This study systematically explores the pathways through which smart packaging (SP) enhances logistics capabilities (LCs) to simultaneously address two key challenges for the circular economy (CE) – physical value and information loss. While the link between SP and circularity is recognised, the specific pathways through which item-level data reconfigures LCs and leads to circular outcomes remain underexplored. This paper clarifies these connections, detailing how and why SP serves as a foundational enabler for the strategic transition from reactive recovery to proactive circular supply chains.
Following a theory-informed systematic literature review (SLR), 59 articles were analysed using axial coding and multi-layered framework synthesis. The research utilises packaging functionalities (PFs) and LCs as micro-level and macro-level units of analysis, respectively, while adopting the R-imperative framework as a strategic lens to map circular outcomes.
The study identifies four thematic pathways: (1) proactive logistics management and dynamic optimisation, (2) autonomous and scalable reverse flows, (3) systemic trust and quality of material streams, and (4) collaborative value co-creation. The findings demonstrate that SP-enhanced LCs address the challenges of achieving circularity by mitigating physical value loss through functional preservation and information loss through item-level visibility. Information-management capabilities are the foundational antecedents allowing supply-management, demand-management, and coordination capabilities to mitigate the information asymmetry inherent in closed-loop systems.
This research develops a novel multi-layered conceptual framework explaining the “how” and “why” behind SP-enabled higher-order circularity. It identifies the theoretical boundaries of logistics-led interventions, highlighting the limits of item-level intelligence in supporting design-led strategies like repair and refurbish.
1. Introduction
Digitalisation has evolved packaging from a passive container to a strategic asset for supply chain (SC) efficiency (Perotti et al., 2025). This shift is enabled by SP, which integrates intelligent, active, and connected technologies into conventional packaging, such as radio-frequency identification (RFID) tags, QR codes, and sensors (Endara et al., 2023; Lydekaityte and Tambo, 2020). By enhancing traceability and enabling real-time data exchange (Chen et al., 2020), SP represents a technological advancement that redefines the business model and creates value in logistics and supply chain management (LSCM). As sustainability becomes a standard requirement, the CE has moved to the forefront, aiming to reduce waste and resource consumption by keeping products and materials in use for as long as possible (Kirchherr et al., 2017).
Despite the direct influence of packaging and logistics operations on a firm's efficiency and effectiveness (Dev et al., 2021; Gatenholm et al., 2021), the specific processes through which SP reconfigures logistics activities to address challenges of circularity remain fragmented and underexplored. Within the CE transition, LCs are essential competencies used to reconfigure resources and gain competitive advantage in response to circular challenges (Sandberg and Abrahamsson, 2011). Integrated LCs are a dominant influence on a firm's ability to build resilience and manage the inherent complexity of circular systems (Mandal et al., 2017). Consequently, SP serves as a potential enabler for enhancing these LCs and supporting the CE transition by facilitating timely access to information and coordination (Beske, 2012).
While the literature recognises SP's contribution to circularity, existing reviews often lack a detailed understanding of the “how” and “why” behind this connection (Chen et al., 2020; Palazzo et al., 2023). The value of technology is only realised when a clear link to the capabilities it enhances is established (Riggs et al., 2023); yet research on SP's role in CE often remains superficial (Abekoon et al., 2024; Yang et al., 2024). Notably, a strong growth trend in publications over the last five years has provided the critical mass of evidence required for this SLR. To fill this gap, this study provides a theory-informed SLR that synthesises fragmented knowledge into a rigorous analytical foundation (Durach et al., 2017; Kembro et al., 2025).
We conceptualise the CE transition as being fundamentally rooted in the simultaneous mitigation of physical resource loss and information asymmetry across the SC. While design-led strategies prioritise waste prevention, the operational core of circularity – recovering and extending product lifecycles – is often hindered by logistics-related uncertainties (de Lima and Seuring, 2023). SP addresses these challenges by mitigating physical value loss through functional preservation and information loss through item-level visibility. In this study, “how” refers to the process of reconfiguring LCs from reactive functions into proactive management systems (Jayarathna et al., 2024), while “why” addresses the theoretical logic of reducing material loss and information asymmetry to enable circularity. Following this, our research addresses two primary research questions.
How does SP enhance LCs?
What CE strategies can be supported by these enhanced LCs?
Our core contribution is a pathway-based framework that explains the systemic transition from SP data to circular outcomes. This study identifies the thematic pathways from micro-level PFs to macro-level LCs and their subsequent circular impacts. Specifically, we argue that SP enables the CE transition by reconfiguring LCs. Consequently, it responds to calls for a more analytically rigorous lens by integrating the R-imperative framework, which facilitates the delineation between design-led strategies and logistics-led interventions required to operationalise circularity at scale (de Lima and Seuring, 2023; Jegen et al., 2025).
2. Conceptual background
This section synthesises three interconnected domains: SP-enhanced PFs, LCs and CE. To bridge the gap between technology and strategic outcomes, we utilise PFs as the micro-level unit of analysis to explain how item-level data generation reconfigures macro-level LCs. Then, we adopt the R-imperative framework to move beyond descriptive accounts to provide an operational understanding of why SP-enhanced interfaces facilitate the logistics flows (via LCs) required for a CE transition.
2.1 The micro-level: smart packaging-enhanced functionalities
This study uses SP-enhanced PFs as the micro-level unit of analysis where physical items reconfigure macro-level logistics processes. These functionalities represent the touchpoints where physical products interact with logistics operations (Robertson, 2016; Young et al., 2023). Specifically, Robertson's (2016) classification is adopted as it captures the functional intersection between the package and the SC (da Silva Ponciano et al., 2025; Sadeghi et al., 2022), whereas material-based classifications fail to address this relationship required for CE transitions (Young et al., 2023). As synthesised in Table 1, the integration of active and intelligent technologies transforms these static interfaces into dynamic data nodes capable of sensing, communicating, and reacting to an item's status (Lydekaityte and Tambo, 2020; Sadeghi et al., 2022).
SP enhancement of PFs (classification criteria adapted from Robertson (2016))
| PF | Description | SP enhancement – active packaging (AP) and intelligent packaging (IP) |
|---|---|---|
| Protection | To provide protection from external and internal impacts (barrier protection, mechanical protection), facilitate preservation from spoilage, and ensure security against tampering, pilferage, theft, fraud, and counterfeiting throughout the SC (da Silva Ponciano et al., 2025; Robertson, 2016; Young et al., 2023) | AP – Facilitates interactions between the item and its internal environment to absorb or release substances, thereby keeping items fresher for longer while guaranteeing nutritional attributes (Abekoon et al., 2024; Sharma et al., 2023) |
| IP – Continuously monitors, records, and verifies product integrity through real-time sensing of internal and environmental conditions, integrity/tamper detection, and traceability/authentication features, enabling early risk detection and rapid intervention to maintain product protection (Abekoon et al., 2024; Ahmed et al., 2018; Sadeghi et al., 2022) | ||
| Communication | To deliver diverse information in various forms to different SC stakeholders, including identifying and informing about the product, attracting attention and influencing perception, decision-making, branding, marketing and advertising (Lydekaityte and Tambo, 2020; Robertson, 2016; Sadeghi et al., 2022; Young et al., 2023) | AP – Indirectly contributes through functional compounds that may induce visible state changes indicating product condition (Barone et al., 2021; Mesci et al., 2024; Sadeghi et al., 2022) |
| IP – Converts product and environmental states (e.g. temperature, gas composition, location) into digital signals, enabling real-time monitoring, tracking and tracing, quality alerts, accessible product information, and data-driven decision-making across supply chain stakeholders and consumers (Abekoon et al., 2024; Sadeghi et al., 2022; Young et al., 2023) | ||
| Convenience | To facilitate convenient handling including logistics (storage, transportation, distribution, warehousing, and stacking), retail (placement on shelves, inventory), consumption or utilisation (opening, re-opening, portability, storability), and disposal (Robertson, 2016; Sadeghi et al., 2022; Young et al., 2023) | AP – Indirectly contributes to maintain optimal internal package environment, prolonging product viability which may indirectly facilitate storage and distribution operations. (e.g. reducing the need for frequent manual quality inspections) (Firouz et al., 2021; Sadeghi et al., 2022) |
| IP – Uses non-line-of-sight scanning and wireless data exchange to automate inventory capture and data flows, enabling real-time inventory management, streamlined handling operations, and data-driven optimisation of logistics and return processes (Abekoon et al., 2024; Sadeghi et al., 2022) | ||
| Containment | To serve as a source of containment by enclosing, enveloping, and holding goods together securely (da Silva Ponciano et al., 2025; Robertson, 2016; Sadeghi et al., 2022) | AP – Regulates the internal package environment (e.g. oxygen, moisture, gas) to stabilise product conditions and indirectly support containment by preventing degradation that could compromise package integrity (da Silva Ponciano et al., 2025) |
