Search Results (159)

This paper studies a supply chain comprising a supplier, a third-party remanufacturer (TPR), and a retailer. The retailer sells both genuine and remanufactured products (i.e., Model O). Leveraging information advantages, the retailer may engage in brand deception by mislabeling remanufactured products as genuine to obtain extra profits (i.e., Model BD). AI-powered anti-counterfeiting technologies (AIT) (i.e., Model BA) and revenue-sharing contracts (i.e., Model C) are considered countermeasures. The findings reveal that (1) brand deception reduces (increases) sales of genuine (remanufactured) products, prompting the supplier (TPR) to lower (raise) wholesale prices. The asymmetric profit erosion effect highlights the gradual erosion of profits for the supplier, retailer, and TPR under brand deception. (2) The bi-interval adaptation effect indicates that AIT is particularly effective in industries with low adaptation resistance. When both the relabeling rate and industry adaptation resistance are low (high), Model BA (Model O) achieves a triple win. (3) Sequentially, when the industry adaptation resistance is low, AIT can significantly improve total profits, consumer surplus (CS), and social welfare (SW). Compared to Model BD, revenue-sharing offers slight advantages in CS but notable disadvantages in SW. Full article

This review article examines the role of additive manufacturing (AM) in increasing energy efficiency and sustainability within the evolving framework of Industry 5.0 and 6.0. This review highlights the unique ability of additive manufacturing to deliver mass-customized products while minimizing material waste and reducing energy consumption. The integration of smart technologies such as AI and IoT is explored to optimize AM processes and support decentralized, on-demand manufacturing. Thisarticle discusses different AM techniques and materials from an environmental and life-cycle perspective, identifying key benefits and constraints. This review also examines the potential of AM to support circular economy practices through local repair, remanufacturing, and material recycling. The net energy efficiency of AM depends on the type of process, part complexity, and production scale, but the energy savings per component can be significant if implemented strategically.AM significantly improves energy efficiency in certain manufacturing contexts, often reducing energy consumption by 25–50% compared to traditional subtractive methods. The results emphasize the importance of innovation in both hardware and software to overcome current energy and sustainability challenges. This review highlights AM as a key tool in achieving a human-centric, intelligent, and ecological manufacturing paradigm. Full article

Retreading is a cornerstone in the remanufacturing process of tires, facilitating the extraction of maximum kilometers (Km) from a tire carcass. Tire remanufacturing plays a crucial role in conserving raw materials, reducing environmental impacts, and lowering the overall operating costs. This study employs predictive modeling techniques to forecast tire performance and optimize resource allocation, departing from traditional approaches, for a bus transport system in India. Machine learning models, including linear regression, ensemble boosted trees, and neural network models, were used. Two scenarios were devised: Scenario I addressed premature failures and optimizing performance to reduce tire procurement and Scenario II used targeted interventions, such as eliminating new tire condemnations and optimizing retread (RT) strategies, and could potentially salvage 169 tires from premature retirement. The results achieved R² values of 0.44, 0.51, and 0.45 and improved values for the test datasets of 0.46, 0.52 and 0.44. By leveraging these models, decision-makers can substantially improve tire mileage, reduce premature condemnations, increase tire production, and drive cost savings in fleet operations. Notably, this approach contributes to enhanced operational efficiency and promotes sustainability by cutting costs by 15–25%, improving tire mileage by 20–30%, and reducing environmental impacts by up to 25%. These results demonstrate the broader implications of predictive modelling as a decision-support tool, highlighting its capacity to drive economic and environmental benefits across industrial logistics and sustainable development. Full article

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance. Full article

The growing need for sustainable and flexible automation solutions has led to the exploration of transforming traditional industrial robots into collaborative robots (cobots). This paper presents a framework for the conversion of conventional industrial robots into safe, intelligent, and sustainable cobots, leveraging advancements in artificial intelligence and computer vision and the principles of the circular economy. The proposed modular framework contains key components such as visual perception, cognitive adaptability, safe human–robot interactions, and reinforcement learning-based decision-making. Our methodology includes a comprehensive analysis of safety standards (e.g., ISO/TS 15066), robot typologies suitable for retrofitting, and sustainability strategies, including remanufacturing and lifecycle extension. A multi-phase implementation approach is laid out for a theoretical design to contribute to the development of cost-effective and environmentally responsible robotic systems, offering a scalable solution for extending the usability and social acceptance of legacy robotic platforms in collaborative settings. Full article

