Enabling Circular Reverse Logistics: A Digitally Enhanced Decision-Support Framework

Abstract

The increasing pressure on manufacturing enterprises to reduce environmental impacts and resource dependency has amplified the importance of reverse logistics within circular manufacturing systems. However, fragmented planning processes, legacy infrastructure, and insufficient coordination and data exchange across return flows hinder efficient resource recovery and value retention. While substantial research addresses individual aspects of reverse logistics, there is a lack of integrative frameworks that connect logistics planning with system-level optimisation and digital enablement. This paper presents a conceptual framework for integrated reverse logistics planning in circular manufacturing, developed through a structured literature review across industrial ecology, supply chain management, and lifecycle engineering. The framework maps interdependencies between return conditions, recovery strategies, and logistics network design, incorporating diagnostics, prognostics and data-driven planning principles. A typology of reverse logistics scenarios is proposed based on product complexity, return timing, and recovery objectives. Digital technologies such as predictive analytics, tracking systems, and extended reality (XR) are positioned as enablers to enhance visibility, support data collection and exchange, and align decision-making across stakeholders. XR is specifically used to visualise condition-based routing tailored with intelligent information provisioning and facilitate collaborative scenario planning. The proposed framework supports the design of responsive reverse logistics strategies that integrate inbound and outbound flows with condition-based maintenance, enabling higher-value circular outcomes. The paper concludes with recommendations for digital integration, data governance, and operational strategies that facilitate the implementation of a circular economy through coordinated reverse logistics planning.