 2012; Cool et al., 2024), the study further clarifies how reliable sustainability data connects directly to competitiveness, stakeholder trust and risk management.

By treating sustainability data as a master domain, this study provides a missing architectural foundation for systematic supply chain performance measurement. This perspective exposes an untheorised layer in existing frameworks: sustainability indicators depend on consistent data structures, cross-tier continuity and governance mechanisms that these models rely on but do not conceptualise. In doing so, the study extends existing frameworks in two significant ways. Firstly, it shows that sustainability measurement capability must be redefined from the ability to select appropriate indicators to the ability to ensure the data integrity, continuity and governance structures that make those indicators meaningful. Secondly, it shows that sustainability performance cannot be reliably assessed without a coherent data architecture that spans the supply chain, thereby challenging the implicit assumption of data availability embedded in established frameworks.

The study also adds conceptual precision by clarifying the distinction and interaction between product and operational sustainability data, including data describing operational processes such as energy use, emissions and logistics activities. While existing frameworks (Hansen and Schaltegger, 2016; Seuring and Müller, 2008; Akyuz and Erkan, 2010) acknowledge multidimensional sustainability measurement, they do not theorise how product material information and operational process data jointly shape measurement validity. By drawing this distinction, the findings show that sustainability performance measurement requires both domains to be jointly structured yet analytically separable to ensure comparability and accuracy.

Furthermore, the study integrates insights from master data management research (Silvola et al., 2019; Vilminko-Heikkinen and Pekkola, 2017) and extends emerging data-centric perspectives (Krasikov and Legner, 2023; Paasivirta et al., 2024) by demonstrating how static and dynamic data support the calibration of sustainability goals over time. It also contributes to strategy-focused work (Baumgartner and Rauter, 2017; Engert and Baumgartner, 2016) and master data research (Silvola et al., 2011) by showing that sustainability strategy execution is conditioned by the maturity of the underlying data architecture.

Although based on a single case, these conceptual contributions, identifying a missing architectural layer, reconceptualising sustainability measurement capability and clarifying the product–operational data interaction, are generalisable to diverse organisational contexts facing similar upstream–downstream data dependencies. In this way, the study advances both sustainability strategy and master data management literatures while grounding these contributions firmly in the supply chain domain.

From a managerial perspective, the study offers actionable guidance for sustainability managers, data governance professionals and especially supply chain leaders. Firstly, managers should integrate sustainability data requirements into supplier contracts and procurement processes to improve upstream visibility, particularly for Scope 3 emissions, recycled content and hazardous material reporting. In practice, integrating sustainability requirements into supplier contracts involves specifying mandatory data fields (e.g. material composition, emission factors and recycled content), defining acceptable data formats and linking non-compliance to approval or delivery conditions. For example, companies can adapt supplier contract templates to embed mandatory sustainability data fields early in onboarding processes, ensuring that missing data prompts systematic follow-ups.

Secondly, product development and supply chain teams can leverage the distinction between static and dynamic data to improve traceability and embed sustainability metrics into BOMs. Thirdly, IT and data governance professionals should define and structure sustainability-related data fields within ERP and PDM systems, ensuring that sustainability analysis begins at the product design stage rather than as an afterthought. The case also revealed concrete system-level and organisational barriers that managers must address. The ERP system lacked structured fields for several sustainability attributes, forcing teams to rely on spreadsheets and responsibilities for maintaining these fields were unclear, leading to inconsistent data ownership. These challenges highlight why the capability model emphasises defined data structures, governance roles and system-level integration as prerequisites for reliable sustainability metrics.

Fourthly, supply chain governance should prioritise data quality, transparency and traceability as performance measurement enablers across multiple tiers of suppliers. Finally, executive leadership can use the proposed data capability model to align sustainability targets with actual data availability, making performance measurement more robust, reducing risks of greenwashing and enabling more agile strategy adjustments. The results highlight a direct implication for accounting and supply chain management: the accuracy of sustainability reporting, including Scope 3 emissions and other disclosures, depends on the maturity of the underlying cross-tier data architecture, making data governance a central concern for both accounting functions and supply chain leaders rather than an auxiliary technical issue.

