The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Overview and classification of coordination contracts within forward and reverse supply chains

Among coordination mechanisms, contracts are valuable tools used in both theory and practice to coordinate various supply chains. The focus of this paper is to present an overview of contracts and a classification of coordination contracts and contracting literature in the form of classification schemes. The two criteria used for contract classification, as resulted from contracting literature, are transfer payment contractual incentives and inventory risk sharing. The overview classification of the existing literature has as criteria the level of detail used in designing the coordination models with applicability on the forward and reverse supply chains.Coordination contracts; forward supply chain; reverse supply chain

An integrated decision support framework for remanufacturing in the automotive industry

In today\u27s global economy, firms are seeking any and every opportunity to differentiate from competitors by reducing supply chain costs and adding value to end customers. One increasingly popular option, under growing consumer awareness and increasing legislation, is to reintegrate returned products into the supply chain to achieve economic benefits as well as improve sustainability. An important class of such reverse goods flows has to do with remanufacturing (reman), which refers to activities that restore returned products ( cores ) or their major modules to operational condition for using in place of new product or distributing through other channels (e.g., spare parts). While opportunities abound, some key complications reported in the literature include: 1) difficulty in timing the launch of reman product (while accounting for uncertainties associated with product life-cycle demand and core supply), 2) difficulty with capacity planning for remanufacturing (while accounting for the fact that volumes can be low and that facilities/lines should target multiple product families for economies of scale), and 3) operational difficulties in maintaining efficiencies in production planning and control of remanufacturing activities. These difficulties are mostly attributable to limited visibility and higher levels of uncertainty in reverse logistics (in comparison with forward logistics). Despite advances in the remanufacturing literature in the last two decades (both in the academic literature and practitioner community), there is no integrated decision support framework that can guide companies to successful launch and execution of remanufacturing operations. This is particularly true for companies that engage in both original equipment (OE) service as well as the independent after-market (IAM) in the automotive industry. This research aims to address these limitations by developing a decision support framework and necessary models for effective remanufacturing in the automotive industry. At the strategic level, we propose a unified approach to explicitly model and address issues of capacities as well timing the launch of remanufacturing programs for new product. We derive the optimal remanufacturing policy and extensively studied the drivers of cost-effective remanufacturing program for aftermarket services. Our policies exploit the ability to leverage OE production to support both the OE service operations as well as demand from the IAM. To the best of our knowledge, this research is the first attempt of its kind in the remanufacturing literature, as prior research treated these interrelated decisions separately. Valuable managerial insights are obtained by minimizing the discounted cash outflows caused by appropriate investment and core return inventory building decisions. We show that, under certain conditions, it may be optimal to delay the launch of the remanufacturing program to build up an adequate initial core return inventory. This may help in perfectly substituting virgin parts with remanufactured parts after end of the OE production run. At operational level, efficient production planning and control of reman parts for the supplier heavily impinges on the ability to accurately forecast core returns from customers (e.g., dealers, distributors). There are several challenges to this, including, the volume and diversity of customers served by the supplier, differences among individual customer warehouses in returning cores, large reman product catalogs, changing customer behaviors (often improving core return delays), and data sparsity. In this research we report the evidence for the effectiveness of hazard rate regression models to estimate core return delays in the context of remanufacturing. We investigate a number of hazard rate modelling techniques (e.g., parametric, semi-parametric etc.) using real-world datasets from a leading Tier-1 automotive supplier. Results indicate the effectiveness of the proposed framework in terms of stability and face validity of the estimates and in predictive accuracy

Optimal Decision Making for Capacitated Reverse Logistics Networks with Quality Variations

