The value of regulating returns for enhancing the dynamic behaviour of hybrid manufacturing-remanufacturing systems

Several studies have determined that product returns positively impact on the dynamics of hybrid manufacturing-remanufacturing systems, provided that they are perfectly correlated with demand. By considering imperfect correlation, we observe that intrinsic variations of returns may dramatically deteriorate the operational performance of these closed-loop supply chains. To cope with such added complexity, we propose a structure for controlling the reverse flow through the recoverable stock. The developed mechanism, in the form of a prefilter, is designed to leverage the known positive consequences of the deterministic component of the returns and to buffer the harmful impact of their stochastic component. We show that this outperforms both the benchmark push system and a baseline solution consisting of regulating all the returns. Consequently, we demonstrate that the operation of the production system is greatly smoothed and inventory is better managed. By developing a new framework for measuring the dynamics of closed-loop supply chains, we show that a significant reduction in the net stock, manufacturing, and remanufacturing variances can be achieved, which undoubtedly has implications both for stock reduction and production stabilization. Thus, the known benefits of circular economy models are strengthened, both economically and environmentally

Forecast Model for Return Quality in Reverse Logistics Networks

Giving rise to the field of reverse logistics are the governmental legislations mandating used electronics take-backs and sustainable recovery, which often burden manufacturers with the challenge of high implementation costs but no guaranteed profitability. One way to tackle this challenge is to demystify the multi-faceted uncertainties of product returns, namely timing, quantity and quality, that currently inhibit optimal design and operations of reverse logistics networks (RLN). In recognition of the limitations particularly caused by uncertainty of returns’ quality in the strategic, tactical and operational planning of the RLN, this research seeks to develop a forecast model for the prediction of the returns’ quality of future electronics returns. The proposed forecast model comprehensively incorporates three major factors that affect quality decisions which are usage, technological age and remaining economic value of expected product returns to predict its quality grade. While technological age and economic trends can readily be established, the main complexity lies in modeling of usage-dependent reliability distribution of returned electronics. The novelty of the proposed forecast model lies in deducing usage distributions through segmentation of the consumer base by socioeconomic factors such as age, income, educational status and location. These usage distributions are then used to estimate remaining useful life of returned products and their components, the associated repair costs and the subsequent profitability of reprocessing based on economic value in the market. This research develops analytical models of expected return quality based on empirical usage distributions and pricing trends. The analytical models are then applied in Monte Carlo simulations to forecast expected returns’ quality from different regions, including large and small population centers, in Canada

Deep learning based vision inspection system for remanufacturing application

Deep Learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, localisation, natural language processing, prediction and forecasting systems. With significant applicability, Deep Learning is continually seeking other new fronts of applications for these techniques. This research is the first to apply Deep Learning algorithm to inspection in remanufacturing. Inspection is a key process in remanufacturing, which is currently an expensive manual operation in the remanufacturing process that depends on human operator expertise, in most cases. This research further proposes an automation framework based on Deep Learning algorithm for automating this inspection process. The proposed technique offers the potential to eliminate human factors in inspection, save cost, increase throughput and improve precision. This paper presents a novel vision-based inspection system on Deep Convolution Neural Network (DCNN) for three types of defects, namely pitting, surface abrasion and cracks by distinguishing between these surface defected parts. The materials used for this feasibility study were 100cm x 150cm mild steel plate material, purchased locally, and captured using a web webcam USB camera 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, especially for accuracy and speed. 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

Used product acquisition, sorting and disposition for circular supply chains: Literature review and research directions

