Developing reuse logic into product configuration: the operational approach for circularity

Interesting because – Manufacturing firms are increasingly exploring how to incorporate circular economy principles into product configuration processes. While prior research has examined reuse and remanufacturing strategies, less attention has been given to how such decisions can be systematically embedded within configuration systems. This study addresses this by developing an operational approach for integrating reuse logic into product configuration. Drawing on a design science methodology and an industrial case in pump manufacturing, the study introduces a product variant master structure that incorporates evaluation criteria for returned components to support their potential reintegration.

Theoretical value – The study contributes by linking component evaluation dimensions with configuration decision logic, thereby extending product configuration theory into a circular context. It demonstrates how different levels of evaluation complexity influence the degree to which reuse decisions can be standardized and embedded into configuration systems. Furthermore, the study shows that reuse potential and evaluation complexity jointly shape decision pathways, highlighting how increased complexity constrains configurability, while high reuse potential enables broader integration of used components.

Practical value – For managers, the study provides a structured approach to operationalizing reuse in configuration systems. Specifically, it enables firms to classify returned components and define clear rules for their inclusion, restriction or exclusion in product configurations. This supports more consistent decision-making, reduces reliance on expert judgment and facilitates scalable reuse practices. As a result, firms can improve resource efficiency while maintaining control over product performance and risk.

The transition toward circular and sustainable manufacturing is challenging the foundational logic of product development, which continues to operate within forward-oriented, linear value chains (Helo et al., 2024). While product lifecycle management (PLM) has traditionally coordinated engineering data and supported efficiency across design and production phases, its role is now evolving in response to circular economy (CE) requirements (Jakobsen and Tambo, 2025). To enable circular strategies, PLM must not only function as an administrative repository but be reconceptualized as an integrated lifecycle infrastructure that supports disassembly, traceability and the reintegration of components into future product configurations (Hapuwatte and Jawahir, 2021). However, despite institutional and regulatory pressure, many firms still operate with fragmented digital infrastructures and limited feedback mechanisms, which constrain their ability to operationalize circularity within core engineering and manufacturing processes.

A central barrier is that the digital architecture supporting PLM often lacks the capacity to model and manage the reuse of heterogeneous components, particularly when lifecycle histories are incomplete or component quality must be evaluated under uncertainty (Bianchi et al., 2022; Castiglione et al., 2024). As a result, PLM systems face a junction: they can remain static archives of disconnected product data or evolve into dynamic enablers of digital circularity. Realizing the latter demands new data governance models, tools for knowledge integration and operational logic that supports closed-loop strategies such as reuse and remanufacturing (Akinade et al., 2020). Among circular strategies, remanufacturing holds particular promise because it restores components to a state where functionality meets use application needs, thereby preserving functional integrity and extending product lifespans without quality compromise (Núñez et al., 2024). Yet this potential remains underutilized due to the lack of scalable methods for receiving, evaluating and reintegrating returned components into ongoing product development and manufacturing routines.

Current research and practice still show a pronounced gap between circular ambitions and operational execution. The literature tends to emphasize high-level design-for-disassembly principles or recycling scenarios, whereas the operational processes required to assess component condition and determine reuse eligibility are often overlooked (López-Torres et al., 2019). For circularity to become routine rather than an exception, evaluation and reuse pathways must be embedded within the digital infrastructure of product development, so that circular decisions can be made systematically, repeatably and at scale, rather than relying on ad hoc judgment.

This study addresses that operational gap by leveraging existing product configuration logic to support component reuse. Product configurators, rule-based expert systems used to manage modular product architectures, already support the complexity of variant-rich environments (Hvam et al., 2008; Haug et al., 2019). These systems therefore provide a promising foundation for integrating reuse logic, because decisions about component inclusion can be encoded at the level of individual modules and subassemblies. However, current configuration practices remain primarily designed for forward engineering, assuming new components with complete metadata (Kristjansdottir et al., 2018). They often do not address the reverse flow of used components, nor the decision structures required to classify, assess and reintegrate them. The opportunity, then, lies in adapting configuration systems that are already embedded in many industrial environments, so that they can also support circular use cases, provided that new frameworks for reuse evaluation, lifecycle data structuring and embedded decision logic can be developed. Importantly, this also requires translating expert knowledge, often tacit and dispersed into systematic assessment criteria that can be represented and executed digitally.

Against this backdrop, the present study investigates how reused components can be systematically reintegrated into product configuration systems to support circularity. The research draws on a Design Science Research (DSR) approach and is grounded in an industrial case study of an original equipment manufacturer (OEM). Specifically, it explores how lifecycle knowledge, evaluation logic and PLM infrastructures can be aligned to make component reuse a scalable and routine part of digital product configuration. The central research question guiding this work is:

By addressing this question, the study contributes to manufacturing technology management research in three ways. First, it extends the CE and PLM discourse by specifying the operational conditions under which reuse and remanufacturing can be enabled digitally, rather than treated as peripheral end-of-life activities (Akinade et al., 2020). Second, it advances product configuration research by conceptualizing and demonstrating how configurator logic can be adapted from a forward-only assumption set to one that accommodates the reverse flow and uncertain quality of used components (Hvam et al., 2008; Haug et al., 2019; Kristjansdottir et al., 2018). Third, it provides a practice-oriented pathway for firms seeking to move from fragmented lifecycle data toward actionable reuse decisions through integrated PLM and configuration infrastructures (Hapuwatte and Jawahir, 2021; Bianchi et al., 2022; Castiglione et al., 2024).

