The manufacturing industry needs to change its performance assessment methods because Industry 5.0 introduces new human-centric design standards, sustainable practices, and intelligent collaboration systems. In this context, performability is defined as the assessment of a system’s complete capability to provide consistent and dependable performance. This study aims to develop a new understanding of performability within the context of Industry 5.0 by integrating human-centricity, sustainability, resilience, and digital intelligence into a unified conceptual framework that connects intelligent technologies with closed-loop supply chains, remanufacturing systems, and dynamic warranty strategies to enhance sustainable and resilient industrial operations. Based on an examination of more than 50 recent studies, this study demonstrates how predictive maintenance, smart contracts, and digital twins have advanced the field, while proposing an integrated performability model that supports circular economy objectives. The applicability of the framework is demonstrated through multiple real-world extensions that span the electronics, automotive, and agriculture industries. Key challenges, which include interoperability, ethical governance, and system expansion, are addressed while creating a detailed research path for upcoming investigations. The study position performability as a fundamental element that enables sustainable value development in Industry 5.0 environments while delivering essential information for researchers, practitioners, and policymakers.
The paper describes the experience gained from designing robotic tools for Electric Vehicle (EV) Battery Packs (BPs), specifically, pack-to-module, non-destructive, and partially destructive disassembly with operator support. The design evaluates the technological requirements for disassembly, involving the risk of potentially explosive atmospheres (ATEX) in the region around the BP. The study optimises the number of robotics tools, adopting solutions that privilege commercial components to reduce costs and for straightforward industrial adoption. The work proposes solutions in accordance with current international standards and details the implementation process.
This review examines the current research space within human-robot collaboration (HRC) in remanufacturing. Specifically, we present a 6-level HRC hierarchy tailored to the high-uncertainty and high-variability environment of remanufacturing, detailing how collaboration must evolve from simple coexistence to knowledge-driven communication to manage the complexities of EOL disassembly. Each HRC system is classified, in order, into one of the following categories: coexisting, relying, accounting, recognizing, instructing, and communicating. Additionally, a separate review examines the current landscape of human factors research within remanufacturing. This review groups the research into distinct evaluations: trust in automation, workload, fatigue, usability, and ergonomics. Finally, key robotics features for HRC systems are highlighted to provide a comprehensive guide for system development. Although some studies incorporate human factors measurements also propose HRC systems, the opposite is not true, and the majority of HRC studies do not consider human factors when developing their systems. Additionally, more recent studies have regressed in their integration of robotic teaming systems, with recent studies proposing simpler, less collaborative systems. This review highlights the importance of a human-centered approach to the ever-growing remanufacturing landscape.
Various repairability indices have been developed to indicate how easily electronic products can be repaired and to inform consumers. These indices consist of different repair criteria, each of which is assigned a score, and an aggregated total score. However, this approach can be misleading: products that score low on critical criteria can still achieve a high overall repairability score. This masks repair obstacles that only become apparent during the actual repair work, limiting the practical value of these indices. Furthermore, these indices treat printed circuit boards (PCBs) as whole units that need to be replaced, overlooking the unique repair challenges that arise at the PCB level. However, to fully support the circular economy, repairability must be considered at all product-levels. This study addresses these shortcomings by proposing a new, technically grounded repairability index for general purpose PCBs, using power electronic products as a sample case. The index includes PCB-specific repair criteria and warning indicators to identify repair obstacles. It was developed combining repair process modeling, semi-structured interviews with repair experts and a review of existing indices. The final index consists of five weighted criteria: traceability, design features, disassembly/reassembly, diagnostics and spare parts. The weightings were determined based on a survey of 50 electronics and eco-design experts. Presented in the form of a simple tool with built-in warning indicators, this index is intended to promote practical engagement with circular economy and eco-design principles at the PCB-level and provide actionable guidance to manufacturers, service providers and policy makers.
