Location, inventory and testing decisions in closed-loop supply chains: a multimedia company

Our partnering firm is a Chinese manufacturer of multimedia products that needs guidance developing its imminent Closed-Loop Supply Chain (CLSC). To study this problem, we take into account location, inventory, and testing decisions in a CLSC setting with stochastic demands of new and time-sensitive returned products. Our analysis pays particular attention to the different roles assigned to the reverse Distribution Centers (DCs) and how each option affects the optimal CLSC design. The roles considered are collection and consolidation, additional testing tasks, and direct shipments with no reverse DCs. The problem concerning our partnering firm is formulated as a scenario-based chance-constrained mixed-integer program and it is reformulated to a conic quadratic mixed-integer program that can be solved efficiently via commercial optimization packages. The completeness of the model proposed allows us to develop a decision support tool for the firm and to offer several useful managerial insights. These insights are inferred from our computational experiments using data from the Chinese firm and a second data set based on the U.S. geography. Particularly interesting insights are related to how changes in the reverse flows can impact the forward supply chain and the inventory dynamics concerning the joint DCs.This research is partially supported by the National Natural Science Foundation of China under grants 71771135, 71371106 and 71332005

A systematic review of decision-making in remanufacturing

Potential benefits have made remanufacturing attractive over the last decade. Nevertheless, the complexity and uncertainties associated with the process of managing returned products make remanufacturing challenging. Since this process involves enormous decision-making practices, various methods/techniques have been developed. This review is to specify the current challenges and opportunities for decision-making in remanufacturing. To achieve this, we perform a systematic review over decision-making in remanufacturing by classifying decisions into different managerial levels and areas. Adopting a systematic approach which provides a repeatable, transparent and scientific process, 241 key articles have been identified following a multi-stage review process. Our review indicates that most studies focuses on strategic-level(48%) and tactical-level (34%)with only 5% focusing on operational-level and the rest on two levels(13%). Regarding decision-making methods, most studies propose mathematical models (60%) followed by analytical models (31%). Furthermore, only 36% of the studies address uncertainties in which stochastic approach is mostly applied. A total of 21 knowledge gaps are highlighted to direct future research work

A Fuzzy Inference System Approach for Evaluating the Feasibility of Product Remanufacture

In the recent past, efforts have been made in enhancing sustainable manufacturing aimed at protecting the environment and saving natural resources. Among the efforts that have been explored include strategies to ensure responsible end-of-life product management so as reduce the impact on the environment and achieve effective use of resources. Towards this end, reduce, reuse and recycle product disposal strategies have found a lot of consideration in manufacturing. Of the product reuse strategies, remanufacturing has been widely applied owing to its unique feature of rendering the remanufactured product as good as new. For remanufacturers, this strategy leads to provision of quality products comparable to new their new counterparts at a reduced cost. Remanufacturing also leads to a sustainable environment through energy and material savings, as well as minimized solid wastes. Remanufacturing however, poses challenges related to collection of the returns or cores, manufacturing process planning, resource allocation, warranty estimation and redistribution. These challenges are due to product and process complexities, customer requirements, and uncertainties associated with product take back and the remanufactured products’ market-base. Key among these challenges is the remanufacturing process which is complicated, labor intensive with varying process times. In most cases the routing of these processes is stochastic in nature, based on the condition of the returned product. There is also the negative perception among consumers that remanufactured products are less superior to new ones, which calls for the need to allocate preferably longer warranty periods for the remanufactured product to induce confidence in the consumer while at the same time keeping the warranty costs low. The objectives of this study were informed by challenges faced by a local remanufacturing firm. They include: (1) a detailed study of the current remanufacturing process of the firm’s products; (2) identification of bottlenecks in the process to make recommendations for improvement; (3) develop a decision support system for assessing product remanufacture; (4) assess warranty allocation options for remanufactured product reuse. The study revealed that there are bottlenecks in the current remanufacturing process and suggested an improvement to enhance efficiency. This bottlenecks include overutilization of some of the process centers such as the diagnostic testing and the after-repair testing centers which lead to the product spending more time in the system than necessary. To improve the system performance the capacities of the bottleneck centers were increased which yielded significant reduction in the time the product spends in the system. The key contribution of this dissertation is the development of a decision support system based on a bi-level fuzzy linguistic computing approach. This model integrates qualitative and quantitative product attributes in determining the remanufacturability of a product. The fuzzy-based model established remanufacturability metric, herein referred to as an index, is applied to assess the feasibility of remanufacturing two products that were used as a case study. A number of warranty scenarios are considered to ascertain the impact of different warranty periods on the cost of warranty. The results show that the additional warranty cost for product reuse is a function of the period of first use and the residual life of the produc

Modeling a Remanufacturing Reverse Logistics Planning Problem: Some Insights into Disruptive Technology Adoption