Micro-, small-, and medium-sized enterprises (MSMEs) have the opportunity to increase resource efficiency, decrease waste, and promote sustainability by implementing remanufacturing techniques. However, determining whether MSMEs are prepared for the adoption of such techniques requires a methodical approach that considers several aspects of manufacturing readiness, such as core acquisition, the design of remanufacturing, and others. Therefore, this study proposes a framework to measure the readiness for the adoption of remanufacturing practices. Further, the remanufacturing readiness index (RRI) is proposed by combining remanufacturing indicators with a structural approach based on graph theory and matrices (GTM). Nine remanufacturing readiness attributes are identified through the literature and validated by an expert team. These nine attributes include core acquisition, reverse logistics availability, resource availability for remanufacturing initiatives, the design for remanufacturing, enterprise collaboration, remanufactured product positioning, performance measurement, labor skill and availability, and a flexible remanufacturing system. The finalized remanufacturing readiness attributes are modelled using GTM to explore their interdependencies, forming the basis for a quantitative index (RRI) that reflects MSMEs’ readiness for the adoption of remanufacturing. It is used to measure the possibility of MSMEs implementing remanufacturing processes. To illustrate the efficacy of the RRI, a case study of a remanufacturing facility was conducted. This RRI acts as a decision-support tool to help MSMEs, industry stakeholders, and governments identify priority areas for intervention, promote resource efficiency, and create sustainable growth. The results highlight the importance of readiness attributes as fundamental components in implementing remanufacturing practices at the MSME level. Full article

The widespread scrapping of retired mechanical parts has led to severe waste of resources and environmental burdens, posing a significant challenge to sustainable industrial development. To enable efficient recycling of retired mechanical parts and enhance the sustainability of their remanufacturing processes, the concept of biological genes is adopted to characterize the changes in the information of retired mechanical parts during the remanufacturing process as gene mutations of parts, aiming to maximize remanufacturing potential and devise an optimal generalized remanufacturing strategy for extending part life cycles. However, gene mutation of retired mechanical parts is not an isolated event. The modification of local genes may disrupt the original equilibrium of the part’s state, leading to conflicts such as material–performance, structure–function/performance, and function–performance. These conflicts constitute a major challenge and bottleneck in designing generalized remanufacturing schemes. Therefore, we propose a conflict identification and resolution method for gene mutation of retired mechanical parts. First, gene mutation graph of retired mechanical parts is established to express its all-potential remanufacturing pathways. Using discrimination rules and the element representation method from extenics, mutation conflicts are identified, and a conflict problem model is constructed. Then, the theory of inventive problem solving (TRIZ) engineering parameters are reconstructed and mapped to the mutation conflict parameters. The semantic mapping between the inventive principles and the transforming bridges is established by the Word2Vec algorithm, thereby improving the transforming bridge method to generate conflict resolution solutions. A coexistence degree function of transforming bridges is proposed to verify the feasibility of the resolution solutions. Finally, taking the generalized remanufacturing of a retired gear shaft as an example, we analyze and discuss the process and outcome of resolving gene mutation conflicts, thereby verifying the feasibility and effectiveness of the proposed concepts and methodology. Full article

Background: The global climate crisis has intensified the demand for sustainable solutions, positioning Reverse Logistics (RL) as a critical strategy for minimizing environmental impacts. Simultaneously, Industry 4.0 technologies are transforming RL operations by enhancing their collection, transportation, storage, sorting, remanufacturing, recycling, and disposal processes. Understanding the roles of these technologies is essential for improving efficiency and sustainability. Methods: This study employs a systematic literature review, following the PRISMA methodology, to identify key Industry 4.0 technologies applicable to RL. Publications from Scopus and Web of Science were analyzed, leading to the development of a theoretical framework linking these technologies to RL activities. Results: The findings highlight the fact that technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Blockchain enhance RL by improving traceability, automation, and sustainability. Their application optimizes execution time, reduces operational costs, and mitigates environmental impacts. Conclusions: For the transportation and manufacturing sectors, integrating Industry 4.0 technologies into RL can streamline supply chains, enhance decision-making, and improve resource utilization. Smart tracking, predictive maintenance, and automated sorting systems reduce waste and improve operational resilience, reinforcing the transition toward a circular economy. By adopting these innovations, stakeholders can achieve economic and environmental benefits while ensuring regulatory compliance and long-term competitiveness. Full article