Growing environmental awareness and the increasing emphasis on sustainability have heightened the focus on circular economy models, particularly remanufacturing, which extends product life cycles through systematic refurbishment. Remanufacturing operations encompass three key stages: (1) remanufacturing, (2) stock management, and (3) assembly. This study optimises these interconnected processes while explicitly considering timing-related uncertainties in the remanufacturing–assembly system that impact operational performance. A remanufacturing production control model is developed to coordinate the disassembly of end-of-life products, remanufacturing of components, and hybrid assembly of final products. These finished products may integrate both remanufactured and new components, requiring effective material flow management to maintain operational efficiency and quality standards. The objective of this study is to support decision-makers in maintaining efficient production flows, meeting customer demand, and mitigating system uncertainties that may arise throughout the remanufacturing-assembly workflow. To achieve this, key performance metrics such as throughput, machine-related disruptions, and material release are continuously monitored to guide system optimisation. A reinforcement learning (RL)-based approach is proposed to determine the optimal material flow, service level management, and on-time delivery of remanufactured products within a hybrid assembly environment. The assembly process operates under three distinct operational modes, allowing the RL agent to dynamically adapt its decision-making strategy based on real-time system conditions. Additionally, the agent explicitly accounts for machine failures in its decision framework to proactively manage disruptions and maintain a stable material flow. Simulation results demonstrate that the decentralised RL-based production control system outperforms conventional heuristics, achieving an average cumulative reward improvement of approximately 4
Mineral extraction and processing equipment, particularly tricone drill bits and cone crushers, play crucial roles in rock fragmentation for blasting and ore comminution for grinding in mining. Understanding their operation, material composition, and operating environment is essential for analysing failure modes and mechanisms to propose wear resistant materials and remanufacturing strategies for prolonging their life span to enhance their efficiency. This review examined the operation, material composition, and operating environment of drilling and crushing components, characterising their failure modes and mechanisms to identify mitigation measures. Subsequently, wear-resistant materials tailored to predominant failure modes and suitable remanufacturing techniques are discussed. A techno-economic analysis methodology was presented to review the selected material and the remanufacturing technique, comparing their economic feasibility with replacement options. The novelty of this study is that it integrates failure analysis, remanufacturing methods and a techno-economic evaluation into a practical remanufacturing framework that the mining industry can follow to assess and remanufacture failed components. A composite of H13 matrix with TiC reinforcement is proposed for the steel body of the tricone drill bit, while Rockit 606 powder was recommended for the cone crusher mantle liner. Hybrid remanufacturing was suggested, employing gas tungsten arc welding (GTAW) for the tricone drill bit body and submerged flux core arc welding (FCAW-S) for the mantle liner. The direct energy deposition technique (laser cladding) is proposed to deposit the selected wear-resistant material as a protective coating, offering a significantly improved lifespan. The goal is to improve mining efficiency, mitigate downtime, optimize performance, coat, productivity and support sustainable remanufacturing practices.
Manufacturers adopt different warranty strategies for remanufactured products, offering shorter, identical, or longer warranty periods compared to new products. However, prior research has only focused on manufacturers offering either shorter or identical warranties. In addition, the existing literature has not captured the diminishing returns of warranties, i.e., as the warranty coverage increases, its incremental benefits begin to decrease but the costs continue to increase. To address these gaps, we develop an optimization model that jointly considers pricing and warranty decisions while accounting for warranty’s diminishing effect on consumer’s willingness to pay for remanufactured products. We show that all three observed warranty strategies for remanufactured products (shorter, identical, or longer) can be optimal, depending on specific market and remanufacturing conditions, which we systematically identify. Our findings also reveal that extending warranty coverage is not always an effective strategy for expanding the remanufacturing market, particularly under extremely favorable or unfavorable market conditions. Our results provide valuable insights and practical guidance for manufacturers in making warranty decisions on remanufactured products.