| IP – Affixes digital identifiers to primary, secondary, and tertiary units to monitor package integrity and detects breaches (e.g. damage, tampering), enabling timely intervention to maintain secure enclosure of a product (Abekoon et al., 2024; Machiels et al., 2021; Sadeghi et al., 2022) |
| PF | Description | SP enhancement – active packaging (AP) and intelligent packaging (IP) |
|---|---|---|
| Protection | To provide protection from external and internal impacts (barrier protection, mechanical protection), facilitate preservation from spoilage, and ensure security against tampering, pilferage, theft, fraud, and counterfeiting throughout the SC ( | AP – Facilitates interactions between the item and its internal environment to absorb or release substances, thereby keeping items fresher for longer while guaranteeing nutritional attributes ( |
| IP – Continuously monitors, records, and verifies product integrity through real-time sensing of internal and environmental conditions, integrity/tamper detection, and traceability/authentication features, enabling early risk detection and rapid intervention to maintain product protection ( | ||
| Communication | To deliver diverse information in various forms to different SC stakeholders, including identifying and informing about the product, attracting attention and influencing perception, decision-making, branding, marketing and advertising ( | AP – Indirectly contributes through functional compounds that may induce visible state changes indicating product condition ( |
| IP – Converts product and environmental states (e.g. temperature, gas composition, location) into digital signals, enabling real-time monitoring, tracking and tracing, quality alerts, accessible product information, and data-driven decision-making across supply chain stakeholders and consumers ( | ||
| Convenience | To facilitate convenient handling including logistics (storage, transportation, distribution, warehousing, and stacking), retail (placement on shelves, inventory), consumption or utilisation (opening, re-opening, portability, storability), and disposal ( | AP – Indirectly contributes to maintain optimal internal package environment, prolonging product viability which may indirectly facilitate storage and distribution operations. (e.g. reducing the need for frequent manual quality inspections) ( |
| IP – Uses non-line-of-sight scanning and wireless data exchange to automate inventory capture and data flows, enabling real-time inventory management, streamlined handling operations, and data-driven optimisation of logistics and return processes ( | ||
| Containment | To serve as a source of containment by enclosing, enveloping, and holding goods together securely ( | AP – Regulates the internal package environment (e.g. oxygen, moisture, gas) to stabilise product conditions and indirectly support containment by preventing degradation that could compromise package integrity ( |
| IP – Affixes digital identifiers to primary, secondary, and tertiary units to monitor package integrity and detects breaches (e.g. damage, tampering), enabling timely intervention to maintain secure enclosure of a product ( |
2.2 The macro-level: logistics capabilities
To map these micro-level PFs to the macro-level process layer, this study utilises Mentzer et al.’s (2004) framework of LCs. Theoretically grounded in the resource-based view and dynamic capabilities (DC), LCs are used instrumentally to explain how SP-enhanced PFs serve as reconfigurable resources that enable firms to sense (via condition monitoring), seize (via responsive logistics), and transform (via network reconfiguration) to achieve competitive advantage in fast-changing global and circular SCs (Mandal et al., 2016). While the SC represents a network of member activities (Gligor and Holcomb, 2014), the LCs of individual members remain fundamental to broader network success (Mentzer et al., 2004). This choice is analytically necessary because the four LC interfaces remain the dominant conceptualisations used to validate logistics integration and SC resilience (Jayarathna et al., 2024; Mandal et al., 2017). Crucially, information-management capabilities serve as the fundamental antecedent for integrating other logistics functions to master uncertainty and enable SC agility (Gligor and Holcomb, 2014). Furthermore, integrated LCs represent the dominant influence on a firm's ability to build resilience and manage the inherent complexity of circular systems (Mandal et al., 2017). As shown in Table 2, these interfaces provide a theoretical grounding for this study to categorise and explain how item-level intelligence reconfigures LCs for CE transitions. In this study, LCs are analysed through their capacity to reconfigure the four primary logistics interfaces: information-management, supply-management, demand-management, and coordination, which act as the operational conduits for circularity.
Summary of LCs as the macro-level analytical lens (classification criteria adapted from Mentzer et al. (2004))
| LC interface | Description | Sub-capabilities | Key references |
|---|---|---|---|
| Information-management | Information technology and/or connectivity that enables firms to acquire, analyse, store, and share tactical and strategic information internally and with partners | Acquiring, storing, analysing information (IN1); Sharing and exchanging information (IN2); Supporting decision-making (IN3); Securing information and trust (IN4) | Gligor and Holcomb (2014), Jayarathna et al. (2024), Mandal et al. (2017), Mentzer et al. (2004) |
| Supply-management | The management of the logistics function including total cost minimisation, effective time and resource management, and the standardisation of logistics activities | Proactive or creative logistics solutions (SP1); Simplification and standardisation of key logistics activities (SP2); Timeliness and time efficiency (SP3); Human labour minimisation (SP4); Resource minimisation (SP5); Other cost minimisation (SP6) | Gligor and Holcomb (2014), Jayarathna et al. (2024), Mandal et al. (2017), Mentzer et al. (2004), Rajaguru et al. (2022) |
| Demand-management | Distinctive product or service offerings focused on fulfilling customer requirements through high-quality, responsive, and flexible logistics; also called customer-focused, value-added, or customer integration capabilities | Flexibility in operational circumstances (DM1); Responsiveness to customer requirements (DM2); Timeliness in logistics operations (DM3); Availability of operational coverage (DM4); Delivery quality conforming with customer requirements (DM5); Communication with customers (DM6) | Gligor and Holcomb (2014), Jayarathna et al. (2024), Mandal et al. (2017), Mentzer et al. (2004), Rajaguru et al. (2022) |
| Coordination | Internal and external coordination; integration of internal and external organisational elements to align actions and achieve the goals of the logistics operation; ability to communicate and collaborate | Standardisation – i.e. cross-functional policies and procedures (CO1); Simplification (CO2); Compliance – i.e. adherence to established policies, internal procedures, and external regulations (CO3); Structural adaptation – i.e. network structure, and deployment of resources (CO4); Visibility (CO5) | Gligor and Holcomb (2014), Jayarathna et al. (2024), Mandal et al. (2017), Mentzer et al. (2004) |
| LC interface | Description | Sub-capabilities | Key references |
|---|---|---|---|
| Information-management | Information technology and/or connectivity that enables firms to acquire, analyse, store, and share tactical and strategic information internally and with partners | Acquiring, storing, analysing information (IN1); Sharing and exchanging information (IN2); Supporting decision-making (IN3); Securing information and trust (IN4) | |
| Supply-management | The management of the logistics function including total cost minimisation, effective time and resource management, and the standardisation of logistics activities | Proactive or creative logistics solutions (SP1); Simplification and standardisation of key logistics activities (SP2); Timeliness and time efficiency (SP3); Human labour minimisation (SP4); Resource minimisation (SP5); Other cost minimisation (SP6) | |
| Demand-management | Distinctive product or service offerings focused on fulfilling customer requirements through high-quality, responsive, and flexible logistics; also called customer-focused, value-added, or customer integration capabilities | Flexibility in operational circumstances (DM1); Responsiveness to customer requirements (DM2); Timeliness in logistics operations (DM3); Availability of operational coverage (DM4); Delivery quality conforming with customer requirements (DM5); Communication with customers (DM6) | |
| Coordination | Internal and external coordination; integration of internal and external organisational elements to align actions and achieve the goals of the logistics operation; ability to communicate and collaborate | Standardisation – i.e. cross-functional policies and procedures (CO1); Simplification (CO2); Compliance – i.e. adherence to established policies, internal procedures, and external regulations (CO3); Structural adaptation – i.e. network structure, and deployment of resources (CO4); Visibility (CO5) |
2.3 The strategic-level: circularity through R-imperatives
The CE transition requires slowing, narrowing, and closing material loops. While the 9R framework provides the strategic hierarchy (Potting et al., 2017), this study adopts the R-imperative framework (de Lima and Seuring, 2023; Jegen et al., 2025) as the primary strategic lens. This shift is analytically necessary because R-imperatives cluster circular strategies based on their underlying SC design and flow requirements (Kirchherr et al., 2017). Unlike purely hierarchical models, R-imperatives group the 9R strategies (see Appendix 1 for definitions) into three operational clusters: smarter use, lifespan expansion, and material application (de Lima et al., 2022; Mostafayi Darmian et al., 2026). In this study, these three imperatives serve as the primary classification for CE outcomes, allowing for an analysis of how and why logistics interventions facilitate specific circular pathways (see Appendix 1, Table A1 for details on the R-imperative and 9R). As synthesised in Table 3, this clustering provides a clear delineation between forward-focused design strategies and the reverse-focused operational core of circularity.