This paper compares the performance of Deep Learning (DL) and multi-template matching (MTM) models for detecting small defects. DL models extract distinguishing features of objects but require a large dataset of images. In contrast, alternative computer vision techniques like MTM need a relatively small dataset. The lack of large datasets for small metal-surface defects has inhibited the adoption of automation in small-defect detection in remanufacturing settings. This motivated this preliminary study to compare template-based approaches, like MTM, with feature-based approaches, such as DL models, for small-defect detection on an initial laboratory and remanufacturing industry dataset. This study used You Only Look Once v5 (YOLOv5) as the DL model and compared its performance against the MTM model for small-crack detection. The findings of our preliminary investigation are as follows: (i) YOLOv5 demonstrated higher performance than MTM in detecting small cracks; (ii) an extra-large variant of YOLOv5 outperformed a small-size variant; (iii) the size and object variety of the data are crucial in achieving robust pre-trained weights for use in transfer learning; and (iv) enhanced image resolution contributes to precise object detection. Full article

The adoption of remanufacturing technology is gaining traction, considering sustainability principles and the goal of fostering a resource-efficient society. However, given the unique environment of construction sites and the context of incorporating lean production into remanufacturing, implementing remanufacturing concepts in the construction industry presents significant obstacles. The goal of this article is to provide guidance and recommendations for construction professionals when developing remanufacturing plans, including circumstances, insights, and methodology for implementation. Initially, this study distinguishes the widely used ‘3R’ terminology (reduce, reuse, and recycle) from the concept of remanufacturing applicable to the construction industry. It then investigates the characteristics of the ‘core’ (items to be remanufactured) of construction components, as well as evaluates and restructures key influencing aspects associated with remanufacturing techniques. A careful assessment of the literature and detailed descriptions help to clarify these factors. The findings show that these criteria have a double impact on remanufacturing and that successful remanufacturing techniques necessitate a mix of flexibility, safety, and stability. Finally, a ‘tumbler’ approach was offered for experts in construction component professionals, allowing key influencing factors to play a more inclusive and dependable role in the development of remanufacturing strategies. Full article

Background: Remanufacturing products for sustainability involves layout and production planning, tools and equipment, material arrangement and handling, inventory management, technology integration, and more. This study presents an empirical vision through a discrete event simulation (DES) model integrating lean manufacturing (LM) and supply chain (SC) strategies with industry 4.0 (I4.0) technologies, applied to a case in a railway company. Methods: The work presents scenarios following a methodology with an incremental approach to implement strategies of lean manufacturing (LM) and supply chain (SC) in the context of I4.0 and their effects represented in DES models with applicability in remanufacturing and production line management. Five simulation scenarios were analyzed according to strategies layered incrementally. Results: Behaviors and outcomes were compared across the scenarios considering the remanufactured engines, percentage of process time, human labor occupation, and the statistical analysis of the process capability. Scenario five achieved the objective of remanufacturing 40 engines in one year with a cycle time of 214.45 h. Conclusions: The purpose was to design an engine remanufacturing line incorporating LM and SC strategies via a DES model, highlighting the importance of their gradual adoption toward I4.0 implementation. The integration of previous strategies improves flexibility and productivity in manufacturing processes. Full article

In the face of escalating environmental degradation and dwindling resources, the imperatives of prioritizing environmental protection, and conserving resources have come sharply into focus. Therefore, remanufacturing processing, as the core of remanufacturing, becomes a key step in solving the above problems. However, with the increasing number of failing products and the advent of Industry 5.0, there is a heightened request for remanufacturing in the context of environmental protection. In response to these shortcomings, this study introduces a novel remanufacturing process planning model to address these gaps. Firstly, the failure characteristics of the used parts are extracted by the fault tree method, and the failure characteristics matrix is established by the numerical coding method. This matrix includes both symmetry and asymmetry, thereby reflecting each attribute of each failure feature, and the remanufacturing process is expeditiously generated. Secondly, a multi-objective optimization model is devised, encompassing the factors of time, cost, energy consumption, and carbon emission. This model integrates considerations of failure patterns inherent in used parts and components, alongside the energy consumption and carbon emissions entailed in the remanufacturing process. To address this complex optimization model, an improved teaching–learning-based optimization (TLBO) algorithm is introduced. This algorithm amalgamates Pareto and elite retention strategies, complemented by local search techniques, bolstering its efficacy in addressing the complexities of the proposed model. Finally, the validity of the model is demonstrated by means of a single worm gear. The proposed algorithm is compared with NSGA-III, MPSO, and MOGWO to demonstrate the superiority of the algorithm in solving the proposed model. Full article