Tool wear has a significant impact on product quality, process reliability, and overall productivity in machining operations. Although the prevailing data-driven approaches are dominantly dependent on a single dataset, traditional Pearson’s Correlation Coefficient (PCC)–based feature selection, or stand-alone machine learning models, their performances are often restricted by feature redundancy and limited model diversity. In this context, this article presents a dual-dataset validated, hybrid feature selection technique that combines PCC with Random Forest (RF) importance scores, along with a heterogeneous stacking ensemble paradigm for classification and regression tasks. The proposed approach has been evaluated using the NUAA Ideahouse dataset and an in-house experimental milling dataset based on comprehensive time-domain and frequency-domain feature extraction and preprocessing. The proposed Stacking Ensemble Learning Classifier (SELC) achieves accuracies of 0.92 and 0.95 for tool-state classification and outperforms stand-alone machine learning and ensemble learning baselines. In the case of tool wear prediction, the Stacking Ensemble Learning Regressor (SELR) achieves the highest R² values of 0.90 and 0.92 for the two datasets. The results demonstrate that the combined hybrid feature selection and stacking-based architecture has greatly enhanced generalization and prediction performance compared to conventional methods in the literature.
The healthcare system in Nigeria and many developing countries struggles with inadequate medical equipment, which is responsible for high mortality and morbidity rates in this region. The burden of the cost of imported medical equipment in Nigeria is high, hence the need for an alternative solution. This study explores the feasibility of adopting remanufacturing techniques in Nigeria for improved access to healthcare using qualitative analysis approaches. This study purposively recruited 12 experts, due to their involvement with medical equipment and the healthcare system, for a focused group discussion in November 2024. The interviews were transcribed and coded to identify key themes using content analysis. The key concerns for discussion were: challenges in medical equipment remanufacturing, perspectives on remanufacturing, remanufacturing as a strategy to improve healthcare in Nigeria, and legal and policy implications. The barriers were identified as: bureaucracy of processes, lack of local manufacturing capacity, delay in delivery time, and unavailability of spare parts. The motivators were identified as: retention of healthcare practitioners, cost reduction in healthcare services, improved access to medical equipment, and capacity building for healthcare professionals. The guidelines suggested from this study include: enhancement of medical equipment remanufacturing in Nigeria, proper documentation, government involvement, foreign joint venture agreement, public-private partnership, formulation of a remanufacturing act or law, development of a database of defective medical equipment, and quality assurance. In conclusion, this study revealed the essential function of remanufacturing in strengthening healthcare systems, boosting service delivery and outcomes, building local capacity, and minimizing expenses.
End-of-life product recovery is mandatory for some industries and presents opportunities and challenges for (re-)manufacturers. Disassembly planning models can help decision-makers save costs and make processes more sustainable. In a disassembly facility dealing with various product types and utilizing specialized workstations with different equipment, multiple decisions arise regarding the priority of jobs and the time-wise allocation of tasks to resources. This work presents an overview of the literature on sequencing and scheduling decisions in the disassembly shop. Since the models under review do not uniformly classify within the existing taxonomy of disassembly problems, the term disassembly shop floor scheduling (DSFS) is used. In total, 58 research articles are identified and classified systematically by searching and analyzing the literature. After discussing the different problem formulations and solution approaches, the review outlines important directions for further research, including job shop-type disassembly and the consistent use of benchmark instances for DSFS.
Remanufacturing is a key process for enabling a circular economy by restoring used products, often referred to as cores, to a like-new condition. It still heavily relies on manual work. One exemplary manual task is the visual inspection of cores before further processing. This manual effort arises from uncertainties, such as varying product conditions, a broad variety of product variants, and the lack of product information to support automation. To address these challenges and to enable the automation of visual inspection tasks, flexible and adaptive inspection systems are required. These systems must be capable of automatically detecting defects and performing product-specific inspections across a wide range of product variants. Therefore, this work introduces a semantic 3D product modelling method that integrates two-dimensional (images) and three-dimensional (point cloud) data. With the resulting semantic 3D model, the semantic information, such as detected defects and components requiring closer inspection, can be encoded on a geometric product model. This model provides the information basis for an adaptive inspection approach. Using semantic (U-Net) and instance segmentation approaches (Mask-RCNN), the proposed method assigns each surface point of the 3D model to a specific component, thereby creating the semantic 3D product model during the inspection process. The results show that the method presented can achieve semantic 3D modelling both in accuracy and model completeness, encoding component information on the geometric product model. Furthermore, we show that the U-Net architecture used to detect components is also able to detect corrosion as one exemplary defect type, enabling the encoding of various semantic information into the semantic 3D product model. This semantic 3D product model then enables targeted individual inspection of these semantically mapped components and defects on the product model in a later stage of an automated inspection procedure.