Analytical mapping of R-imperative framework to LCs
| R-imperative | R-strategies | Operational requirements and LC interfaces | Key references |
|---|---|---|---|
| Smarter use | Refuse (R0) | Upstream waste prevention relying on supply-management and demand-management interfaces to optimise forward flows and reduce overproduction (including unnecessary product waste), as well as support from information-management interface to optimise the connection between supply and demand | Gligor and Holcomb (2014), Jayarathna et al. (2024), Jegen et al. (2025), Mandal et al. (2017), Mentzer et al. (2004), Mostafayi Darmian et al. (2026), Rajaguru et al. (2022) |
| Rethink (R1) | |||
| Reduce (R2) | |||
| Lifespan expansion | Reuse (R3) | Reverse logistics requiring the information-management interface to verify asset condition and the coordination interface for triage and sorting, resolving information asymmetry | Carlos and de Mattos (2025), Gligor and Holcomb (2014), Jayarathna et al. (2024), Jegen et al. (2025), Mandal et al. (2017), Mentzer et al. (2004) |
| Repair (R4) | |||
| Refurbish (R5) | |||
| Remanufacture (R6) | |||
| Repurpose (R7) | |||
| Material application | Recycle (R8) | Scalable recovery of material value requiring the coordination interface, with support from the information-management interface, to manage high-volume return flow and material purity | de Lima et al. (2022), Gligor and Holcomb (2014), Jayarathna et al. (2024), Mandal et al. (2017), Mentzer et al. (2004), Mostafayi Darmian et al. (2026) |
| Recover (R9) |
| R-imperative | R-strategies | Operational requirements and LC interfaces | Key references |
|---|---|---|---|
| Smarter use | Refuse (R0) | Upstream waste prevention relying on supply-management and demand-management interfaces to optimise forward flows and reduce overproduction (including unnecessary product waste), as well as support from information-management interface to optimise the connection between supply and demand | |
| Rethink (R1) | |||
| Reduce (R2) | |||
| Lifespan expansion | Reuse (R3) | Reverse logistics requiring the information-management interface to verify asset condition and the coordination interface for triage and sorting, resolving information asymmetry | |
| Repair (R4) | |||
| Refurbish (R5) | |||
| Remanufacture (R6) | |||
| Repurpose (R7) | |||
| Material application | Recycle (R8) | Scalable recovery of material value requiring the coordination interface, with support from the information-management interface, to manage high-volume return flow and material purity | |
| Recover (R9) |
Upstream “smarter use” strategies are primarily design-led with LCs supporting optimised forward flows. In contrast, the operational core – “lifespan expansion” and “material application” – is fundamentally enabled through reconfigured LCs. However, these models require robust logistics infrastructures to manage the physical complexity of take-back, sorting, quality triage, and supply-demand matching (Morgan et al., 2016; Mostafayi Darmian et al., 2026). Currently, the reverse-focused models are hindered by critical uncertainties regarding the quality, quantity, and timing of returned assets (de Lima and Seuring, 2023). Consequently, circularity is both a material and an informational challenge. SP addresses this by (1) mitigating physical value loss through functional preservation and (2) mitigating information loss by providing item-level visibility.
As integrated LCs are a dominant influence on a firm's ability to manage circular complexity, they require both functional preservation and real-time visibility enabled by SP to resolve primary implementation barriers (Carlos and de Mattos, 2025; Fatorachian and Kazemi, 2021). Through these SP-enhanced LCs, loop-closing strategies can move beyond sequential siloed approaches towards the concurrent design of forward and reverse flows, facilitating the economic viability and strategic sustainability of circularity.
3. Methodology
This study employed a theory-informed SLR, guided by established LSCM research principles to ensure transparency, rigour, and reproducibility (Durach et al., 2017; Seuring et al., 2020). Moving beyond descriptive summaries, the methodology was designed to provide a detailed explanation of how and why SP reconfigures logistics activities and enables CE transitions. This approach facilitated both theory refinement (deductive-internal) by examining how SP operationalises LCs, and theory extension (deductive-external) by linking these enhancements to the strategic requirements of the CE transition (Seuring et al., 2020).
3.1 Sampling and selection strategy
Following the six-step SLR proposed by Durach et al. (2017), RQs were defined in the first step. Specifically, RQ1 aimed to capture the “how” by examining the operational reconfiguration of macro-level LCs triggered by micro-level SP-enhanced PFs. RQ2 addressed the strategic “why” by identifying the circular outcomes (R-imperatives) enabled by these enhanced capabilities, explaining the logic of how resolving information asymmetry and material loss facilitates CE transitions.
Step 2 operationalised selection criteria via the PEO framework (Population, Exposure, Outcome) (Khan et al., 2003), restricting the population to logistics or SC contexts where packaging is a strategic resource, the exposure to SP-logistics linkages, and the outcome to an identifiable circular R-strategies. The sample records included peer-reviewed articles, chapters, and reviews published in English through December 2025, reflecting the strong growth trend in recent literature. Step 3 comprised a comprehensive search of Scopus and Web of Science using a three-block keyword strategy targeting the intersection of SP, logistics, and circular R-strategies.
In Step 4, the baseline sample was refined through a two-stage screening process to minimise selection bias (Durach et al., 2017). Initially, four researchers independently screened titles and abstracts against exposure criteria, with “maybe” cases reassessed by an independent team member to ensure inter-rater reliability. A subsequent full-text assessment against outcome criteria resolved ambiguities regarding the SP-LC-CE linkage through group consensus, resulting in a final synthesis sample of 59 articles (see Figure 1).
At the top, a box labeled “Search string (generic)” presents the search strategy. The search string reads: TITLE-A B S-KEY (“smart packag asterisk” OR “intelligent packag asterisk” OR “interactive packag asterisk” OR “active packag asterisk” OR “connected packag asterisk” OR “smart label asterisk” OR “creative packag asterisk” OR “enhanced packag asterisk”) AND TITLE-A B S-KEY (“logis asterisk” OR “supply chain asterisk” OR “operation asterisk” OR “handling asterisk” OR “transport asterisk” OR “inventory asterisk” OR “warehous asterisk” OR “distribu asterisk” OR “storag asterisk” OR “procur asterisk” OR “purchas asterisk” OR “freight asterisk” OR “rout asterisk” OR “schedul asterisk”) AND TITLE-A B S-KEY (“circular asterisk” OR “closed loop asterisk” OR “recyc asterisk” OR “reus asterisk” OR “revers asterisk” OR “re-manu asterisk” OR “remanu asterisk” OR “reduc asterisk” OR “redesign asterisk” OR “rethink asterisk” OR “refuse asterisk” OR “resell asterisk” OR “upcyc asterisk” OR “downcyc asterisk” OR “extended producer responsibilit asterisk” OR “E P R asterisk” OR “repurpos asterisk” OR “recover asterisk” OR “sharing econom asterisk” OR “obsolenc asterisk”). The search engines are “Scopus” and “Web of Science”. An arrow leads downward to a box labeled “Scopus: 1193” and “Web of Science: 1101”. To the right, a dashed box lists the inclusion criteria: “a. infinity to 31.12.2025”, “b. Articles, book chapters, and reviews”, and “c. English language”. A dased arrow from this dashed box leads to box labeled box labeled “Scopus: 1193” and “Web of Science: 1101”. A downward arrow leads to a box labeled “Records identified for title and abstract review, n equals 1762”. A dashed rightward arrow from this downward arrow leads to a dashed box labeled “Duplicates excluded, n equals 532”. Another downward arrow leads to a box labeled “Reports intended for full text review, n equals 133”. A dashed rightward arrow from this downward arrow leads to a dashed box labeled “Records excluded, n equals 1629”. Below, the exclusion criteria box reads “Exclusion criteria (E dimension, P E O): a. Not addressing smart packaging. b. Focused only on material development or packaging description without logistic relevance”. A further downward arrow leads to the final box labeled “Studies included in the review, n equals 59”. A dashed rightward arrow from this downward arrow leads to a dashed box labeled “Reports excluded, n equals 74”. Below, the exclusion criteria box reads “Exclusion criteria (O dimension, P E O): a. No clear influence of smart packaging on logistics practices. b. No identifiable implication for C E strategies”.SLR search and selection process