Remanufacturing is the process to restore the functionality of high-value end-of-life (EOL) products, which is considered a substantial link in reverse logistics systems for value recovery. However, due to the uncertainty of the reverse material fow, the planning of a remanufacturing reverse logistics system is complex. Furthermore, the increasing adoption of disruptive technologies in Industry 4.0/5.0, e.g., the Internet of things (IoT), smart robots, cloud-based digital twins, and additive manufacturing, has shown great potential for a smart paradigm transition of remanufacturing reverse logistics operations. In this paper, a new mixed-integer program is modeled for supporting several tactical decisions in remanufacturing reverse logistics, i.e., remanufacturing setups, production planning and inventory levels, core acquisition and transportation, and remanufacturing line balancing and utilization. The model is further extended by incorporating utilization-dependent nonlinear idle time cost constraints and stochastic takt time to accommodate diferent real-world scenarios. Through a set of numerical experiments, the infuences of diferent demand patterns and idle time constraints are revealed. The potential impacts of disruptive technology adoption in remanufacturing reverse logistics are also discussed from managerial perspectives, which may help remanufacturing companies with a smart and smooth transition in the Industry 4.0/5.0 era

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

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

System Dynamics modelling of closed loop supply chain systems for evaluating system improvement strategies

In the past, many companies were concerned primarily with managing activities along the traditional supply chain in order to optimise operational processes and thereby economic benefits, without considering new economic and environmental opportunities in relation to the reverse supply chain and the use of used or reclaimed products. In contrast, there is now increasing interest among companies in reverse logistics and closed loop supply chain (CLSC) and their economic benefits and environmental impacts. In particular, the concept of CLSC views the reverse flow (i.e. the reverse supply chain) of reclaimed goods as integral to the forward flow (i.e. the traditional supply chain) to the consumer. At the end of the useful life of products, a reverse supply process is activated in which unwanted materials and products are recovered from end users to recapture some of their value. Therefore, planning for the forward flow of goods must take into account the recovered products. Three main processes that need to be considered are: (1) collection and distribution planning; (2) inventory control; and (3) production planning. In this thesis, our focus is the study of remanufacturing activity, which is one of the main recovery methods applied to closed loop supply chains. Specifically, we investigate and evaluate strategies for effective management concerning inventory control and production planning of a remanufacturing system. In order to pursue such a research objective, we model a production and inventory system for remanufacturing using the System Dynamics (SD) simulation modelling approach. Our primary interest is in the remanufacturing and returns processes of such a system. 3 As part of the development of the SD models, we identify the main factors, their influence relationships and the business/operational policies that affect the dynamic behaviour of the system. The returns process is modelled using significant factors which define: (1) the average period of time for which a product stays with its customer before it is returned (residence time); (2) the incentives offered by companies for the recovery of the used products (service agreement with customer); and (3) the behaviour of customers in returning used products (customer behaviour). Interestingly, combining these factors in a process model addresses the issue regarding the uncertainty in quantity and timing of returns in the reverse supply chain. To our knowledge, a returns process modelled with such factors and their influence relationships is not readily available in the literature. For the same system, the remanufacturing process is modelled using such key factors as: (1) integrated remanufacturing/production capacity, (2) lead times, (3) backorder and (4) inventory coverage. Several policies that affect the dynamic behaviour of the system are defined in the modelling process using such factors. These modelled policies are included in order to improve the efficiency of managing production/remanufacturing and inventory activities in the process. This thesis also contributes to the field through the analysis of several scenarios combining the aforementioned factors and utilising simulation in order to evaluate strategies aimed at the optimum performance of the system. The evaluation results reveal that efficiency in managing inventory can be improved by increasing the returns rate (quantity of returns), which in turn can be achieved by reducing the residence time and increasing company incentives for the recovery of used products. At the same time, the uncertainty around the returns rate is significantly diminished by increasing those incentives that encourage customers to return used products. Other findings indicate improved efficiency in the remanufacturing process with higher remanufacturing capacity if the quantity of remanufacturable returns and the remanufacturing lead time are increased and decreased, respectively. Moreover, increasing the production lead time affects system performance more than does an equivalent increase in the remanufacturing lead time. 4 Case studies are used in this thesis in order to support some of the research findings and to further validate the developed models of the production and inventory system for remanufacturing. The selection of companies employed as case studies was based on their engagement in remanufacturing and returns processes, which made them useful for our research. Specifically, data and information were collected through interviews with company management representatives of the Australian Mobile Telecommunications Association, Fuji Xerox Australia and CEVA Logistics. These three companies are significantly involved in operational and management activities linked to reverse logistics and remanufacturing processes. The knowledge gained about these companies’ activities, coupled with the data collected from the ‘real world’, were useful for the development of the models of returns and remanufacturing processes as well as for the assessment of the research findings

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