The increasing shift towards electrification in the automotive industry has highlighted the critical need for a large-scale and efficient processing of end-of-life (EoL) lithium-ion batteries (LiBs) from electric vehicles (EVs). Linear EoL strategies, such as landfilling, pose significant environmental hazards, while recycling, repurposing, and remanufacturing offer more sustainable alternatives; however, these alternatives require scalable, safe, and cost-efficient disassembly processes. To support circular EoL practices, a prototype of a robotic cell was proposed, presenting a novel automated disassembly process for EV-LiB modules, addressing challenges in the areas of safety and cost-effectiveness. Safety was addressed by encapsulating the work area with protective windows and implementing a safety system that suspends operation if safety conditions are not met. Cost-effectiveness was achieved by implementing a cell layout and robot program that can easily be reconfigured to be compatible with a range of Battery module designs available in the market, and utilising low-cost tools. This paper also presents a methodology for assessing the economic impact of the robotic cell. Initial assessments conducted on a mock-up Samsung model 12S1P Battery module demonstrate that the robotic cell can match the productivity of 5.50 workers within a workweek, saving over £15,000 per month in operational expenses, leading to a potential payback period of 1.33 years. This research marks a step towards realising scalable and financially feasible robotic disassembly systems for EoL EV-LiBs, promoting a circular economy in the automotive sector.
This paper explores the transformative potential of reuse and repurposing strategies in the manufacturing industry, with a focus on sustainability and circular economy principles. Through an extensive review of 115 articles from Scopus using 6 different queries for broad topic coverage, the study provides a comprehensive analysis of reuse and repurposing frameworks, highlighting their benefits and challenges. The research identifies three essential pillars for successful implementation: product design, operational processes, and consumer acceptance. The study emphasizes the significance of modular designs, lifecycle extensions, and advanced cost models to maximize resource efficiency and sustainability. The paper also discusses the integration of advanced technologies, such as blockchain, artificial intelligence, and IoT-enabled systems, to enhance traceability, streamline reverse logistics, and optimize predictive maintenance. Automated disassembly and collaborative robotics are highlighted as critical enablers for efficient and scalable reuse operations. Furthermore, the study advocates for flexible supply chains and real-time data analytics to address uncertainties in core acquisition and product variability. Despite the progress, the lack of standardized frameworks and metrics remains a significant barrier to widespread adoption. The paper concludes by proposing a cohesive framework that incorporates technological advancements, regulatory support, and collaborative approaches to overcome these limitations. This study provides actionable insights to foster a scalable, transparent, and sustainable manufacturing ecosystem.
Remanufacturing adds value to post-use products by replacing or reprocessing components. Despite the growing market for used vehicles and rising maintenance costs in Brazil, remanufactured products face significant challenges in gaining traction, particularly in the automotive sector. While existing literature highlights global trends, the Brazilian market faces unique regulatory, cultural, and logistical barriers that hinder the development of remanufacturing practices. Moreover, empirical studies addressing these specific challenges in Brazil remain scarce, especially those that incorporate insights from multiple stakeholders across the remanufacturing value Chain. This study aims to identify these barriers and propose targeted strategies to overcome them in the automotive sector, contributing to the advancement of remanufacturing practices in Brazil. Drawing on interviews with 11 automotive remanufacturing specialists, including professionals from Original Equipment Manufacturers (OEM), Tier 1 suppliers, and other key stakeholders, the study employs Content Analysis and Descriptive Statistics to analyze the barriers faced by the sector. The research identifies four key themes: Market Insights; Government Influence and Reverse Logistics; End-of-Life Product Management; and Sustainable Core Return Strategies. Major barriers include regulatory deficiencies, a repair-oriented consumer culture, informal market competition, lack of traceability in core management, and ineffective product portfolio strategies. The study proposes actionable recommendations such as tax incentives, regulatory updates, improved traceability systems, and targeted communication campaigns to raise awareness of remanufactured products. By integrating technical, regulatory, and cultural dimensions, the research bridges an important gap in the Brazilian literature and offers practical insights to policymakers, industry leaders, and other stakeholders interested in fostering a more sustainable and competitive remanufacturing ecosystem.