At the top, a box labeled “Search string (generic)” presents the search strategy. The search string reads: TITLE-A B S-KEY (“smart packag asterisk” OR “intelligent packag asterisk” OR “interactive packag asterisk” OR “active packag asterisk” OR “connected packag asterisk” OR “smart label asterisk” OR “creative packag asterisk” OR “enhanced packag asterisk”) AND TITLE-A B S-KEY (“logis asterisk” OR “supply chain asterisk” OR “operation asterisk” OR “handling asterisk” OR “transport asterisk” OR “inventory asterisk” OR “warehous asterisk” OR “distribu asterisk” OR “storag asterisk” OR “procur asterisk” OR “purchas asterisk” OR “freight asterisk” OR “rout asterisk” OR “schedul asterisk”) AND TITLE-A B S-KEY (“circular asterisk” OR “closed loop asterisk” OR “recyc asterisk” OR “reus asterisk” OR “revers asterisk” OR “re-manu asterisk” OR “remanu asterisk” OR “reduc asterisk” OR “redesign asterisk” OR “rethink asterisk” OR “refuse asterisk” OR “resell asterisk” OR “upcyc asterisk” OR “downcyc asterisk” OR “extended producer responsibilit asterisk” OR “E P R asterisk” OR “repurpos asterisk” OR “recover asterisk” OR “sharing econom asterisk” OR “obsolenc asterisk”). The search engines are “Scopus” and “Web of Science”. An arrow leads downward to a box labeled “Scopus: 1193” and “Web of Science: 1101”. To the right, a dashed box lists the inclusion criteria: “a. infinity to 31.12.2025”, “b. Articles, book chapters, and reviews”, and “c. English language”. A dased arrow from this dashed box leads to box labeled box labeled “Scopus: 1193” and “Web of Science: 1101”. A downward arrow leads to a box labeled “Records identified for title and abstract review, n equals 1762”. A dashed rightward arrow from this downward arrow leads to a dashed box labeled “Duplicates excluded, n equals 532”. Another downward arrow leads to a box labeled “Reports intended for full text review, n equals 133”. A dashed rightward arrow from this downward arrow leads to a dashed box labeled “Records excluded, n equals 1629”. Below, the exclusion criteria box reads “Exclusion criteria (E dimension, P E O): a. Not addressing smart packaging. b. Focused only on material development or packaging description without logistic relevance”. A further downward arrow leads to the final box labeled “Studies included in the review, n equals 59”. A dashed rightward arrow from this downward arrow leads to a dashed box labeled “Reports excluded, n equals 74”. Below, the exclusion criteria box reads “Exclusion criteria (O dimension, P E O): a. No clear influence of smart packaging on logistics practices. b. No identifiable implication for C E strategies”.SLR search and selection process
3.2 Data analysis and synthesis
Data analysis and framework development (Step 5) followed a two-phase synthesis designed to transition from the micro-level unit of analysis to the macro-level process layer, and finally to strategic circular outcomes. In the first phase, the specific mechanisms through which SP reconfigures micro-level PFs were inductively derived from the analysis of 59 reviewed articles (list of full references in Supplementary Material 1). Coded data associated with each primary PF were systematically examined and grouped into recurring themes, which were subsequently defined as packaging sub-functionalities (see Appendix 2 for definitions and further explanation). These sub-functionalities were subsequently mapped to logistics sub-capabilities across the four LC interfaces using an analysis matrix, with mappings recorded only when studies explicitly linked SP technology or its functionality to a change in a specific logistics sub-capability (see details in Supplementary Material 2).
In the second phase, these reconfigured LCs were synthesised through the lens of the R-imperative framework to determine the specific circular strategies enabled by these enhanced capabilities. This involved systematically analysing how each logistics sub-capability contributes to CE outcomes and assigning these contributions to corresponding R-strategies based on the reported operational effects (e.g. waste prevention, reuse facilitation, or recycling support), as detailed in Supplementary Material 3. To ensure analytical rigour and move beyond the identification of isolated variables, we utilised axial coding in NVivo during first and second stages to identify the sequences and linkages between these layers (Durach et al., 2017).
This methodological choice was essential to construct a systemic mapping of reconfiguration, tracing how a technological trigger (micro-level PFs) reconfigures a logistics process (macro-level LCs) to enable a specific circular outcome (strategic-level R-imperative). This process provided the analytical foundation for the framework synthesis grounded in a theoretically sound evidence base (Tranfield et al., 2003). Finally, results were synthesised into a multi-layered framework (Step 6). The following findings present four thematic pathways where SP-enhanced PFs are systematically linked to specific LC interfaces (RQ1) to address the operational and informational requirements of the R-imperatives (RQ2).
4. Findings
To synthesise the complex relationships between SP-enhanced functionalities, LCs and their contribution to R-imperatives, Table 4 provides an analytical roadmap derived from two-stage qualitative coding and analysis where we identified the core configurations that appear most consistently as effective enablers of circularity. This synthesis reveals four thematic pathways: (1) proactive logistics management and dynamic optimisation, (2) autonomous and scalable reverse flows, (3) systemic trust and quality of material streams, and (4) collaborative value co-creation.
Consolidated summary of thematic mapping
| Thematic pathway | Dominant sub-PFs | Dominant sub-LCs | R-imperatives | Dominant R-strategies |
|---|---|---|---|---|
| Predictive logistics management and dynamic optimisation | CM1, CM2, PT1, PT2, and PT3 | IN1, IN3, SP1-SP6, DM1, DM2, DM3, and DM4 | Smarter use | R2 |
| Autonomous and scalable reverse flows | CM1, CV1, CV2, CV3, and CT1 | SP1-SP6, CO1, CO2, and CO4 | Lifespan expansion and material application | R3, R6, R8 |
| Systemic trust and quality of material streams | CM1, PT2, PT5, PT4, and PT6 | IN4, CO3, CO5, and SP1 | All R-imperatives | R2, R3, R8 |
| Collaborative value co-creation | CM1, CM2, PT2, and CV4 | IN2, SP1, DM2, DM5, and DM6 | All R-imperatives | R2, R3, R8 |
| Thematic pathway | Dominant sub-PFs | Dominant sub-LCs | R-imperatives | Dominant R-strategies |
|---|---|---|---|---|
| Predictive logistics management and dynamic optimisation | CM1, CM2, PT1, PT2, and PT3 | IN1, IN3, SP1-SP6, DM1, DM2, DM3, and DM4 | Smarter use | R2 |
| Autonomous and scalable reverse flows | CM1, CV1, CV2, CV3, and CT1 | SP1-SP6, CO1, CO2, and CO4 | Lifespan expansion and material application | R3, R6, R8 |
| Systemic trust and quality of material streams | CM1, PT2, PT5, PT4, and PT6 | IN4, CO3, CO5, and SP1 | All R-imperatives | R2, R3, R8 |
| Collaborative value co-creation | CM1, CM2, PT2, and CV4 | IN2, SP1, DM2, DM5, and DM6 | All R-imperatives | R2, R3, R8 |
Following the DC framework (Teece, 2007), each pathway tracks the transition from sensing (data acquisition) to seizing (operational intervention) and reconfiguring (systemic change). To ensure clarity, each section foregrounds a primary mechanism representing the most significant process within that pathway. This structure differentiates between pathways driven by seizing capabilities (process-level interventions in Pathways 1 and 3) and those requiring reconfiguring capabilities (system-level changes in Pathways 2 and 4). The following sections detail these primary mechanisms, using secondary elements as illustrative support to demonstrate how SP-enabled capabilities achieve circular outcomes.
4.1 Proactive logistics management and dynamic optimisation
SP serves as a critical enabler for shifting logistics from a reactive operational function to a proactive management system. This pathway addresses the loss of materials and information by transforming traditional information-management capabilities into dynamic assets for circularity. This shift is underpinned by foundational sensing capabilities, where SP-enhanced functionalities allow firms to move beyond static labelling to acquire, store, and analyse item-level data regarding product freshness and physical integrity (Dodero et al., 2021; Du et al., 2025; Vanderroost et al., 2014; Wang et al., 2019). Specifically, SP-enhanced protection functionalities ensure physical integrity throughout the forward flow, providing the environmental control necessary to preserve product value. By embedding sensors and unique data carriers, the technological layer transforms the packaging into an active data node capable of monitoring thermal history and atmospheric conditions (Aguiar et al., 2022; Mohebi and Marquez, 2015; Taoukis, 2010). These capabilities reduce the uncertainty regarding an item's remaining shelf-life, providing the accurate data needed to mitigate the information asymmetry that leads to material waste across the SC (Jayarathna et al., 2024).