CE principles aim to close material loops and extend product lifespans through reuse, remanufacturing and refurbishment. In this study, component reuse and product disassembly are emphasized as particularly relevant strategies for industrial manufacturers seeking to enhance resource efficiency (Muñoz et al., 2024). However, implementation requires more than reverse logistics. Manufacturers must ensure that product and component data are structured and accessible to support evaluation and reintegration into new configurations (Geissdoerfer et al., 2017). We define reuse as the utilization of a previously used component for a similar function, typically following checking, cleaning or minor repairs to ensure safe and effective integration (den Hollander et al., 2017). Remanufacturing refers to a documented industrial process involving disassembly, inspection and repair or replacement to meet performance standards equivalent to new (Ponte et al., 2021). In this study, reuse is treated as the objective, and remanufacturing as a key enabler that makes reuse viable. Empirical research shows that achieving circularity in practice demands rethinking design and product architectures to support disassembly and reuse (Diaz et al., 2022). Early design-phase decisions heavily influence downstream operations, including compatibility assessments between recovered parts and new variants. Identifying reuse opportunities depends on detailed data about prior use, conditions and technical specifications, data that are often missing or inconsistently documented (Badurdeen et al., 2018). Integrating circularity into environmental and operational systems requires mechanisms for capturing and managing disassembly-relevant data (Kristensen et al., 2021). Recent work highlights the need for context-specific configurations and monitoring mechanisms to enable tailored circular strategies (Prosman and Cagliano, 2022).

Product configurators are expert systems used to generate product specifications for sales and engineering (Hvam et al., 2008; Zhang et al., 2013; Haug et al., 2019). They digitally model product family structures, guiding users through valid customization options and automating tasks such as bills of materials, pricing and production planning (Zhang et al., 2013; Kristjansdottir et al., 2018). By doing so, configurators reduce errors, improve quote speed and enhance specification accuracy (Haug et al., 2019). Configurators link product architecture to customization decisions (Bonev et al., 2015). Product architecture refers to abstract models mapping functional elements to physical components (Bonev et al., 2015). In circular contexts, architectures for remanufacturing must ensure mechanical compatibility between reused parts and new variants. The configurator retrieves feasible combinations from its knowledge base and translates architectural constraints into viable configurations (Zhang et al., 2013; Campo Gay et al., 2024). Reused components carry constraints from prior use, whereas new components do not. To manage this, components can be grouped by usage history, constraint type and mechanical limits to ensure consistency across configurations that include both reused and new parts (Bruun et al., 2015). The product variant master (PVM) approach structures variant-defining characteristics across product families to support complex configurations. A simplified example is illustrated in Figure 1.

PVM explicitly maps functional requirements, component variants and configuration constraints to streamline configurator logic (Hvam et al., 2008; Zhang et al., 2013). This is especially important for reused components, which inherit constraints from prior use (Kristjansdottir et al., 2018; Castiglione et al., 2024). For example, a reused product may be limited in materials or performance ranges, whereas a new version is more flexible. By encoding such dependencies, the PVM enables the configurator to apply recovery data and historical constraints systematically (Bonev et al., 2015; Akinade et al., 2020), improving feasibility, reducing configuration errors and supporting sustainable reuse (Campo Gay et al., 2024).

Circular economy research highlights reuse and remanufacturing as central value-retention strategies, but practice depends on structured component data and systematic evaluation routines that enable reintegration into new variants, capabilities often weakened by missing or inconsistent condition and use-history information and by limited embedding of monitoring mechanisms in operational systems (Geissdoerfer et al., 2017; Diaz et al., 2022; Prosman and Cagliano, 2022; Muñoz et al., 2024). Product configuration research shows how configurators generate feasible variants by translating architectural constraints, yet typically assumes new components with complete metadata, offering limited support for reverse flows and uncertainty-driven constraints associated with reuse (Kristjansdottir et al., 2018; Haug et al., 2019; López-Torres et al., 2019).

This study addresses the gap by operationalizing reuse as executable configuration logic, formalizing eligibility, compatibility and constraint propagation during variant generation, and by positioning PVM as the modeling backbone because it explicitly links functional requirements, component variants and constraints, enabling lifecycle- and recovery-derived constraints to be embedded systematically in configurator decisions (Hvam et al., 2008; Zhang et al., 2013; Akinade et al., 2020; Campo Gay et al., 2024).