On a global scale, the circular economy is essential for reducing raw material consumption and increasing sustainability. Remanufacturing is a distinctive process within this economy, transforming used products to “as-new” condition, ready for a second life. To meet the quality and safety standards of new products, remanufacturing relies on comprehensive assessments phases led by skilled operators. However, despite their critical role in ensuring product reliability, little attention has been given to the specific skills operators develop to assess components. This study addresses this gap by investigating how expert operators manage variability in train component remanufacturing, developing specialized skills through strategies to assess and repurpose high-stakes components. Using an activity-oriented ergonomics approach, data was gathered through in situ observations, and operator interviews. Findings reveal how operators integrate both old and new parts to create “as-new” products that meet stringent performance and safety standards, specific to the requirements of train components. A key contribution is the identification of remanufacturing-specific skills: including diagnosis, where operators assess the current state of components to determine their reparability, and prognosis, where they anticipate the remaining lifespan and functional reliability of components. These insights reveal operators’ sensory strategies and their anticipation of component degradation, allowing them to adapt parts for a second life while ensuring optimal functionality. This research sheds light on the characteristic skills required in remanufacturing and underscores the essential role of operator assessment strategies in advancing sustainable production.
Ground exploring tools (GETs), used in coal mining industries, encounter severe failure due to their continuous pressing and scratching against the coal seam embedded with hard bands and impurities. Failure of GETs lead to direct cost expenditure due to replacement of worn-out components; besides, significant indirect cost resulting from machine downtime when they are removed, and new ones are reinstalled. Mining businesses replace worn GETs with new parts at an exorbitant cost at a great risk to their sustainability. Replacement goes against the ethos of the circular economy (CE) philosophy which aims at ensuring highest value of resource utilisation while eliminating waste by improving the design of materials, products, and systems. A critical analysis of the approaches of CE for restoring damaged GETs reveals remanufacturing is the best option to adopt to keep GETs in good working conditions. Meanwhile, there is scanty literature to guide remanufacturing practitioners on materials selection and framework for implementing remanufacturing of damaged GETs. This review addresses this challenge by identifying appropriate wear-resistant materials and the most economically feasible remanufacturing technology which restores the performance of GET’s components to at least as new upon remanufacturing. Using the components of continuous miner (CM) as a case study, the operating environments in which GETs function are described to gain insight into the modes of failure encountered. Information gathered from the operation environments of the GETs and their failure modes assisted in selecting appropriate wear-resistant materials. Techno-economic analysis of the remanufacturing of various modes of failure of the components of GETs was carried out to ascertain the economic feasibility of remanufacturing various failure modes. Future perspectives of failure analysis, material selection, and framework for implementing remanufacturing of various failure modes (based on severity of damage) in GETs are presented. This review extends the frontier of knowledge in the fields of GETs remanufacturing and potential wear-resistant materials for GETs to academic researchers and industrial practitioners.