The primary mechanism driving this pathway is the development of seizing capabilities, specifically foregrounded using predictive decision-support models in the supply-management interface. By feeding real-time data into proactive decision models, firms move beyond simple detection to anticipate anomalies, such as temperature deviations and fraudulent route changes, and predict spoilage patterns before material value is lost (Mostaccio et al., 2023; Yang et al., 2024). High-fidelity data from nano-sensors and AI-driven indicators provide up to 97.5% accuracy in identifying freshness stages, offering the analytical precision required for seizing interventions and decision support in a timely manner (Du et al., 2025; Rajan and Wani, 2025). This capability allows managers to utilise AI and machine learning to replace rigid, time-based protocols with dynamic logistics solutions tailored to the actual state of the product (Fernandez et al., 2023; Jia et al., 2025). By synchronizing environmental sensing with analytical insights, firms align product status with logistics planning, ensuring that material value is preserved (Alam et al., 2021; Upadhyay et al., 2024).
Consequently, these interventions facilitate reconfiguring capabilities in supply-management and demand-management interfaces, where firms implement dynamic operational flexibility and process optimisation in the forward flow. This allows shipments to be prioritised or rerouted in transit based on actual degradation rates, enhancing responsiveness to both environmental conditions and customer needs (Fernandez et al., 2023; Kamau et al., 2025). Furthermore, SP enables firms to influence demand by transforming quality data into market intelligence. Retailers can use real-time shelf-life and quality data from sensors to trigger dynamic pricing, accelerating the sale of at-risk items (Heising et al., 2017; Jia et al., 2025; Taoukis, 2010). These capabilities, supported by streamlined inventory management through non-line-of-sight scanning, allow firms to eliminate the manual errors and time lags inherent in high-volume environments (Abekoon et al., 2024; Semunab et al., 2016). Moreover, they enable the First-Expired-First-Out (FEFO) systems, providing the operational precision required to make circular models economically competitive and prevent systemic waste (Ahmed et al., 2018; Ganeson et al., 2023; Lu et al., 2025). Ultimately, the integration of these capabilities effectively mitigates the loss of materials and information across SC and operationalise the “smarter use” (reduce-R2) imperative (Kabadurmus et al., 2023; Zhang et al., 2025). Therefore, we propose:
Pathway 1 (P1): SP-enhanced protection and communication functionalities reconfigure the information-management, supply-management, and demand-management interfaces into proactive logistics management systems and dynamic optimisation by resolving information asymmetry in forward flows, thereby enabling the smarter use imperative.
4.2 Autonomous and scalable reverse flows
SP-enhanced functionalities are essential for transitioning from manual, niche-scale recovery to autonomous and scalable reverse flows. The primary mechanism driving this pathway is the development of reconfiguring capabilities that enable network structural adaptation and scalability, which provides the operational threshold necessary to maintain assets in high-value reverse flows. This pathway prevents material and information loss by industrialising the identification and routing processes required for circular loops.
This pathway is underpinned by foundational sensing capabilities, where SP establishes the identity, provenance, and condition of returned items. Through SP-enhanced communication functionalities (i.e. using unique digital identifiers and data carriers), firms can implement standardised governance and the acquisition of item-level data for the return and re-entry of reusable items among SC actors, such as ownership history and material composition (Abekoon et al., 2024; Al-Thani et al., 2025; Kamau et al., 2025). For example, the integration of Digital Product Passports (DPPs) facilitates the end-to-end exchange of item-level lifecycle data (Singh et al., 2025). In specialised sectors like pharmaceuticals, these protocols establish a verifiable record of storage history to ensure item integrity (Gerrans et al., 2023). By resolving uncertainty regarding an item's origin and history, these sensing capabilities provide the evidence required for the safe recovery of post-consumer assets (Kamboj et al., 2025; Singh et al., 2025).
These data streams enable seizing capabilities within the coordination and supply-management interfaces through automated triage and processing. By combining convenience functionalities and unitised containment with standardised carriers and digital watermarking, firms execute high-fidelity sorting and assess asset integrity with near-instantaneous precision (Machiels et al., 2021; Rajan and Wani, 2025). This capability mitigates labour-intensive manual assessments, traditionally a major bottleneck, and significantly increases the throughput of recovery facilities (Ahmed et al., 2018; Gerrans et al., 2023). Consequently, this ability to seize recovery opportunities overcomes the operational delays that prevent reverse flows from achieving industrial speeds, thereby ensuring the economic feasibility of circular strategies (Ahmed et al., 2018; Fernandez et al., 2021; Gerrans et al., 2023; Machiels et al., 2021).
As previously mentioned, the primary mechanism of this pathway is the development of reconfiguring capabilities that enable the structural adaptation of the supply-management and coordination interfaces. While circularity is often hindered by the geographic uncertainty and transport costs of decentralised returns, SP reconfigures the network architecture into a flexible, scalable loop. This involves feeding empirical condition and location data into decision-support models to manage collection, consolidation, and dynamic routing (Gerrans et al., 2023; Jia et al., 2025; Kamau et al., 2025; Lu et al., 2025). By synchronising data between actors and eliminating manual evaluation, this transformation achieves system-wide cost minimisation (Ahmed et al., 2018; Rajan and Wani, 2025; Semunab et al., 2016). Ultimately, these reconfiguring capabilities effectively resolve information asymmetry across the reverse logistics network, providing the architecture required to operationalise the “lifespan expansion” (reuse-R3 and remanufacture-R6) and “material application” (recycle-R8) imperatives at an industrial scale (Gerrans et al., 2023; Kamboj et al., 2025; Machiels et al., 2021; Zhang et al., 2025). Therefore, we propose:
Pathway 2 (P2): SP-enhanced communication, convenience, and containment functionalities reconfigure the supply-management and coordination interfaces into autonomous reverse flows by unitising assets and reducing the operational cost of recovery, thereby enabling the lifespan expansion and material application imperatives.
4.3 Systemic trust and quality of material streams
While P1 and P2 address operational optimisation and scalability, the CE transition is frequently hindered by a lack of trust among SC actors. Accordingly, SP-enhanced functionalities are fundamental to establishing the verifiable transparency for systemic trust required for high-value circularity. The primary mechanism driving this pathway is the development of seizing capabilities related to immutable verification and auditing, which allow firms to transition from trust-based to evidence-based models that mitigate fraud, theft, and counterfeiting. This pathway addresses the loss of materials and information by providing a secure record of an item's lifecycle, thereby resolving the lack of trust regarding the environmental history and material purity of returned assets.
This pathway is underpinned by foundational sensing capabilities, where security-focused protection and communication functionalities, such as blockchain-linked RFID, holograms, and tamper-evident seals, ensure that provenance data remain unalterable (Davidescu et al., 2025; Endara et al., 2023; Zuo et al., 2022). These capabilities are further enhanced by condition monitoring, where the package captures an objective history of temperature, moisture, and chemical exposure (Ahmed et al., 2018; Matan et al., 2024). By providing continuous visibility of product location and quality variables, these sensing capabilities reduce the uncertainty regarding material integrity and close the information gaps that traditionally leads to material leakage and premature disposal.
These data streams enable the primary seizing capabilities within the information-management and coordination interfaces through the creation of digital audit trails. By replacing best-guess assessments with digital verification, firms can seize the proof required to satisfy safety-critical requirements in multi-actor networks, facilitate stakeholder collaboration and prevent the downcycling of high-value materials (Al-Thani et al., 2025; Gerrans et al., 2023; Mostaccio et al., 2023; Yang et al., 2024). This capability allows firms to dynamically reroute assets based on their specific environmental exposure and residual safety margins rather than relying on chronological stock rotation (Abekoon et al., 2024; Du et al., 2025). Furthermore, the integration of granular data on material composition into decision-support models enables firms to replace speculative manual sorting with precision identification (Al-Thani et al., 2025; Htun et al., 2023; Yang et al., 2024). By synchronising item-level evidence with logistics processes, firms resolve the information asymmetry between disparate actors, ensuring that only items with verified storage history are reintegrated into high-value streams.
This pathway culminates in reconfiguring capabilities that evolve the information-management and coordination logistics interfaces into a precision material harvesting system. These capabilities generate the auditable proof required to satisfy strict regulatory standards, effectively turning the package into a digital certificate of circular eligibility (Gerrans et al., 2023; Kamboj et al., 2025). This systemic shift replaces subjective visual inspections with verifiable data, providing the structural transparency required to safely scale circular operations (Butuc et al., 2025; Singh et al., 2025). Ultimately, these capabilities resolve information asymmetry regarding provenance and integrity, ensuring that materials retain their highest utility across multiple lifecycles (Davidescu et al., 2025; Singh et al., 2025; Yang et al., 2024). This allows firms to operationalise the “smarter use” (reduce-R2), “lifespan expansion” (reuse-R3), and “material application” (recycle-R8) imperatives simultaneously. Therefore, we propose:
Pathway 3 (P3): SP-enhanced protection and communication functionalities reconfigure the information-management and coordination interfaces to establish systemic trust and facilitate high-quality material streams by resolving information asymmetry regarding material purity and lifecycle provenance, thereby enabling all three R-imperatives.