This study adopts a DSR approach to develop and evaluate artefacts that support the integration of reused components into product configuration systems (Gregor and Hevner, 2013; vom Brocke et al., 2020). DSR is suited for addressing complex socio-technical problems through the iterative development of practically applicable artefacts grounded in empirical inquiry. The research design followed three interconnected cycles. The relevance cycle ensured alignment with the case company's practical challenges through longitudinal engagement, including participant observation and informal interactions that reveal existing reuse barriers, organizational routines and system constraints. The rigor cycle connected these insights to the scientific knowledge base, drawing on literature on product configuration, circularity and remanufacturing to inform artefact conceptualization. The design cycle consisted of iterative loops of empirical exploration, inductive coding, artefact development and expert evaluation. This iterative structure enabled continuous refinement of the complexity framework, governance guide and configuration integration steps, ensuring that artefact development remained grounded in both empirical observations and theoretical insights.

The case company is a global leader in mechanical manufacturing, headquartered in Denmark, employing ∼20,000 people across 60 countries and producing over 17 million products annually. Its strategic focus includes developing customized, sustainable solutions that reduce resource consumption. In 2001, the company launched a product configuration system, built on SAP, to improve inquiry response times, production efficiency and market position (Kristjansdottir et al., 2018). Its product portfolio and global scale provide insight into the operationalization of circular strategies under increasing sustainability expectations. Two researchers acted as non-interventionist participant observers and were granted unrestricted access to facilities and internal systems. The observers were not assigned to any specific department or project-related tasks but were embedded in the organizational setting daily to facilitate the natural emergence of data through continuous engagement. Employees were fully briefed on the academic purpose of the study, the observer roles and the voluntary nature of participation. Data collection and analysis proceeded iteratively, allowing concepts to emerge and be refined over time.

Primary data were collected through 10 semi-structured interviews with informants across functions, including product development, PLM, materials and circularity. These individuals were selected based on their domain-specific knowledge relevant to disassembly practices, product architecture, material evaluation and configuration modeling. The aim was to ensure a broad yet focused representation of expert perspectives that influence the integration of reused components into the product configuration. This ensured coverage of relevant informational and knowledge requirements related to reuse. The interview protocol began with open-ended questions about practices for assessing, disassembling and reintegrating components. While early interviews sought to identify general knowledge needs for component reuse, later sessions focused more narrowly on themes emerging from prior rounds of analysis. All interviews were audio recorded and fully transcribed, generating a substantial qualitative dataset for analysis. In addition to interviews, supplementary data was collected through participant observations and informal conversations with employees at the case site. Observational field notes and researcher memos were maintained throughout the study to capture tacit knowledge and contextual details that might not surface during formal interviews. This combination of semi-structured interviews, theoretical sampling and embedded observation enabled the collection of a rich and triangulated dataset, well-suited for analysis. It ensured that insights were not only grounded in the discourse of individual experts but also reflected in the observed practices, organizational context and underlying information systems supporting circular configuration activities (Yin, 2009).

The analysis employed inductive coding techniques, drawing inspiration from grounded theory (Charmaz, 2006; Corbin and Strauss, 1990), to inform artefact development and refinement. While not adhering to a strict school of grounded theory, the study adopts its core principles to support inductive theory-building from empirical material. This approach was deemed suitable given the limited prior theorization on how reuse-related decisions can be operationalized in digital configuration systems. The emphasis was placed on generating context-sensitive categories emerging from practice, rather than applying a predefined analytical structure. This enabled the study to explore not only what information is needed to enable reuse but also how this information is interpreted, structured and mobilized by various actors. The coding process followed three interrelated phases, open, axial and selective coding, supported by memo writing and comparison (Lawrence and Tar, 2013). Iterative memoing and revisiting of transcripts helped refine conceptual boundaries and reduce the risk of premature closure. Importantly, this process was embedded in the design cycle of the DSR methodology. Coding outcomes informed artefact development by identifying patterns in evaluation logic, knowledge needs and governance challenges. These insights shaped the content and structure of the complexity framework and the subsequent guides. The goal was not to generate a grounded theory but to extract actionable, empirically grounded input for artefact construction. Figure 2 illustrates this analysis process and its role in the study's design activities.

The analysis began with open coding of six interview transcripts to identify actions, challenges and knowledge practices related to component reuse. Codes were generated inductively without predefined categories to ensure close grounding in participants' expressions (Corbin and Strauss, 1990). Iterative comparison across transcripts revealed three dominant patterns: (1) reliance on tacit visual assessment, (2) significant gaps in product history data and (3) absence of structured evaluation criteria for returned components. These insights formed the empirical foundation for the subsequent axial coding phase, as illustrated in Figure 2.

In the axial coding phase, open codes were consolidated into higher-order categories that captured key dimensions shaping reuse feasibility. Eleven categories emerged, including component evaluation practices, inspection challenges, data and traceability requirements, electronic reuse barriers, organizational and strategic issues, and regulatory considerations. Analytical memos documented relationships between categories, for example, how inspection challenges stemmed from missing operational data, revealing systemic dependencies across technological, informational and organizational domains. These categories provided the structural backbone for the conceptual framework developed in the design cycle.

