Closed-loop supply chain procedures provide manufacturers with a competitive advantage over their competitors. Also, the supply chain involves a manufacturer and customers, in which the manufacturer produces and sells products from the manufacturing market. Interestingly, combining forward and reversal strategies presents challenges on both the on-demand and supply sides of the market. When customers purchase products, they are uncertain about the quality of the product, and the manufacturer offers a warranty to assure the customers about the reliability and quality of the products. This paper presents a two-phase News-vendor framework for warranted and non-warranted products with inventory carryover to describe the best manufacturing and re(manufacturing) procedures. The demand is modeled in stochastic nature with one dimensional extended warranty for both new and reman products. Two scenarios are considered to demonstrate the importance of providing an extended warranty. Finally, the numerical analysis concludes that the expected profit affects the manufacturer's influence when providing a warranty and graphical results reveal that offering an extended warranty and lowering holding expenditure enhances the manufacturer's expected total profit over two phases.

As environmental problems become more apparent, manufacturers need to balance environmental considerations with economic activities. This is where closed-loop supply chains are gaining attention. However, in addition to demand fluctuations, which are a problem in conventional supply chains, a circular supply chain is unstable in terms of supply, where end-of-life products are collected and reused. This destabilizes not only excess inventory and shortages but also production resources, such as manpower, facilities, and raw materials. This study focuses on the stabilization of the manufacturing system in a closed-loop supply chain. To confirm the dynamic changes in the manufacturing system, we designed a simulation model of a closed-loop manufacturing system and conducted numerical experiments under several scenarios, taking the variation of manufacturing quantity per unit period as an evaluation measure of stability. After showing that unplanned remanufacturing destabilizes the recovery of reusable end-of-life products, we demonstrate that the manufacturing system can be stabilized by appropriately limiting the amount of remanufacturing. However, excessive limits reduce opportunities for remanufacturing end-of-life products and generate adverse economic and environmental impacts. To determine appropriate restrictions, it is necessary to consider the product currently in use by the customer as a virtual inventory and to consider factors such as the quality of the products in the virtual inventory. In the future, we plan to study a system that can dynamically manage remanufacturing quantities based on the status of virtual inventories.

In 2021, sales of electric bicycles in Germany increased compared to the previous year, leading to a 43% share of electric bicycles in the total German bicycle market. The German Bicycle Industry Association (ZIV) expects this growth to continue and that by 2025 every second bicycle sold in Germany will have an electric motor. Therefore, due to the increasing number of sales, both the built-in resources and the possible treatments after the use phase need to be considered regarding sustainable market development. In this context, remanufacturing is one promising end-of-life strategy that enables a new life cycle of products and components, thus reducing the consumption of natural resources and minimizing waste production. The used products are restored to their original conditions by disassembling, cleaning, sorting, reworking, and reassembling. Disassembly is a decisive process step as it creates the prerequisites for all further steps in the process chain and significantly determines the economic feasibility of a remanufacturing process. Therefore, this paper aims to evaluate the ease of disassembly of electric bicycle motors and thus determine their suitability for remanufacturing. To evaluate the disassemblability, the ease of Disassembly Metric (eDiM), a quantitative assessment method for evaluating disassembly effort, was adapted to the needs of the electric bicycle motors. The method was finally applied to five electric bicycle motors from established manufacturers and assessed their ease of disassembly. The results show that the electric bicycle motors can be disassembled without significant damage and classified as suitable for remanufacturing.

Value-retention processes (VRPs), a collective term that includes practices of direct reuse, repair, refurbishment, and remanufacturing, can facilitate the cycling of products and components within a circular economy (CE). VRPs are often presented as alternatives to conventional manufacturing and consumption, and as mechanisms for avoiding negative environmental impacts (e.g., landfill) and mitigating issues of material scarcity. However, these practices are typically lumped together under generic ‘reuse’ strategies within sustainable materials management programs and policies. Further, there is a lack of clarity and data regarding how VRPs differ, and the extent to which they contribute to the avoidance of negative environmental and economic impacts. Using novel integrated product-, process, and economy-level models, we quantify select environmental and economic impact metrics for VRPs and conventional manufacturing across six case study products, in two industrialized economies (USA and China). Using this novel methodology, we demonstrate a basis for clear differentiation of VRPs as distinct strategies within a CE, and show that each VRP offers differing forms of value (e.g. cost reduction, labor opportunity, and material retention) and differing degrees of environmental and economic impacts (e.g., primary material requirement, embodied emissions, process emissions). In all cases, the product- and process-level comparative analyses indicate that VRPs present a clear opportunity for significantly reduced environmental impacts, relative to conventional manufacturing. This novel methodology provides an adaptive, comprehensive model that can support the decision of whether or not to engage in VRPs. By quantifying and evaluating VRPs in terms of their relative environmental and economic performance, the distinct avenues, expectations and outcomes for CE can be better integrated across diverse industry and product portfolios (International Resource Panel [29]).

Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.