Increasing concerns about the environmental impact of production, product take-back laws and dwindling natural resources have heightened the need to address the impact of disposing end-of-life (EOL) products. To cope this challenge, manufacturers have integrated reverse logistics into their supply chain or chosen to outsource product recovery activities to third party firms. The uncertain quality of returns as well as uncertainty in return flow limit the effectiveness of planning, control and monitoring of reverse logistics networks. In addition, there are different recovery routes for each returned product such as reuse, repair, disassembling, remanufacturing and recycling. To determine the most profitable option for EOL product management, remanufacturers must consider the quality of returns and other limitations such as inventory size, demand and quantity of returns. The work in this dissertation addresses these pertinent aspects using two models that have been motivated by two remanufacturing facilities whereby there are uncertainties in the quality and quantity of return and capacitated inventories. In the first case, a disposition decision making model is developed for a remanufacturing process in which the inventory capacity of recoverable returns is limited and where there\u27s a constant demand to be met, for remanufactured products that meet a minimum quality threshold. It is assumed that the quality of returns is uncertain and remanufacturing cost is dependent on the quality grade. In this model, remanufacturing takes place when there is demand for remanufactured products. Accepted returns that meet the minimum quality threshold undergo the remanufacturing processes, and any unacceptable returns are salvaged. A continuous time Markov chain (CTMC) is presented as the modeling approach. The Matrix-Geometric solution methodology is applied to evaluate several key performance metrics for this system, to result in the optimal disposition policy. The numerical study shows an intricate trade-off between the acceptable quality threshold value and the recoverable product inventory capacity. Particularly, there are periodic system starvation whenever there is a mis-match between these two system metrics. In addition, the sensitivity analysis indicates that changes to the demand rate for remanufactured products necessitates the need to re-evaluate the existing system configuration. In the second case, a general framework is presented for a third party remanufacturer, where the remanufacturer has the alternative of salvaging EOL products and supplying parts to external suppliers, or remanufacture the disassembled parts to \u27as new\u27 conditions. The remanufacturing processes of reusable products and parts is studied in the context of other process variables such as the cost and demand of remanufactured products and parts. The goal of this model is to determine the return quality thresholds for a multi-product, multi-period remanufacturing setting. The problem is formulated as a mixed integer non-linear programming (MINLP) problem, which involves a discretization technique that turns the problem turns into a quadratic mixed integer programming (QMIP) problem. Finally, a numerical analysis using a personal computer (PC) remanufacturing facility data is used to test the extent to which the minimum acceptance quality threshold is dependent on the inventory level capacities of the EOL product management sites, varying operational costs and the upper bound of disposal rate

Sustainable supply chain management trends in world regions: A data-driven analysis

This study proposes a data-driven analysis that describes the overall situation and reveals the factors hindering improvement in the sustainable supply chain management field. The literature has presented a summary of the evolution of sustainable supply chain management across attributes. Prior studies have evaluated different parts of the supply chain as independent entities. An integrated systematic assessment is absent in the extant literature and makes it necessary to identify potential opportunities for research direction. A hybrid of data-driven analysis, the fuzzy Delphi method, the entropy weight method and fuzzy decision-making trial and evaluation laboratory is adopted to address uncertainty and complexity. This study contributes to locating the boundary of fundamental knowledge to advance future research and support practical execution. Valuable direction is provided by reviewing the existing literature to identify the critical indicators that need further examination. The results show that big data, closed-loop supply chains, industry 4.0, policy, remanufacturing, and supply chain network design are the most important indicators of future trends and disputes. The challenges and gaps among different geographical regions is offered that provides both a local viewpoint and a state-of-the-art advanced sustainable supply chain management assessment

Remanufacturing Process Planning Considering Quality Uncertainties, Environmental Taxes and Incentives

As environmental issues are gradually being valued by governments and societies, companies have begun to engage in economic sustainable practices such as remanufacturing, reuse and recycling, among other socially responsible practices. The broader impact of these practices enables companies to archive the goal of circular economies. Under normal circumstances, consumers’ used products have often been released into landfills, resulting in environmental pollution. This is especially so for electronic products since most materials used in their production are non-biodegradable. This research addresses the practice of remanufacturing. The remanufacturing value of the products gradually declines with the usage--also referred to as the product resident time. So the remanufacturer must decide when to acquire these end of life products from customers, to carry out remanufacturing at maximum benefits. Companies face logistical challenges in the remanufacturing process, including uncertainties in the quality and quantity of returned products, and uncertainties in the process variables including process times and resource availability. In order to maximize expected profits, we provide a decision model for finding the optimal quality threshold to accept into the system and also show the variability in the profit percentages when products are returned at various stages in their life cycle. The model also considers a system that not only remanufactures products but also salvages components and uses them in the remanufacturing process. The model also allows for purchases of new components from suppliers as needed. The model also includes environmental factors such as emissions taxes and remanufacturing government incentives. The model is applied to a case study of a real control drive remanufacturing process, with two types of products that have interchangeable key components. The results confirm that the quality threshold is indeed of significance in the process. The demand forecast for remanufactured products in the secondary market is even more significant, driving the acceptable threshold quality to as low as 0.25 on a scale of 0 (worst quality) to 1 (best quality). Lastly, the results show that the resident time (time of return after the product was first sold in the first market) also significantly impacts the profit