Selective coding integrated the axial categories around a single core concept: Enabling Systematic Component Reuse in Circular Product Systems. This construct captures the interplay between evaluation logic, traceability structures, regulatory expectations and organizational alignment required to support scalable reuse. Interviewees emphasized that missing lifecycle data and fragmented information systems increase evaluation complexity and reinforce dependence on expert judgment. The core category therefore informed a structured classification of components by evaluation complexity, which became central to the reuse framework. Categories related to technical ambiguity, information fragmentation and external constraints collectively shaped the model. Through this abstraction, the conceptual framework emerged directly from the empirical material rather than being imposed a priori.

Finally, the three coding stages played distinct roles in artefact development: open coding surfaced initial knowledge gaps, axial coding organized them into actionable design dimensions and selective coding synthesized them into the core concept guiding the development of the complexity framework, governance guide and configurator integration steps. Thus, each artefact element can be traced back to empirically identified challenges. Figure 2 visualizes this translation process, illustrating how empirical observations were progressively abstracted into structured evaluation logic and configuration-level design principles.

The following section presents the artefacts developed through the design cycle, informed by the empirical inputs of the relevance cycle and refined through iterative coding and conceptual synthesis.

The case examines how returned industrial circulator pumps can be assessed and reintegrated into product configuration workflows. Although around 120 warranty returns per month provide a stable source of potentially reusable mechanical components, current practices default to scrapping due to the absence of standardized evaluation methods and reuse protocols. The case therefore serves as an empirical setting for developing artefacts that structure reuse decisions. It shows that implementing reuse requires aligning modular product architecture with assessment routines and addressing barriers such as fragmented disassembly knowledge, incomplete component histories and the need to adapt configuration logic for refurbished parts. By using the PVM as a backbone for formalizing reuse criteria and component classification, the case demonstrates how reuse can transition from ad hoc exception to a systematic, scalable element within existing configuration systems.

A PVM was developed to provide the structural basis for classifying component complexity and integrating reuse logic into the configuration system. The PVM formalizes domain knowledge by mapping variant classes and hierarchical relationships across the pump architecture, functioning both as a knowledge-elicitation tool and as a structured representation for assessing reuse potential. Based on a full Bill of Materials of 730 components, the model was simplified into four functional domains: (1) documentation and labels, (2) pump assembly, (3) control and electronics and (4) packaging and logistics (Figure 3), to enhance transparency and support component-level evaluation. This modular decomposition aligns with established configurator practices and clarifies where reuse decisions are most relevant. In particular, the pump assembly and control and electronics domains contain the components with highest structural and functional variation, making them central targets for reuse classification. The PVM thus provides the architectural scaffold needed to link product structure with assessment routines and to embed verified reused components into configuration logic.

Mapping the product's modular architecture through the PVM enables a structured understanding of where complexity is concentrated and where reuse potential may be realized. However, structural representation alone is insufficient. To support actionable decisions, components must also be classified by the complexity of evaluating their reusability. The next section introduces a classification framework that builds on the PVM to operationalize reuse assessment by linking component complexity to evaluation demands.

This section introduces an evaluation complexity framework for systematically assessing component reusability in circular product systems. The framework is grounded in interviews with product developers, engineers and circularity specialists, who identified recurring challenges in evaluating returned components, including missing operational data, reliance on tacit expertise, inconsistent inspection routines and system-level integration barriers. These findings indicated the need for a structured method to classify components according to the effort required to reach a reliable reuse decision. Rather than evaluating intrinsic component quality, the framework focuses on the procedural and informational demands associated with reuse assessment. It thus provides a decision-support structure that distinguishes components with low evaluation burden from those requiring extensive data, expertise or validation.

The framework is built around five empirically derived dimensions: data dependency, assessment effort, reuse risk, integration barriers and knowledge codification. Each component is evaluated across the five dimensions on a scale from 1 (low complexity) to 5 (high complexity). The composite complexity score is calculated as the unweighted average of these dimensions. Equal weighting was a deliberate design choice to ensure operational simplicity and practical usability across organizational contexts. While the five dimensions emerged inductively from empirical observations, no single dimension dominated evaluation decisions. The framework therefore avoids imposing predefined prioritization logic, but organizations may adapt weighting schemes to reflect context-specific risk tolerance or strategic priorities. The complexity scoring framework is presented in Table 1.

To improve clarity, Table 2 provides illustrative examples of what characterizes low and high levels across each evaluation dimension introduced in Table 1. Drawing on components from the empirical case, the examples illustrate how product characteristics influence the assessment of evaluation complexity.

The scoring is typically performed by a cross-functional team composed of domain experts, such as product engineers, service technicians, quality specialists and circularity professionals. These experts draw on design specifications, field experience, operational data availability and documentation reviews to assign a score from 1 to 5 for each dimension. For each component, the five individual scores are compiled and averaged to compute a single Complexity Score:

The result is a numerical value between 1.0 and 5.0 and to facilitate practical application, complexity scores are interpreted using a three-tier classification that reflects the relative ease or difficulty of conducting a reliable reuse assessment. These tiers are not used to exclude components from reuse but rather to tailor the level of scrutiny and the type of evaluation required.