This study investigates the direct and mediated impact of ethical sensitivity, pro-environmental self-identity, and ecological consciousness on consumers’ intentions to purchase remanufactured electronic devices, focusing on computers, through the lens of the Value-Belief-Norm (VBN) theory and Self-Identity Theory. Utilizing a cross-sectional research framework, data from 349 respondents were analyzed using Structural Equation Modeling (SEM). The findings reveal that ethical sensitivity and pro-environmental self-identity significantly enhance purchasing intentions, with ecological consciousness serving as both a direct predictor and a mediator. Interestingly, perceived costs negatively influence purchasing intentions, while perceived benefits emerge as a strong positive determinant. However, heightened price consciousness showed no significant effect, suggesting a shift in consumer priorities towards value and sustainability over cost. These results highlight the critical role of aligning marketing strategies with consumers’ ethical and environmental values, emphasizing transparency in marketing and inventory management. The study also offers actionable insights for businesses and policymakers, including strategies to reduce cognitive burdens, foster pro-environmental identities, and enhance trust in remanufactured products. By addressing key psychological and practical barriers, this research contributes to advancing sustainability goals and promoting the circular economy in an era of escalating environmental challenges.
This paper considers a hybrid production-remanufacturing system of a single product, composed of an original equipment manufacturer (OEM) and a set of customers/retailers, operating under collection and remanufacturing targets. For this kind of production system, we address the problem of simultaneously determining the sizes of the lots of production, remanufacturing and transportation to and from customers, in order to meet their demand on time and collect used products to be remanufactured. Set-up and unit costs are incurred for the three activities, and unit costs for holding inventory for both used and serviceable items in the OEM but also in the customers. Recovery targets are stated as lower bounds on the number of used products that must be collected and remanufactured by the OEM. We provide a mixed integer-linear programming formulation for the problem and suggest a solution procedure based on the metaheuristic of Tabu Search. We propose and evaluate several variants of the heuristic procedure by extending a well-known set of instances from the literature. The suggested procedures are compared against a state-of-the-art optimization solver, under different parameter settings of interest such as set-up costs, inventory holding costs and recovery targets. The results obtained from the numerical experimentation allow us to conclude that at least one of the variants of the proposed heuristic is effective both in cost and running time, even outperforming the optimization solver for some of the large instances.
This paper focuses on designing a cellular manufacturing system as a step toward creating sustainable cells. The proposed design aims to manufacture final products and remanufacture the returned products to diminish waste, where the return rate for each product is known. Firstly, the product families are formed using the uncapacitated mathematical model. Secondly, a nonlinear stochastic mathematical model is utilized for the propounded design as a step toward creating sustainable cells, where machine duplications are allowed. Besides, a simulation model is developed to handle the uncertainty in demand. Further, the performance of the proposed design with flexible cells is compared to regular cells. The results explain the ability of the proposed design to minimize the total number of machines. Furthermore, allowing machine duplication in the proposed design enhances the system flexibility; thus, machine breakdown will not impact system performance. The total number of machines required to cover the demand increases as the risk level of demand cover decreases. Therefore, the average waiting time and average work in process increase.
In the era of environmental degradation and resource scarcity, the concept of circular economy (CE) has emerged as a pivotal strategy to transform the contemporary industrial landscape. As an integral component of the 10R framework, remanufacturing is emerging as a production strategy that revitalizes end-of-life (EOL) products to a like-new condition, fostering a more sustainable production and consumption. Despite its immense environmental and economic benefits, the implementation of remanufacturing practices is confronted with a multitude of challenges, including sourcing of EOL products, managing component variability, and arbitrary failure rates that result in major process inefficiencies. This paper embarks on the definition of functional and non-functional requirements for remanufacturing production planning and control (RPPC) to establish a systematic approach to address the existing challenges and uncertainties that arise in remanufacturing systems. Based on the synthesis of a comprehensive literature study, eight functional requirements and a total of 48 associated key performance measures are derived and contextualized in a coherent conceptual framework. This establishes a consensus to mitigate the impacts caused by uncertainty in remanufacturing. The feasibility of the conceptual framework is validated in an industrial case study with an OEM remanufacturer of electric power steering products. The findings of this research paper advance the field of RPPC and offer guidance to industrial decision-makers to evaluate and optimize their remanufacturing production systems.