The vision of a circular economy (CE) inspires firms, governments, and scholars alike. The transition is underway in both practice and the literature, but success depends on the effective implementation of circular supply chains (CSCs), which encompass acquiring used products, sorting them by type and quality, and deciding which to dispose to various processing options. We review 131 high-impact journal articles on returns acquisition, sorting, and disposition (ASD) over the decade 2012-2021 to assess the current status of ASD research for CSCs and to discuss important research directions for supporting the transition to a CE. Uniquely synthesising the state of the art on all these three overarching decision areas, we find aspects of CSCs prominent in the decade's research agenda, such as closed loop supply chain coordination and ASD for remanufacturing, and highlight growing coverage of behavioural considerations. Research applicability has been constrained by a lack of empirical studies, limited practical validation of mathematical models, a focus on economic objectives, and restrictive modelling assumptions about behaviour and uncertainty in returns. We recommend further research in each part of ASD to facilitate a CSC, and as a whole, for transitioning to a CE. CE concepts such as joint decision-making between product design and returns management, cross-sector collaboration, and product-service systems should inform the agenda for CSC research

Revenue management for multiple product recovery options : a triangulation approach

In recent times large numbers of end-of-use/end-of-life returns have been the result of the increasing pressure from environmental legislations, particularly the directive on Waste Electrical and Electronic Equipment (WEEE) in the European Union. These returns incur acquisition costs and take-back operation costs regarded as a sunk cost by many industries. Thus, returned/recovered product valuation and marketing issues become crucial factors for survival and profitability of many firms in various sectors in today’s competitive world. The research undertaken is relevant as pricing and revenue management for recovered products. Indeed, this theme is considered as a niche research and the fifth phase (prices and markets) of the evolution of closed loop supply chain research. Hence, it has been noted as one of the most critical research areas in quantitative modelling for reverse logistics and closed loop supply chain management studies. The research area is in its early stage because it can be seen that only a handful of articles have been published in peer reviewed international journals, exploring a pricing and marketing decision of recovered products. Hence, there are significant opportunities to conduct pricing and revenue management research in reverse logistics, particularly with regard to multiple recovery options.The primary objective of this research work is to formulate three pricing models by using a non-linear programming approach to determine optimal profit-maximising acquisition prices and selling prices, together with UK-based case studies in the mobile phone and computer recycling businesses. Moreover, this research aims to formulate two simulation models based on these case companies by investigating the impact of the uncertainty element in terms of return quantity and reprocessing time on firm’s profit. The triangulation approach is employed, specifically the multilevel model comprising case studies, questionnaire survey, and empirical quantitative models in order to address the principal research questions i.e. “What are optimal acquisition prices of received mobile phones and optimal selling prices of reprocessed handsets?”, “What are optimal selling prices of reprocessed computers?”, and based on the total profit, “What if the model's parameters change?”The contribution of this research covers the generation of pricing and simulation models that are suitable for the recycled mobile phone and computer sector. The literature review discovers that the research on this subject lacks considerations of multiple recovery options, return rate and demand rate as exponential functions, recovery capacity limitation, product substitution policy, the element of uncertainty in terms of return quantity and reprocessing time, and multiple time periods. Hence, this research fulfils six main research gaps in academic literature as follows. First, this study takes multiple recovery options into account. Second, return and demand rate are modelled as an exponential function. Third, pricing and simulation models cope with a limit to recovery capacity. Fourth, models with product substitution policy are investigated. Fifth, the element of uncertainty in terms of return quantity and reprocessing time is added into proposed models. Finally, this study proposes models with multiple time periods.The results from this research work support current pricing and revenue management research and most importantly, the results generated from these proposed models can enhance managers’ decision making in recovery operations and reverse logistics

Achieving remanufacturing inspection using deep learning

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

The Newsvendor Problem with Different Delivery Time, Resalable Returns, and an Additional Order

In a B2C scenario, the retailer is confronted with two kinds of demand. One requires an immediate delivery after placing an order, while the other prefers a delayed shipment due to some personal reasons. Considering demands for different delivery time, we explore a newsvendor model with resalable returns and an additional order to optimize the procurement decision under a stochastic demand distribution. The impact of the proportion of the instant delivery needs and the return rate on the order quantity and the expected profit is illustrated through numerical tests. It is shown that the expected profit decreases as the ratios of immediate delivery needs and returned products increase. Besides, if the sum of the percentage of the instant delivery needs and the return rate is less than 1, the expected profit is always greater than the result if the sum of them is equal to or greater than 1. Management implications are also discussed