1. Low complexity (1.0–2.0): These components require minimal data, can be assessed visually and pose little functional or safety risk. Evaluation is fast and repeatable, requiring limited expert input. Such parts are ideal for early-stage reuse programs due to low decision-effort and high scalability.

2. Medium complexity (2.1–3.0): Components in this range require structured assessments, such as partial disassembly, functional tests or cross-functional review. While not trivial, evaluation remains feasible using established procedures. These components are viable candidates when supported by appropriate tools and workflows.

3. High complexity (3.1–5.0): High-complexity components require advanced testing, hard-to-access data or expert-specific evaluation logic. Often safety- or regulation-critical, their reuse is possible but only under controlled, case-by-case conditions. These components may be deprioritized unless their material or strategic value justifies the effort.

This classification supports risk-informed reuse strategies, allowing firms to tailor decision processes and resource allocation based on evaluation complexity. Rather than prescribing whether to reuse, the framework indicates how much effort evaluation requires, guiding reuse pathway design, documentation priorities and system support. Scoring consistency may be ensured through expert workshops or cross-functional calibration. The framework remains adaptable to organizational context. Beyond decision-support, the framework serves a diagnostic function. Components scoring high in complexity may reveal broader systemic issues, such as undocumented procedures, poor data traceability or overly complex interfaces. These insights can inform design-for-reuse initiatives by targeting areas for complexity reduction through: Design Interventions, modularization, standardization or interface simplification to reduce complexity by making the product better suited for circularity. Information Enrichment, Better documentation, inspection routines, thereby improving the decision-making related to circular choices, by increasing available information. In this way, the framework not only guides current reuse practices but also supports long-term improvements in circular readiness. At the end of the evaluation process, each component receives a binary classification: Prioritize for reuse or avoid reuse. Components with low complexity and sufficient value are approved for reintegration into configuration systems, while others are routed toward recycling or flagged for future redesign. This classification acts as a key decision-point between reverse logistics, quality assurance and digital configuration platforms.

To validate the proposed complexity-based reusability framework in a real-world context, a pilot workshop was conducted with two senior product experts specializing in centrifugal pump systems. The purpose of the workshop was to apply the scoring methodology outlined above to actual pump components represented within the PVM model. Each component was evaluated across the five defined complexity dimensions. These assessments resulted in a quantified complexity score for each component. The scores were subsequently mapped in a complexity heatmap to visualize evaluation challenges and guide actionable reuse recommendations based on expert discussion and contextual knowledge. This pilot exercise not only confirmed the operational applicability of the scoring framework but also enabled initial insights into the feasibility of integrating reuse logic into product development and configuration environments. To operationalize the component complexity assessments, the resulting scores from the pilot workshop were visualized in a complexity heatmap in Figure 4. This heatmap captures each component's dimension-specific complexity, providing a structured overview of where evaluation bottlenecks are likely to occur. By averaging the five-dimension scores for each component, an overall complexity score was derived, which served as the basis for positioning the components within the reuse decision framework. Component scores were used to support decision-making by highlighting the level of complexity associated with evaluation, enabling practitioners to identify components that could be prioritized for reuse or that required further review based on organizational priorities and technical feasibility. Components were discussed in terms of evaluation feasibility and flagged accordingly as either prioritize for reuse or avoid reuse. High-complexity components were subject to expert review to determine whether reuse would be viable, while others were identified as candidates for redesign or information enrichment to lower evaluation barriers. These results serve not only as a basis for reuse decision-making but also as input for designing and sequencing the disassembly process. Components that score low on evaluation complexity and are suitable for reuse are prioritized and sorted during disassembly to enable efficient verification workflows. Once verified, these components can be reintegrated as selectable options within the product configuration platform, where users may choose between new or reused parts, thereby operationalizing circularity directly within the configuration process.

1. Pump House received a complexity score of 1.6, reflecting minimal assessment effort, high visibility of condition and full compatibility with current configurations. Due to its low complexity and structural relevance, this component should be prioritized for reuse.

2. Pump Head received a complexity score of 2.0, indicating straightforward evaluation requirements and well-documented integration characteristics. Its physical durability and ease of inspection make it a strong candidate to be prioritized for reuse.

3. Motor and Stator scored slightly higher on assessment effort, resulting in a medium complexity score of 2.2. While structurally durable, verifying performance may require additional testing based on inspection data and expert judgment. Given their technical value and manageable complexity, these components can be prioritized for reuse.

4. Control Box, with a complexity score of 3.2, represents the most evaluation-intensive component. High complexity stems from data dependency, embedded electronics and undocumented software configurations. While reuse may be technically feasible, the lack of standardization and high verification effort currently suggests that this component should be avoided for reuse unless complexity can be reduced through redesign or enriched information.

5. Sensor Package with a score of 2.6, exhibits medium complexity, primarily due to moderate data access requirements and the need for specialized assessment procedures. Although reuse may be technically possible with improved calibration documentation or test routines, the component's low functional and economic value does not justify the evaluation effort. Therefore, this component should be avoided for reuse. This initial pilot demonstrates the practical feasibility of translating complexity-based evaluations into actionable reuse pathways within industrial settings. By combining expert-driven scoring with a structured assessment process, the approach facilitates informed design feedback and systematic end-of-life decision-making. Establishing component scores required eliciting tacit knowledge and translating it into standardized evaluation criteria. Furthermore, the integration of complexity results into disassembly planning and product configuration systems highlights the potential for digital platforms to facilitate circular practices at scale.