4.4 Collaborative value co-creation
The findings reveal that SP-enhanced functionalities serve as a foundation for transitioning from traditional, firm-centric SCs toward collaborative value co-creation platforms. The primary mechanism driving this pathway is the development of reconfiguring capabilities related to consumer role transformation, which fundamentally redefines their position within the logistics network. This pathway addresses the loss of materials and information by bridging the information gap between industrial actors and end-users to facilitate circular loops through direct engagement and decentralised decision-making.
This transformation is underpinned by foundational sensing capabilities, where communication functions and digital data carriers (e.g. QR codes, near-field communication) create a direct digital link for information flow. These capabilities allow consumers to access real-time data regarding product origin, safety status, and usage instructions via personal devices (Ahmed et al., 2018; Du et al., 2025; Kamau et al., 2025). Simultaneously, SP-enhanced protection functionalities ensure the physical integrity of the product, providing the tangible safety assurance that supports these digital credentials. By offering transparent credentials and sourcing data, SP-enabled information-management interfaces empower consumers to make informed choices and align their consumption with circular commitments (Davidescu et al., 2025; Kamboj et al., 2025; Uysal-Unalan et al., 2024). These sensing capabilities resolve provenance uncertainty and mitigate information asymmetry at the point of purchase, providing the foundational trust required to prevent the premature disposal of viable products.
These data streams enable seizing capabilities within the demand-management interface through direct digital engagement and condition-driven interventions. By matching the physical state of the package to sustainability expectations, firms can align logistics quality with evolving customer requirements for freshness and safety (Fernandez et al., 2023; Rajan and Wani, 2025; Versino et al., 2023). When consumers interact with smart labels, the system captures real-time signals that trigger dynamic interventions, such as storage advice, usage recommendations, or immediate price markdowns, to prevent the item from becoming waste (Du et al., 2025; Zhang et al., 2025). For green consumer segments, these seizing capabilities foster high-engagement connections by resolving the information asymmetry between industrial actors and consumers (Alves et al., 2023; Davidescu et al., 2025). Consequently, active consumer participation ensures that material value is preserved through data-driven problem anticipation (Alam et al., 2021; Upadhyay et al., 2024; Zhang et al., 2025).
Ultimately, these processes culminate in the primary reconfiguring capabilities that transform the supply-management and demand-management interfaces by integrating the consumer as a functional logistics node in circular SCs. This structural shift reconfigures the logistics architecture by decentralising decision-making and utilising the consumer to perform essential tasks such as initial grading, sorting and automatic data-logging (Gerrans et al., 2023; Singh et al., 2025). These capabilities allow the network to scale by lowering the physical and cognitive effort required for participation, establishing a collaborative return system where consumers actively sustain the circular loop (Machiels et al., 2021; Singh et al., 2025). Finally, these reconfiguring capabilities effectively resolve information asymmetry across the stakeholder interface, providing the collaborative structure required to operationalise the “smarter use” (reduce-R2), “lifespan expansion” (reuse-R3), and “material application” (recycle-R8) imperatives simultaneously (Al-Thani et al., 2025; Barone et al., 2021; Butuc et al., 2025; Kamboj et al., 2025). Therefore, we propose:
Pathway 4 (P4): SP-enhanced communication, protection, and convenience functionalities reconfigure the information-management, supply-management and demand-management interfaces into collaborative platforms by integrating the consumer as a functional logistics node, thereby facilitating all three R-imperatives.
5. Discussion
The synthesis of our findings demonstrates that SP serves as the critical technological bridge for simultaneously addressing CE challenges of physical value and information loss. While design-led strategies focus on material prevention, our analysis confirms that the operational core of circularity – recovering and extending product lifecycles – is fundamentally hindered by logistics-related uncertainties. By mitigating material degradation through functional preservation and resolving information asymmetry through item-level visibility, SP reconfigures LCs from reactive operational functions into proactive management systems. To visualise these pathways, we propose a multi-layered conceptual framework (see Figure 2). It maps the transition from the micro-level technological enabler (SP-enhanced PFs) to the macro-level reconfiguration of LCs, which ultimately operationalises the strategic-level CE outcomes (R-imperatives) identified in the four thematic pathways (P1-P4).
The framework is organized into four columns labeled “Micro-level (S P-enhanced P Fs)”, “Macro-level (L Cs)”, “Thematic pathway”, and “Strategic-level (R-imperatives)”. Four pathways, labeled “P1” through “P4”, are shown from left to right. For “P 1”, the micro-level functions are “Protection” and “Communication”. These connect to the macro-level capabilities “Information-management”, “Supply-management”, and “Demand-management”. These capabilities connect to the thematic pathway “Proactive logistics management and dynamic optimisation”, which connects to the strategic-level outcome “Smarter use”. For “P 2”, the micro-level functions are “Communication”, “Convenience”, and “Containment”. These connect to the macro-level capabilities “Supply-management” and “Coordination”. These capabilities connect to the thematic pathway “Autonomous and scalable reverse flows”, which connects to the strategic-level outcomes “Lifespan expansion” and “Material application”. For “P 3”, the micro-level functions are “Protection” and “Communication”. These connect to the macro-level capabilities “Information-management” and “Coordination”. These capabilities connect to the thematic pathway “Systemic trust and quality of material streams”, which connects to the strategic-level outcomes “Smarter use”, “Lifespan expansion”, and “Material application”. For “P 4”, the micro-level functions are “Communication”, “Protection”, and “Convenience”. These connect to the macro-level capabilities “Information-management”, “Supply-management”, and “Demand-management”. These capabilities connect to the thematic pathway “Collaborative value co-creation”, which connects to the strategic-level outcomes “Smarter use”, “Lifespan expansion”, and “Material application”.Multi-layered conceptual framework of SP-enabled circularity
The framework is organized into four columns labeled “Micro-level (S P-enhanced P Fs)”, “Macro-level (L Cs)”, “Thematic pathway”, and “Strategic-level (R-imperatives)”. Four pathways, labeled “P1” through “P4”, are shown from left to right. For “P 1”, the micro-level functions are “Protection” and “Communication”. These connect to the macro-level capabilities “Information-management”, “Supply-management”, and “Demand-management”. These capabilities connect to the thematic pathway “Proactive logistics management and dynamic optimisation”, which connects to the strategic-level outcome “Smarter use”. For “P 2”, the micro-level functions are “Communication”, “Convenience”, and “Containment”. These connect to the macro-level capabilities “Supply-management” and “Coordination”. These capabilities connect to the thematic pathway “Autonomous and scalable reverse flows”, which connects to the strategic-level outcomes “Lifespan expansion” and “Material application”. For “P 3”, the micro-level functions are “Protection” and “Communication”. These connect to the macro-level capabilities “Information-management” and “Coordination”. These capabilities connect to the thematic pathway “Systemic trust and quality of material streams”, which connects to the strategic-level outcomes “Smarter use”, “Lifespan expansion”, and “Material application”. For “P 4”, the micro-level functions are “Communication”, “Protection”, and “Convenience”. These connect to the macro-level capabilities “Information-management”, “Supply-management”, and “Demand-management”. These capabilities connect to the thematic pathway “Collaborative value co-creation”, which connects to the strategic-level outcomes “Smarter use”, “Lifespan expansion”, and “Material application”.Multi-layered conceptual framework of SP-enabled circularity
5.1 Theoretical contributions
The primary theoretical contribution of this study is the development of a multi-layered conceptual framework that explains the “how” and “why” behind SP-enabled circularity. By returning to the analytical premise that circularity is fundamentally both a material and an information problem, we demonstrate that SP-enhanced interfaces resolve these challenges by transitioning SCs from reactive recovery to proactive management systems. This research contributes to LSCM theory in four ways.
First, we demonstrate how SP-enhanced functionalities operationalise latent logistics DC. While LCs are theoretically recognised as DC, their execution in circular systems is traditionally hindered by a lack of item-level visibility. By providing the necessary data to sense quality and seize recovery opportunities, SP triggers the functional preservation required to mitigate physical value loss while simultaneously resolving the information asymmetry that leads to systemic waste. Consequently, it effectively transforms latent capabilities into proactive data-driven management systems.