Achieving quality medical equipment in developing countries through remanufacturing

Remanufacturing restores a used product to at least, its original equipment manufacturers (OEM) performance specification from the customer’s perspective and gives the resultant product a warranty that is at least equal to that of newly manufactured equivalent product. It is a wise option as it offers high quality products at lower price since remanufactured products are substantially cheaper than new products of equivalent quality. Remanufacturing also has social, economic, and environmental benefits since it has the potential to become a source of revenue, create jobs and reduce environmental pollution. While remanufacturing is common in industries such as automobile and aviation, its application and benefits in the medical device industry have not been investigated. Medical devices are crucial in the diagnosis and treatment of diseases and injuries but are inequitably distributed globally, such that there is acute shortage in developing countries with consequent high mortality rates over disease and adverse health conditions that could be treated if the right equipment were available. Several strategies have been considered to eliminate or mitigate this issue. However, neither has remanufacturing been considered a potential solution to this issue nor key factors in implementing medical equipment remanufacturing for developing countries been identified. This study proposes remanufacturing as a potential sustainable solution to this issue. The research was conducted in 3 phases following a multiphase mixed methods design. Questionnaires and interviews were used to gather data while pre-figured thematic analysis, Decision-making trial and evaluation laboratory (DEMATEL) technique and confirmatory factor analysis techniques were used to analyse the data. Main findings of this research include the following: (1) medical equipment remanufacturing can address 5 out of 11 causes of poor medical equipment availability accounting for 43.5% of the overall prominence. (2) A definition and decision support frameworks for medical equipment remanufacturing that could help to improve availability of quality medical equipment in developing countries (3) Major concerns in implementing medical equipment remanufacturing. (4) Impact of perception on the purchase intention for remanufactured medical equipment. This research is the first to identify the potential impact of remanufacturing in addressing medical equipment availability issues in developing countries, to characterise medical equipment remanufacturing towards this end. It is unique in its application of DEMATEL to the study of root causes of poor availability of medical equipment in developing countries and in applying behavioural science in understanding its purchase intentions.Remanufacturing restores a used product to at least, its original equipment manufacturers (OEM) performance specification from the customer’s perspective and gives the resultant product a warranty that is at least equal to that of newly manufactured equivalent product. It is a wise option as it offers high quality products at lower price since remanufactured products are substantially cheaper than new products of equivalent quality. Remanufacturing also has social, economic, and environmental benefits since it has the potential to become a source of revenue, create jobs and reduce environmental pollution. While remanufacturing is common in industries such as automobile and aviation, its application and benefits in the medical device industry have not been investigated. Medical devices are crucial in the diagnosis and treatment of diseases and injuries but are inequitably distributed globally, such that there is acute shortage in developing countries with consequent high mortality rates over disease and adverse health conditions that could be treated if the right equipment were available. Several strategies have been considered to eliminate or mitigate this issue. However, neither has remanufacturing been considered a potential solution to this issue nor key factors in implementing medical equipment remanufacturing for developing countries been identified. This study proposes remanufacturing as a potential sustainable solution to this issue. The research was conducted in 3 phases following a multiphase mixed methods design. Questionnaires and interviews were used to gather data while pre-figured thematic analysis, Decision-making trial and evaluation laboratory (DEMATEL) technique and confirmatory factor analysis techniques were used to analyse the data. Main findings of this research include the following: (1) medical equipment remanufacturing can address 5 out of 11 causes of poor medical equipment availability accounting for 43.5% of the overall prominence. (2) A definition and decision support frameworks for medical equipment remanufacturing that could help to improve availability of quality medical equipment in developing countries (3) Major concerns in implementing medical equipment remanufacturing. (4) Impact of perception on the purchase intention for remanufactured medical equipment. This research is the first to identify the potential impact of remanufacturing in addressing medical equipment availability issues in developing countries, to characterise medical equipment remanufacturing towards this end. It is unique in its application of DEMATEL to the study of root causes of poor availability of medical equipment in developing countries and in applying behavioural science in understanding its purchase intentions