Scaling component reuse requires more than technical validation; it demands consistent, transparent and auditable quality decisions across organizational units. Based on empirical insights from the case company, this section presents a generalized design guide for building a quality governance framework that supports circularity across product families and business contexts. The guide focuses on three foundational steps: (1) defining reuse criteria, (2) determining how and when relevant data is collected and (3) establishing qualification thresholds that guide decision-making. Each step is described in terms of why it matters, what companies should ask internally, how they might apply it, and the expected outcomes and challenges. This structure is intended to facilitate both implementation and cross-functional dialogue. Table A1 ( Appendix) provides a consolidated overview of these steps in a format suitable for adoption, internal communication or capability audits.

In contrast to traditional quality assurance frameworks, where qualification primarily verifies conformance of new components against stable specifications and controlled production conditions, reuse governance must qualify heterogeneous components with uncertain histories, variable degradation pathways and incomplete data trails (Bravi et al., 2019; Andres-Jimenez et al., 2020). Reuse therefore requires a distinct governance logic that (1) treats “fitness-for-purpose” as contextual rather than absolute, reflecting dependence on use history and application risk (Fontana et al., 2021), (2) makes uncertainty explicit through traceable evidence requirements and role-based verification to ensure auditable decision rationales (Andres-Jimenez et al., 2020; Pratapa et al., 2022) and (3) institutionalizes graded qualification outcomes rather than a single pass/fail decision to align component status with differentiated reuse pathways (Fontana et al., 2021; Ponte et al., 2021). Put differently, whereas new-component QA manages variation around a known baseline (Bravi et al., 2019), reuse governance must manage risk and accountability when the baseline itself is unknown or shifted by prior use, making auditable justification and differentiated reuse pathways essential to scale circular operations (Pratapa et al., 2022).

The guide is applicable across diverse component types and product categories, but its strength lies in its adaptability. Organizations may start with pilot-level implementation for high-volume or high-value components and gradually institutionalize governance routines as reuse maturity increases. Importantly, the framework also supports alignment with product design processes, warranty strategies and downstream traceability requirements, forming a bridge between quality assurance and product lifecycle thinking.

The operationalization of component reuse within a circular product configuration system necessitates a structured, traceable and knowledge-supported process flow. Based on empirical analysis and grounded in the case study context, this section presents a two-tiered process architecture designed to enable the systematic evaluation and reintegration of used components. The process is divided into two figures: a high-level lifecycle model (Figure 5) that captures the sequential states of component assessment and reuse eligibility and a detailed procedural flow (Figure 6) that outlines the operational logic and decision rules governing the reuse evaluation. Together, these layers represent the functional integration between product return handling, quality assessment and digital product configuration systems.

The overarching process model presented in Figure 5 delineates the lifecycle of a component from its initial submission as a reuse candidate to its eventual approval, archival or rejection.

The process begins in the Candidate stage, where a returned component is flagged for potential reuse. This initiation may be triggered by warranty return procedures, internal service inspections or proactive disassembly operations. Upon entry, the component transitions to the UnderEvaluation phase, during which its suitability for reuse is assessed based on structural condition and traceability data. If the component meets predefined quality and data requirements, it advances to the Verified state. In this state, the component is confirmed as functionally intact and traceable, making it eligible for reuse consideration. However, if the evaluation identifies critical flaws, such as irreparable damage, lack of historical data or incompatibility, the component is classified as Rejected and routed toward recycling or disposal. Once a component is Verified, it may follow several trajectories. In the optimal case, it is Approved for reuse, assigned a unique reuseID and integrated into the product configuration system. These identifiers function as boundary objects that transmit expert judgments across systems and functions, enabling knowledge reuse and digital interoperability. This integration ensures that verified reused components can be selected in subsequent product variants. Alternatively, a component may enter a ReEvaluation phase if new operational data emerge, or reuse rules are updated. Over time, verified components may also reach an Archived state when their functional lifespan is exhausted, or reuse eligibility expires. Archival maintains traceability while ensuring that outdated components do not re-enter the configuration logic. This high-level process architecture thus forms a closed-loop governance structure that links circular ambitions to operational and quality assurance requirements.

For reuse to function as an integrated part of circular production, component eligibility must be embedded in the digital systems that govern product design, configuration and production planning. Without this integration, reused components remain operationally disconnected, even if they are technically viable and quality-approved. This section presents a four-step guide for embedding reuse logic into configuration systems. It outlines how to: (1) model reused components in the configuration structure, (2) apply conditional rules to govern when reuse is appropriate, (3) link digital configuration with verification and inventory systems and (4) update reuse logic as products and systems evolve. The goal is to make reuse viable in the product configuration process, supported by traceable logic, live data and adaptive rule systems. Table A2 ( Appendix) summarizes the steps in a structured format that supports application across diverse configurator platforms and product types.