Second, we identify the information-management interface as the foundational antecedent for circular logistics. By generating item-level data, it allows other interfaces to manage closed-loop complexity. This establishes a theoretical hierarchy where the effective operationalisation of the information-management interface serves as a prerequisite for the physical reconfiguration of the SC required for circularity. This hierarchy clarifies that the CE transition is contingent upon the digital maturity of the logistics information layer.
Third, this study clarifies the role of the containment functionality as the physical anchor that operationalises the physical-digital bridge. While previous SP research often overlooks containment or views it as a static integrity-maintaining function, we conceptualise it as the physical-digital unitiser necessary for scaling autonomous reverse flows. We argue that while communication functions (e.g. tags and sensors) create the digital clusters of items, containment provides the structural foundation required to scale these clusters within industrial logistics. By ensuring that aggregated units remain physically intact and digitally identifiable as a single entity during high-volume handling, containment preserves the identity linkage between items and their digital representations. It is this synchronisation that allows individual item intelligence to be effectively operationalised within network-level coordination. Consequently, containment is transformed from a static integrity-maintaining function into a proactive enabler of scalable reverse flows.
Fourth, SP transforms the downstream demand-management interface into a collaborative bridge facilitating multi-imperative circularity. By integrating the consumer as a functional value co-creator, SP allows a single digital interaction to simultaneously activate pathways for all R-imperatives through waste prevention (“smarter use”), asset recovery (“lifespan expansion”), and material purity (“material application”).
Finally, we identify theoretical boundaries regarding the scope of logistics-led interventions. While SP provides robust support for the logistics-led recovery strategies – i.e. industrial-scale reuse, remanufacture, and recycle, there is negligible evidence connecting these capabilities to the design-led strategies such as repair and refurbish. This gap indicates that such high-touch strategies may rely more on fundamental product design (e.g. modularity) and localised service ecosystems than on the logistics reconfiguration enabled by item-level intelligence. This boundary suggests that SP is a critical enabler for industrialising the CE but cannot substitute for upstream design interventions in strategies where material complexity exceeds informational resolution.
5.2 Practical implications
For practitioners, this study provides a concrete roadmap for leveraging SP to achieve tangible sustainability and operational benefits by reducing waste and closing information gaps inherent in circular loops. For policymakers, these findings highlight SP as a key enabling tool for future policy initiatives, such as Extended Producer Responsibility, to verify compliance and ensure material purity in recycling streams. Interpreting the findings through the lens of logistics reconfiguration, we identify four strategic avenues for implementation.
First, logistics and supply chain managers can utilise SP-enhanced sensing to transition from rigid, time-based inventory models to dynamic, quality-controlled systems, such as data-driven FEFO. This shift allows firms to minimise spoilage and maximise resource utility by rerouting products in transit based on their actual condition, effectively achieving the “smarter use” imperative at the operational level. This capability transforms the information interface into a proactive management tool that anticipates waste before it occurs.
Second, to overcome the high operational costs and complexity of reverse logistics, managers should prioritise the integration of automated sorting with unitised containment. In the reverse flow, the integration of non-line-of-sight scanning and standardised data carriers lowers the per-unit cost of processing, eliminating labour-intensive manual assessments. By treating the package as a unified physician and digital unit, firms can achieve the bulk handling required for industrial-scale recovery. This ensures that individual assets remain identifiable and seizable for high-speed triage within high-volume environments. Therefore, by automating the identification and sorting of returns, SP-enhanced LCs make the high-value “lifespan expansion” imperative – i.e. reuse and remanufacture – economically competitive with linear models by achieving the operational threshold required for network-level scalability.
Third, SP provides a verification layer to build systemic trust within circular networks, particularly in high-risk sectors such as pharmaceuticals and food. Managers can leverage tamper-evident and condition-monitoring functions to provide a verifiable audit trail of the item's storage and environmental history. This transparency provides the objective evidence required to satisfy safety standards and regulatory requirements. By providing a secure record of material purity, SP ensures that secondary resources maintain their highest value and prevents the downcycling of high-quality materials.
Lastly, for marketing and product managers, SP provides a direct digital link to the consumer, turning them into an active participant in circular loops. By providing personalised rewards and clear, item-level disposal instructions via mobile applications, firms can simplify the consumer's role in the recovery process. This digital engagement reduces the effort required for participation, ensuring higher rates of high-quality material returns. Collectively, these capabilities transform circularity from a logistical burden into a data-driven competitive advantage, allowing firms to synchronise forward and reverse flows into a single, optimised ecosystem.
6. Conclusion
This study systematically explored the pathways through which SP simultaneously enhances LCs to address the CE challenges of physical value and information loss. By synthesising fragmented literature into a multi-layered conceptual framework, we have demonstrated that SP is more than a data-collection tool. It acts as a foundational reconfigurable resource that transforms LCs from reactive operational functions into proactive management systems.
The study indicates that the enhancement of LCs is a multi-stage process triggered by micro-level SP-enhanced PFs. We have clarified that the information-management interface serves as the foundational antecedent, providing the functional preservation and item-level visibility required to sense quality degradation and seize recovery opportunities. These capabilities reconfigure the supply-management, demand-management, and coordination interfaces, allowing firms to manage the inherent complexity of closed-loop systems. Through these enhanced capabilities, the logistics network can effectively support the three strategic R-imperatives – smarter use, lifespan expansion, and material application. Specifically, our analysis identified four thematic pathways – (1) proactive logistics management and dynamic optimisation, (2) autonomous and scalable reverse flows, (3) systemic trust and quality of material streams, and (4) collaborative value co-creation – as the dominant configurations through which SP reconfigures the LCs to achieve effective and industrial-scale circularity.
While this review provides a rigorous foundation, several critical boundaries and gaps remain. First, our analysis identified a significant sector bias, with 90% of the synthesised articles focused on the food industry. This dominance is expected, as the food sector is a high-volume industry where packaging is used to manage strict shelf-life limits and perishable goods. Despite a strong baseline, future research should explore how these technologies work in technical sectors like electronics or textiles, where products do not spoil but have complex recycling and recovery cycles. Second, there is a notable theoretical boundary regarding the scope of logistics-led interventions. We found negligible evidence connecting SP-enhanced capabilities to the design-led strategies such as repair and refurbish. This indicates a research gap in understanding how item-level intelligence can support such circular strategies that currently rely more on product design than on logistics reconfiguration. Finally, as 73% of our synthesis base comprised literature reviews, there is a pressing need for empirical validation. Future studies should employ longitudinal case designs or large-scale quantitative assessments to validate the causal strength of the proposed pathways (P1-P4). Specifically, exploring the dynamics of human collaborative value co-creation would further refine our understanding of the consumer as a functional logistics node. By addressing these gaps, future research can build upon this framework to transition circularity from a series of isolated initiatives into a synchronised data-driven ecosystem facilitated by SP.
The authors are grateful to Professor Erik Sandberg and Professor Maria Björklund, Department of Management and Engineering, Linkoping University, for their insightful comments on earlier drafts of this manuscript. This research was supported by Svenska Forskningsrådet Formas, and by National Science Centre, Poland for Yash Chawla.