Uncertainty Models in Reverse Supply Chain: A Review

Reverse logistic has become an important topic for the organization due to growing environmental concern, government regulation, economic value, and sustainable competitiveness. Uncertainty is one of the key factors in the reverse supply chain that must be controlled; thus, the company could optimize the reverse supply chain function. This paper discusses progress in reverse logistic research. A total of 72 published articles were selected, analyzed, categorized and the research gaps were found among them. The study began by analyzed previous research articles in reverse logistic. In this stage, we also collected and reviewed journals discussing about the reverse supply chain. Meanwhile, the result of this stage shows that uncertainty factor has not been reviewed in detail. The most common theme as the background research in reverse logistic is environmental and economic aspect. Uncertainty in Close Loop Supply Chain is the most widely used approach, followed by the usage on reverse logistics, reverse supply chain and reverse Model. The most used approach and method on uncertainty are Mixed Integer Linear Programing, mixed integer nonlinear Programing, Robust Fuzzy Stochastic Programming, and Improved kriging-assisted robust optimization method. Customer demand, total cost, product returns are the most widely researched aspects. This paper may be useful for academicians, researchers and practitioners in learning on reverse logistic and reverse supply chain; therefore, close loop supply chain can be guidance for upcoming researches. Research opportunity based on this research combines total cost, quality return product, truck capacity, delivery route, remanufacturing capacity, and facility location got optimum function in uncertainty. The research method and approach for MINLP, IK-MRO and RSFP provide many opportunities for research. For theme and area in reverse logistic, close loop supply chain is the theme that provides the most research opportunities

Integration of mahalanobis-taguchi system and activity based costing in decision making for remanufacturing

Classifying components at the end of life (EOL) into remanufacture, repair or dispose is still a major concern to automotive industries. Prior to this study, no specific approach is reported as a guide line to determine critical crankpins that justifying economical remanufacturing process. Traditional cost accounting (TCA) has been used widely by remanufacturing industries but this is not a good measure of estimating the actual manufacturing costs per unit as compared to activity based costing (ABC). However, the application of ABC method in estimating remanufactured cost is rarely reported. These issues were handled separately without a proper integration to make remanufacturing decision which frequently results into uneconomical operating cost and finally the decision becomes less accurate. The aim of this work is to develop a suitable pattern recognition method for classifying crankshaft into three different EOL groups and subsequently evaluates the critical and non-critical crankpins of the used crankshaft using Mahalanobis-Taguchi System (MTS). A remanufacturability assessment technique was developed using Microsoft Excel spreadsheet on pattern recognition and critical crankpins evaluation, and finally integrates these information into a similar spreadsheet with ABC to make decision whether the crankshaft is to be remanufactured, repaired or disposed. The developed scatter diagram was able to recognize group pattern of EOL crankshaft which later was successfully used to determine critical crankpins required for remanufacturing process. The proposed method can serve as a useful approach to the remanufacturing industries for systematically evaluate and decide EOL components for further processing. Case study on six engine models, the result shows that three engines can be securely remanufactured at above 40% profit margin while another two engines are still viable to remanufacture but with less profit margin. In contrast, only two engines can be securely remanufactured due overcharge when using TCA. This inaccuracy affects significantly the overall remanufacturing activities and revenue of the industry. In conclusion, the proposed integration on pattern recognition, parameter evaluation and costing assists the decision making process to effectively remanufacture EOL automotive components as confirmed by Head of workshop of Motor Teknologi Industri Sdn. Bhd

Internal Supply-chain Competition In Remanufacturing: Operations Strategies, Performance And Environmental Effects

This paper investigates the competitive and environmental effects of different operations strategies of original equipment manufacturers (OEMs) and semi-independent remanufacturers, which simultaneously cooperate and compete in different stages of a closed-loop supply chain. In particular, a co-opetitive situation, in which remanufacturing is undertaken only by retailers while the OEMs' role is restricted to recycling is considered. After adopting a resource-based perspective of competition, investigations are accomplished using system dynamics simulation modelling. The results of simulations indicate that, in the long run, OEMs, regardless of the operation strategy they adopt, are unable to (re)capture the market gained by the remanufacturers. However, some of these strategies contribute to the improvement of the environmental performance of the entire supply chain

Selection of return channels and recovery options for used products

Due to legal, economic and socio-environmental factors, reverse logistics practices and extended producer responsibility have developed into a necessity in many countries. The end results and expectations may differ, but the motivation remains the same. Two significant components in a reverse logistics system -product recovery options and return channels - are the focus of this thesis. The two main issues examined are allocation of the returned products to recovery options, and selection of the collection methods for product returns. The initial segment of this thesis involves the formulation of a linear programming model to determine the optimal allocation of returned products differing in quality to specific recovery options. This model paves the way for a study on the effects of flexibility on product recovery allocation. A computational example utilising experimental data was presented to demonstrate the viability of the proposed model. The results revealed that in comparison to a fixed match between product qualities and recovery options, the product recovery operation appeared to be more profitable with a flexible allocation. The second segment of this thesis addresses the methods employed for the initial collection of returned products. A mixed integer nonlinear programming model was developed to facilitate the selection of optimal collection methods for these products. This integrated model takes three different initial collection methods into consideration. The model is used to solve an illustrative example optimally. However, as the complexity of the issue renders this process ineffective in the face of larger problems, the Lagrangian relaxation method was proposed to generate feasible solutions within reasonable computational times. This method was put to the test and the results were found to be encouraging