Together, these integration steps enable organizations to shift from reactive, case-by-case reuse toward embedded, system-supported reuse logic. This not only enhances traceability and consistency but also reduces the operational burden of managing reused components across design, engineering and sales. Importantly, configuration integration also enables real-time alignment with inventory and quality data, supporting informed variant creation. This integration guide supports firms in extending reuse beyond isolated pilot efforts by embedding decision logic, traceability and validation mechanisms into core configuration workflows. As such, it supports the development of the digital infrastructure needed to enable scalable circular product design.

To ensure scalable and consistent integration of component reuse into product configuration workflows, a formal notation is required that bridges the product architecture, reuse assessment logic and system-level configurator rules. This notation must enable a clear distinction between reused and new component variants, support traceability and be compatible with the logic of rule-based configurator systems. As illustrated in Figure 6, the PVM model was extended to include both factory-new and verified reused variants for key components across the pump architecture, such as the pump housing, pump head, motor and stator, control box and sensor package. These reuse options are exposed as user-selectable configuration choices and are embedded into the product structure as part of the standard configuration hierarchy.

Figure 6 shows how reuse logic changes configuration outcomes. A verified and traceable reused component can be made selectable alongside the factory-new alternative when its condition, compatibility and availability satisfy the defined rules. For instance, a reused pump housing may be directly selectable because it has low assessment complexity and stable interfaces, whereas a tested motor assembly may require additional performance verification before release. Conversely, components with missing use-history data, uncertain software compatibility or disproportionate assessment effort, such as control boxes or sensor packages, may be blocked, routed to engineering review, or replaced by a new component. Reuse logic therefore extends the configurator from selecting feasible new variants to governing whether reused components are allowed, conditionally allowed, escalated or excluded.

The findings of this study highlight that integrating reused components into product configuration systems is fundamentally a knowledge-intensive process. While the artefacts developed here, provide structured mechanisms for operationalizing reuse, their effectiveness depends on how organizations capture, formalize and maintain the knowledge required to evaluate returned components. Across interviews, tacit knowledge emerged as a consistent foundation for reuse decisions. Experts relied on nuanced visual cues, contextual interpretation of wear patterns, historical familiarity with failure modes and embedded organizational memory. These elements resist full codification and cannot be standardized across products or firms. This insight underscores a central implication: the boundary condition for system-supported reuse is not configuration logic alone, but the capacity of the organization to transform tacit expertise into actionable, maintainable digital knowledge.

Knowledge management (KM) provides a theoretical foundation for understanding this boundary. KM in configuration environments focuses on managing explicit, rule-based knowledge that supports forward engineering. However, circular contexts extend these requirements. Reuse decisions involve knowledge about component usage histories, degradation pathways, disassembly practices and compatibility constraints, knowledge that is often tacit, situational and dispersed across organizational actors (Ul-Durar et al., 2023). Supporting such decisions therefore requires continuous processes for capturing, updating and validating domain-specific insights on wear, failure modes and operational context.

The KM literature emphasizes that this transformation requires systematic routines for sharing lifecycle knowledge across internal functions and external partners (Heeß et al., 2024). Research also highlights the importance of shared vocabularies and governance structures to ensure consistent interpretation of reuse-relevant information (Zhang and Seuring, 2024). Without such shared semantic structures, knowledge remains siloed, leading to inconsistent decision-making and limited scalability of reuse practices. Moreover, circular manufacturing depends on lifecycle-oriented information-sharing routines that span design, use, recovery and reintegration phases (Jäger-Roschko and Petersen, 2022). Our findings confirm this requirement; many evaluation challenges came not from the absence of assessment tools, but from missing usage histories, fragmented maintenance data or undocumented expert reasoning, all of which directly increase evaluation complexity.

These insights align with the knowledge transformation process articulated by Shafiee et al. (2018), shown in Figure 7, which illustrates how distributed product and process information becomes actionable configuration knowledge. The model shows four phases: scoping, knowledge acquisition, development and validation, and documentation and maintenance, which describe how fragmented knowledge must be iteratively structured before it can support configuration systems.

Our empirical findings support this framework. In the scoping phase, product families were prioritized not only based on configurator relevance but also on their potential for generating reusable components. Knowledge acquisition required engagement with engineers and circularity specialists to elicit tacit understanding of degradation patterns and reuse feasibility. Development and validation were conducted iteratively with domain experts, who helped refine the complexity scoring logic and qualify evaluation criteria. Documentation and maintenance emerged as a pivotal requirement: without structured routines for updating reuse criteria and traceability information, the logic embedded in configuration systems quickly becomes obsolete.

Thus, the Shafiee et al. (2018) framework provides a conceptual explanation for why the artefacts developed in this study are necessary but not sufficient: they operationalize the knowledge transformation cycle, but the quality of their output depends on the organization's KM maturity and its ability to codify tacit expertise.