The green ecological property is increasingly recognized as a sustainable option in remanufacturing. In practice, despite the environmental concerns expressed by consumers, the growth of the remanufacturing industry falls short of expectations, largely due to the fact that, although remanufacturing conserves costs and is beneficial to our environment, it may not be a profitable option for OEMs, a major barrier in its appearance to green consumers. Many researchers have proposed that environmental groups and similar entities should provide necessary subsidies to encourage green consumerism and improve the profitability of the remanufacturing industry. In this paper, we intend to address how the subsidy policy on green consumerism impacts an OEM’s incentives in remanufacturing; more specifically, we develop two theoretical models: Model N, in which the OEM does not undertake remanufacturing, and Model R, in which the OEM does undertake remanufacturing under the subsidy policy for encouraging green consumerism. In addition to confirming the traditional wisdom that the subsidy policy encourages the remanufacturing industry, our analysis surprisingly reveals that, if a few consumers characterize green consumerism, the aggressive subsidy policy may potentially result in a cannibalization problem, in which remanufacturing hurts the OEM’s overall profits. Therefore, the government and environmental protection agencies need to fully consider the characteristics of the industry to avoid a “good intention, bad result” situation. Full article

This study developed and validated cost–benefit models to evaluate the economic feasibility of reuse and repurposing strategies in remanufacturing, utilizing activity-based costing to assess key financial factors and implications. The models provide a structured approach to compare reuse, repurposing, and recycling, focusing on identifying conditions that maximize cost savings and reduce environmental impact. Reuse strategies emphasize scenarios requiring minimal maintenance to extend product life, while repurposing explores transformations for new applications when direct reuse is not feasible. By quantifying reuse and repurposing costs, the models help manufacturers identify sustainable lifecycle extensions that support circular economy principles. The results demonstrate that reuse and repurposing are particularly advantageous when products retain significant remaining useful life. These models serve as practical tools for industries aiming to implement resource-efficient practices that enhance both economic resilience and environmental sustainability. Furthermore, these models can be adapted for specific industrial applications and enhanced with real-world validation, providing companies with actionable insights to further refine cost-saving and environmental impact predictions. This study addresses gaps in the current literature by presenting tailored cost assessment tools for circular remanufacturing, promoting informed decision making for sustainable manufacturing. Full article

In light of growing global supply chain instability and carbon neutrality initiatives, South Korea has highlighted the need for a circular economy to reduce its reliance on natural resources. As a critical strategy for promoting a circular economy, remanufacturing has become essential because of its ability to improve resource efficiency and reduce environmental impacts. The automotive sector, which accounts for 80% of the remanufacturing industry, plays a critical role in these efforts. Turbochargers, primarily made of cast iron, represent approximately 20% of sales in this sector and are significant contributors to greenhouse gas emissions, making them an important target for emission reduction. This study examined the greenhouse gas emissions associated with turbochargers across multiple remanufacturing cycles using the LCA method. The results indicated an approximate decrease of 50%, 48%, and 46%, based on a comparative analysis between brand-new products and those remanufactured one to three times. Comparing brand-new and remanufactured products does not fully capture the key advantage of remanufacturing. This advantage lies in its ability to extend a product’s life cycle by using core parts as primary raw materials and reducing the consumption of new resources. Therefore, it is important to consider the environmental impact of remanufacturing within an expanded process, where brand-new products are included in the entire life cycle. Using this approach, the accumulated annual greenhouse gas reduction effect for multiple remanufacturing indicated decreases of approximately 25%, 32%, and 35% for remanufacturing one, two, or three times, respectively, compared to using only brand-new products. This study shows that multiple remanufacturing reduces greenhouse gas emissions compared to the use of brand-new products. In particular, as remanufacturing is repeated, the product lifespan can be extended from 3 years to up to 12 years with a concomitant decrease in annual greenhouse gas emissions. These findings provide valuable data for modeling and encouraging the greenhouse gas reduction potential driven by remanufacturing across various industrial sectors. Full article