Appendix 1
Operational definitions and LSCM implications of circular strategies
| R-imperative | R-strategy | Definition and logistical scope | LSCM implication |
|---|---|---|---|
| Smarter use | Refuse (R0) | Avoiding unnecessary products or materials and their associated flows by abandoning functions or switching to non-material solutions | Requires upstream avoidance of non-essential flows and strategic procurement choices to prevent waste at the source |
| Rethink (R1) | Making product use more intensive, often through sharing platforms or multi-functional design | Necessitates demand-management to optimise the connection between supply and demand via service-based models | |
| Reduce (R2) | Using fewer resources and generating less waste per unit of output through increased efficiency | Focuses on preventing systemic leakage through spoilage reduction, fewer handling steps, and optimised execution | |
| Lifespan expansion | Reuse (R3) | Using a discarded but functional product again for its original purpose | Depends on functioning return/collection loops, eligibility checks, and condition verification to enable multiple cycles |
| Repair (R4) | Fixing faults in a broken product so it can be utilised with its original function | Requires diagnostic triage and routing to repair centres supported by parts and service logistics | |
| Refurbish (R5) | Restoring an old product and bringing it up to date through extensive reconditioning | Involves grading, quality assurance, and controlled reintegration into appropriate secondary market channels | |
| Remanufacture (R6) | Rebuilding a product to ‘as new’ condition using recovered cores and new parts | Requires core acquisition, disassembly, testing, and certification within a robust reverse network design | |
| Repurpose (R7) | Using a discarded product or its components for a function different from the original | Necessitates cross-actor coordination and matching criteria to align residual value with new alternative functions | |
| Material application | Recycle (R8) | Processing materials to obtain secondary raw materials of high- or low-grade quality | Requires collection consolidation at scale, strict contamination control, and high material purity to ensure economic viability |
| Recover (R9) | Incinerating materials with energy recovery when higher value retention is not feasible | Focuses on compliant routing and documentation for residual streams to ensure safe, regulated final processing |
| R-imperative | R-strategy | Definition and logistical scope | LSCM implication |
|---|---|---|---|
| Smarter use | Refuse (R0) | Avoiding unnecessary products or materials and their associated flows by abandoning functions or switching to non-material solutions | Requires upstream avoidance of non-essential flows and strategic procurement choices to prevent waste at the source |
| Rethink (R1) | Making product use more intensive, often through sharing platforms or multi-functional design | Necessitates demand-management to optimise the connection between supply and demand via service-based models | |
| Reduce (R2) | Using fewer resources and generating less waste per unit of output through increased efficiency | Focuses on preventing systemic leakage through spoilage reduction, fewer handling steps, and optimised execution | |
| Lifespan expansion | Reuse (R3) | Using a discarded but functional product again for its original purpose | Depends on functioning return/collection loops, eligibility checks, and condition verification to enable multiple cycles |
| Repair (R4) | Fixing faults in a broken product so it can be utilised with its original function | Requires diagnostic triage and routing to repair centres supported by parts and service logistics | |
| Refurbish (R5) | Restoring an old product and bringing it up to date through extensive reconditioning | Involves grading, quality assurance, and controlled reintegration into appropriate secondary market channels | |
| Remanufacture (R6) | Rebuilding a product to ‘as new’ condition using recovered cores and new parts | Requires core acquisition, disassembly, testing, and certification within a robust reverse network design | |
| Repurpose (R7) | Using a discarded product or its components for a function different from the original | Necessitates cross-actor coordination and matching criteria to align residual value with new alternative functions | |
| Material application | Recycle (R8) | Processing materials to obtain secondary raw materials of high- or low-grade quality | Requires collection consolidation at scale, strict contamination control, and high material purity to ensure economic viability |
| Recover (R9) | Incinerating materials with energy recovery when higher value retention is not feasible | Focuses on compliant routing and documentation for residual streams to ensure safe, regulated final processing |
Appendix 2
The high-level packaging functionalities were adopted from Robertson (2016), providing an established conceptual framework for categorising packaging roles. This step ensured theoretical consistency and alignment with widely recognised packaging literature. In contrast, the packaging sub-functionalities in Table A2 were inductively derived from the analysis of 59 reviewed articles, whereby coded data were systematically examined and grouped into recurring themes. This involved iterative comparison of codes within each primary functionality, consolidation of conceptually similar codes, and abstraction into higher-order thematic categories, i.e. packaging sub-functionalities. Given the observed inconsistency and overlap in terminology across the reviewed studies, clear and standardised definitions of sub-functionalities were developed to enhance conceptual clarity and differentiation.
Operational definitions of packaging sub-functionalities (inductively defined based on the analysis of the reviewed papers)
| Packaging functionality | Packaging sub-functionality (enhanced by SP) | Definition |
|---|---|---|
| Communication | Information management and accessibility (CM1) | Enhancing the efficient flow of information among supply chain stakeholders using smart indicators, sensors, and data carriers to enable the storage, monitoring, tracking, tracing, and sharing of real-time product data (e.g. Kabadurmus et al., 2023; Rajan and Wani, 2025) |
| Information-based decision-making (CM2) | Utilising real-time data to support decision-making and enable informed, proactive, and timely strategic responses (e.g. Abekoon et al., 2024; Heising et al., 2017) | |
| Protection | Preservation (PT1) | Maintaining product quality, freshness, and safety by mitigating spoilage through the controlled release or absorption of active components and the regulation of internal atmospheric conditions (e.g. Zuo et al., 2022; Zhang et al., 2025) |
| Environment monitoring (PT2) | Employing sensors, indicators, and data carriers to monitor internal and external environmental conditions and product quality parameters to prevent damage and contamination (e.g. Versino et al., 2023; Kamau et al., 2025) | |
| Anti-theft (PT3) | Preventing unauthorised removal or loss of products by employing additional security measures to actively safeguard goods during transit, storage, and retail display (e.g. Yang et al., 2024; Fernandez et al., 2023) | |
| Authentication (PT4) | Verifying product genuineness and preventing the entry of counterfeit items into the supply chain through the integration of embedded identification and tracking mechanisms (e.g. Ahmed et al., 2018; Versino et al., 2023) | |
| Anti-tampering (PT5) | Providing clear evidence of external environmental impacts to indicate potential product interference, and signalling possible safety or integrity issues (e.g. Mostaccio et al., 2023; Ganeson et al., 2023) | |
| Traceability (PT6) | Providing information on origin, handling, and movement across the supply chain to ensure transparency, visibility, and integrity while preventing fraud and counterfeiting (e.g. Yang et al., 2024; Singh et al., 2025) | |
| Convenience | Automation and the elimination of manual processes (CV1) | Elevating continuous streams of real-time data collection through data carriers and smart sensors to facilitate data sharing and immediate advanced analysis, resulting in automated data-driven decision-making and process execution (e.g. Abekoon et al., 2024; Htun et al., 2023) |
| Process efficiency and optimisation (CV2) | Enhancing the efficiency and optimisation of logistics handling through real-time data from embedded sensors, leading to reduced consumption of resources such as time, energy, materials, and cost (e.g. Gerrans et al., 2023; Mostaccio et al., 2023) | |
| Streamlined processes (CV3) | Simplifying operations and removing unnecessary steps or redundancies to make processes smoother, quicker, and easier to manage (e.g. Abekoon et al., 2024; Zhang et al., 2025) | |
| Instructions and recommendations (CV4) | Providing stakeholders with actionable operational guidance (e.g. handling and storage conditions) to enhance information sharing, flow, and timely communication (e.g. Kabadurmus et al., 2023; Al-Thani et al., 2025) | |
| Containment | Enhancing the containability of secondary or tertiary packaging (CT1) | Ensuring the reusability of structurally sound units through condition monitoring and the removal of damaged items, thereby reducing risks during transit and storage (e.g. Machiels et al., 2021; Abekoon et al., 2024) |
| Packaging functionality | Packaging sub-functionality (enhanced by SP) | Definition |
|---|---|---|
| Communication | Information management and accessibility (CM1) | Enhancing the efficient flow of information among supply chain stakeholders using smart indicators, sensors, and data carriers to enable the storage, monitoring, tracking, tracing, and sharing of real-time product data (e.g. |
| Information-based decision-making (CM2) | Utilising real-time data to support decision-making and enable informed, proactive, and timely strategic responses (e.g. | |
| Protection | Preservation (PT1) | Maintaining product quality, freshness, and safety by mitigating spoilage through the controlled release or absorption of active components and the regulation of internal atmospheric conditions (e.g. |
| Environment monitoring (PT2) | Employing sensors, indicators, and data carriers to monitor internal and external environmental conditions and product quality parameters to prevent damage and contamination (e.g. | |
| Anti-theft (PT3) | Preventing unauthorised removal or loss of products by employing additional security measures to actively safeguard goods during transit, storage, and retail display (e.g. | |
| Authentication (PT4) | Verifying product genuineness and preventing the entry of counterfeit items into the supply chain through the integration of embedded identification and tracking mechanisms (e.g. | |
| Anti-tampering (PT5) | Providing clear evidence of external environmental impacts to indicate potential product interference, and signalling possible safety or integrity issues (e.g. | |
| Traceability (PT6) | Providing information on origin, handling, and movement across the supply chain to ensure transparency, visibility, and integrity while preventing fraud and counterfeiting (e.g. | |
| Convenience | Automation and the elimination of manual processes (CV1) | Elevating continuous streams of real-time data collection through data carriers and smart sensors to facilitate data sharing and immediate advanced analysis, resulting in automated data-driven decision-making and process execution (e.g. |
| Process efficiency and optimisation (CV2) | Enhancing the efficiency and optimisation of logistics handling through real-time data from embedded sensors, leading to reduced consumption of resources such as time, energy, materials, and cost (e.g. | |
| Streamlined processes (CV3) | Simplifying operations and removing unnecessary steps or redundancies to make processes smoother, quicker, and easier to manage (e.g. | |
| Instructions and recommendations (CV4) | Providing stakeholders with actionable operational guidance (e.g. handling and storage conditions) to enhance information sharing, flow, and timely communication (e.g. | |
| Containment | Enhancing the containability of secondary or tertiary packaging (CT1) | Ensuring the reusability of structurally sound units through condition monitoring and the removal of damaged items, thereby reducing risks during transit and storage (e.g. |
The supplementary material for this article can be found online.