Digital product passports (DPPs) offer a complementary infrastructure that supports KM by enriching the lifecycle information available during reuse assessment. DPPs consolidate diverse data, including material composition, repair history, usage conditions and compliance information, into a unified digital asset (Jensen et al., 2023; King et al., 2023). Our findings demonstrate why such infrastructures are necessary: missing or unverifiable operational histories were among the strongest drivers of evaluation complexity.

By providing structured lifecycle data, DPPs reduce uncertainty in reuse assessments and support more consistent interpretation of component condition (Götz et al., 2022; Berger et al., 2023). In OEM environments, where components move across suppliers, service providers and internal departments, DPPs allow verified operational and maintenance histories to be shared securely across actors (Chaudhuri et al., 2024). Their effectiveness, however, depends on interoperability, shared standards, governance mechanisms and role-based data access (Langley et al., 2023). Without these, DPPs risk becoming fragmented data repositories rather than enablers of circular decision-making. DPPs should therefore be designed form a systems-design perspective to realize the full potential (Christensen et al., 2025).

DPPs do not replace expert judgment or eliminate tacit knowledge. Instead, they augment KM processes by increasing lifecycle information availability and reducing avoidable uncertainty, thereby improving the conditions under which expert assessments are applied. The findings demonstrate that circular product configuration depends on the interplay between organizational knowledge, KM routines and digital infrastructures. The artefacts developed in this study provide structural mechanisms for operationalizing reuse within configuration systems, while KM theory explains why these mechanisms must be supported by systematic processes for eliciting, validating and maintaining both tacit and explicit knowledge. Within this architecture, DPPs function as a lifecycle data backbone that enhances traceability and supports consistent, cross-actor reuse assessments.

The central implication is that circular configuration is not merely a systems-design challenge but a knowledge governance challenge. Tacit knowledge remains unavoidable, but when structured through KM processes and complemented by DPP infrastructures, it can be systematically leveraged to enable scalable, repeatable and auditable reuse practices. For policymakers developing DPP frameworks, these findings suggest that regulatory initiatives should prioritize data structures that directly enable reuse eligibility, traceability and configuration-level decision logic, rather than focusing solely on material disclosure or compliance reporting. For managers in manufacturing firms, the findings provide a structured pathway for embedding reuse into existing configuration and PLM systems. The complexity framework supports prioritization of components for reuse, while the governance and integration guides outline how to align quality assurance, data collection, and configuration logic. Rather than treating reuse as an isolated end-of-life initiative, managers can use the proposed artefacts to institutionalize reuse as a scalable and auditable part of core product development processes.

This study is subject to several limitations that also define important directions for future research. First, the framework relies on expert-based evaluation and the elicitation of tacit knowledge within a single industrial context. Although this approach reflects realistic organizational practice, reuse assessments remain highly dependent on experience-based judgment that is difficult to formalize and scale. Future research should therefore examine how organizations can systematically elicit, codify and sustain tacit expert knowledge to support reliable reuse decisions in circular product systems. Second, the effectiveness of the proposed artefacts depends on organizational routines for knowledge acquisition, validation and maintenance. Variations in KM maturity may influence the scalability and long-term robustness of reuse logic embedded in configuration and PLM systems. Further empirical studies across firms and industries are needed to assess how KM maturity shapes reuse integration outcomes. Third, while DPPs are positioned as enabling infrastructures, they primarily facilitate structured lifecycle data and do not inherently capture tacit reasoning. Research is needed to explore how DPP architectures can incorporate, interface with or augment knowledge-intensive reuse assessments. Finally, circular value creation often spans multiple organizational actors. Future research should investigate governance structures and information-sharing arrangements that enable coordination of KM and DPP infrastructures across supply networks to support consistent, cross-actor reuse decisions. Table A3 ( Appendix) summarizes these research directions and associated challenges.

This study has explored the operational integration of reused components within product configuration systems, addressing a significant yet underdeveloped aspect of circular manufacturing. By employing a Design Science Research approach and conducting an in-depth case study, the research demonstrates how product and component data can be structured to support systematic disassembly, evaluation and reintegration of end-of-use products. The proposed complexity-based evaluation framework enables the classification of components according to data requirements, assessment effort and reuse risk, thereby supporting informed decision-making at scale. Furthermore, the development of a reuse-aware PVM and object-oriented configuration notation illustrates how digital configurators can accommodate both new and reused components within existing product architectures. Taken together, these contributions offer a practical pathway for embedding circularity into configuration processes, enhancing product traceability, reducing material waste and extending component lifecycles. This has direct implications for the KM and lifecycle data infrastructures that underpin it, emphasizing the need for flexible configuration logic, reliable capture and maintenance of tacit knowledge and the integration of reuse-oriented data structures facilitated by DPPs. Future research should therefore explore how such reuse-oriented frameworks can be scaled and sustained within PLM and configuration environments with varying levels of KM maturity and evolving DPP infrastructures, and how these organizational and data foundations shape the long-term robustness of circular configuration logic. The findings hold relevance for both practitioners and scholars aiming to align configuration logic with emerging sustainability demands and CE objectives.

All participants of the study were aware of the studies intentions and agreed to be recorded to collect data as input for this study.

The supplementary material for this article can be found online.