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Recovery-Value Optimization in Returned-Product Supply Chains: A Conceptual Framework for Operational Decision-Making

Valentina Ochuko Obukadata, USA., Corresponding author

Abstract

Product returns are a structural feature of retail and manufacturing supply chains rather than a peripheral cost. Returned merchandise in the United States was valued at approximately USD 890 billion in 2024 and USD 850 billion in 2025, between 15 and 17 percent of retail sales (National Retail Federation & Happy Returns, 2024, 2025). Yet returns are still managed as a reverse logistics problem concerned with moving goods backward at least cost rather than as a value recovery problem concerned with extracting the maximum justifiable value before residual worth decays. This paper develops Recovery-Value Optimization : the systematic alignment of diagnostic precision, disposition timing, channel allocation, and network configuration to maximize risk-adjusted net value recovered, subject to capacity, brand, workforce, and regulatory constraints. Adopting a conceptual theory-building approach, it screens a multidisciplinary corpus of 827 records into 181 retained studies across eleven streams spanning procurement and supplier governance (Akinleye & Adeyoyin, 2022b; Okonkwo et al., 2024a; Wedraogo et al., 2025), visibility and traceability (Nnabueze et al., 2021; Opara et al., 2025; Sakyi, Eboseremen, et al., 2024), predictive analytics (Tonoyan et al., 2023; Tonoyan et al., 2024a; Yeboah et al., 2025), lean process design (Ambali et al., 2021; Ezeugwa et al., 2023a; Ike et al., 2022), sustainable procurement (Ike et al., 2024a; Ike et al., 2025; Yeboah et al., 2024), regulatory and AI governance (Annan, 2025a; Ominyi & Anichukwueze, 2024; Ominyi, Anichukwueze, & Uzougbo, 2024b), warehouse automation (Akanbi & Sunday, 2024; Akanbi & Sunday, 2025; Akinlade et al., 2024), fraud and refund control (Atakpa & Abetoh, 2022; Fadayomi et al., 2024), and occupational safety (Obogo et al., 2025; Obriki et al., 2022a). Four constructs, namely return- flow intelligence, temporal value decay, disposition-channel portfolio, and recovery network capability, are linked through a net recovery value function making explicit the trade-offs among reconditioning, transport and holding cost, channel yield, cannibalization, labour capacity, and compliance exposure. Twelve propositions, a three-tier decision architecture, a five-stage protocol, an illustrative application, and a measurement system are presented.

Keywords

reverse logisticsclosed-loop supply chainsproduct returnsdisposition decisionsvalue recoverycircular economysupply chain analyticsremanufacturing

References

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