The reverse logistics process (RLP) has become a key process for the supply chain (SC) given its importance for treating the increasing quantity of returned or recovered products and its impact on sustainability. However, the RLP is complex and involves a high degree of uncertainty and difficult decisions that affect SC efficiency. One of the aspects that can help the most to reduce this complexity and to improve SC efficiency is to formalize this process. The consulted studies agree on the numerous benefits of RLP formalization, but no tools, methodologies or specific solutions were found that help companies to advance in this matter. This work aims to develop a specific tool for RLP formalization so that its efficiency can be increased, leading to an improvement of SC sustainability. The main results comprise a reference model for RLP (RM-RLP) and an associated methodology so that any company can formalize its RLP by modeling its activities. The proposed tool (RM-RLP and methodology) is applied to a closed loop SC of relaxing chairs as an example of RLP formalization, proving its usefulness and, additionally, the improvements that can be reached in three RLP key indicators: total process duration, customer response time and the perceived autonomy and trust of the workers participating in the process.

The study of utilizing human language in computer systems referred to as NLP, is becoming increasingly significant in various aspects of life, including research, daily activities, commerce, and entrepreneurship endeavors. A multitude of tech companies are dedicating resources towards the development and improvement of NLP methods, models, and products. To add to that, open-source contributions to the field are on the rise. However, with so much progress being made, it may be challenging to understand the current state of NLP and what models are considered to be the most efficient. To help those grappling with the fast-paced and constantly evolving NLP landscape, we have put together a comprehensive overview of the latest NLP research and advancements.

Disposition Decision (DD) consists of deciding how to treat a recovered product, and it is one of the most important decisions in reverse logistics. Any of the selected disposition alternatives will have a significant impact on the enterprise sustainability. However, the most sustainable alternative may not be an alternative to make circular economy (CE) possible. In these cases, if the company wishes to adopt a CE strategy, it will have to switch from the most sustainable alternative to a less sustainable one that CE allows. Then, how much should be sacrificed for each sustainability dimension to make CE possible? This paper proposes a methodology for quantitatively comparing the most sustainable disposition alternative and the most sustainable CE alternative. This comparison allows small and medium enterprises (SMEs) to know how exactly all dimensions increase or decrease when selecting the most sustainable CE disposition alternative and to, therefore, assess the interest of adopting a CE policy. The proposed methodology is applied to a used tire recovery company. The results of this example show that the CE alternative offers a better environmental result but presents worst economic and social results. This example can be used as a guide for future applications other SMEs.

Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL). At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified.Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL. Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions.Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.

This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

The Internet of Things (IoT) is a global platform of Internet-connected smart devices that allows manufacturers to sensorize marketed products. Such products can provide manufacturers with feedback about relevant information, such as their status, use and location, which contributes to significantly reduce uncertainty in the time, quantity and quality of the products recovered during the reverse logistics (RL) process. This sensorization is especially simple in connected appliances given the ease of installing sensors inside. This work shows the convenience of using the IoT in the RL process of the household appliances sector by analyzing the impact on recovery management. To do so, it proposes a simulation model based on system dynamics. The main findings indicate that the IoT permits more reverse supply chains’ inventory by significantly increasing collected products’ average value. This, in turn, allows for more backorder demand to be met and improves the client service level. In light of all this, we conclude that the average RL process profit made with an IoT model is significantly better than the RL process without such a model. With this simulation model, it should also be noted that interoperability and communication improve throughout the supply chain, which allows better decision making.