Together, these actions position data not as a reporting burden but as the central enabler of supply chain sustainability performance management. Moreover, these managerial actions generate distinct forms of value for key stakeholders, improving transparency for customers, enhancing the credibility of disclosures for regulators and investors and strengthening coordination with suppliers through clearer data expectations. Although derived from a single case, these recommendations are broadly applicable across manufacturing sectors and other industries with complex supply chains that face similar upstream–downstream data challenges.

This study is based on a single case organisation and six executive interviews, which naturally limits empirical breadth. The intent was not statistical generalisation but depth of understanding within a complex, data-intensive context. The selected company was theoretically appropriate due to its energy- and material-intensive processes, extensive supply chain dependencies and advanced sustainability reporting requirements, making it a strong setting for examining how sustainability data supports strategy execution. Nevertheless, the findings reflect the particularities of this organisation. While the mechanisms identified, such as upstream data gaps undermining KPI validity, the dual role of firms as supply chain data hubs and the need to structure sustainability data as a coherent master domain, are not unique to this case, their manifestations may vary across industries. As such, the study contributes through analytical generalisation: the conceptual mechanisms and the proposed data capability model can be transferred to similar contexts, but further multi-case, cross-industry or quantitative research is required to test boundary conditions and strengthen empirical generalisability. Future research could also examine how variations in industry structure influence the extent to which sustainability data adds measurable value to supply chain sustainability performance measurement.

Building on this limitation, several avenues for future research emerge. Firstly, future studies should examine the applicability of the proposed framework across industries with differing data dependencies, where upstream and downstream gaps may appear in different forms. Secondly, comparative multi-case research or large-scale quantitative studies could validate the mechanisms identified here and provide stronger empirical generalisation. Thirdly, future work could investigate how emerging technologies support the structuring, validation and sharing of sustainability data across supply chains, with particular attention to how such technologies reinforce or transform data governance architectures. In addition, research should explore how accounting and supply chain management functions jointly operationalise cross-tier data governance, and how this integration shapes the accuracy and credibility of sustainability reporting, including Scope 3 disclosures. Finally, organisational and governance dimensions merit attention: how companies balance the costs of data management with the benefits of improved measurement validity, how integration of product and operational data affects stakeholder trust and risk perceptions and how sustainability goals are iteratively recalibrated in longitudinal settings as new and more granular data become available.

This study explored how sustainability data enables the realisation of organisational sustainability goals and, more specifically, how it underpins sustainability performance measurement in supply chains. By combining a conceptual framework with an in-depth case study of a global manufacturer, this study shows how sustainability drivers, supply chain capabilities and data flows interact across both product and operational domains. The findings highlight that sustainability strategies depend not only on regulatory and market drivers but also on continuous upstream supplier data and consistent downstream reporting. By framing the focal firm as a supply chain data hub, this study clarifies how incomplete or fragmented data flows undermine KPI validity and performance assessment, and how treating sustainability as a structured master data domain can provide a foundation for systematic supply chain sustainability measurement and management.

The study contributes to supply chain management and the literature on sustainability performance measurement in three ways. Firstly, it extends existing frameworks (e.g. balanced scorecard, SCOR, TBL and GSCM metrics) by demonstrating that their effectiveness rests on reliable sustainability data. Secondly, it adds conceptual precision by distinguishing and linking product and operational data, showing how their interaction affects measurement validity and stakeholder trust. Thirdly, it emphasises managerial implications: sustainability data must be governed with the same rigour as financial or product data, integrated into ERP and PDM systems and embedded into supplier relationships. The results also underscore that accurate sustainability reporting, including Scope 3 accounting, requires cross-tier data architectures and governance capabilities that integrate supply chain management and accounting functions. In doing so, the study positions sustainability data as a missing structuring element that enables competitiveness, reduces risk and strengthens credibility in supply chain sustainability performance measurement and management.

Thus, the contribution lies not only in identifying common data gaps but also in theorising sustainability data as the structuring mechanism that enables, constrains and ultimately determines the validity of supply chain sustainability performance measurement. This shifts the field towards understanding sustainability measurement as a data-centric, not merely indicator-centric, practice. By demonstrating how structured sustainability data creates measurable value for firms, customers, regulators and supply chain partners, the study directly contributes to the theme of adding value to measuring sustainability supply chain performance.