Performance evaluation of the remanufacturing system prone to random failure and repair

Implementation of new environmental legislation and public awareness has increased the responsibility of manufacturers. Remanufacturing has been applied in many industries and sectors since its introduction. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like high failure rates of the servers, buffer capacities, and inappropriate preventive maintenance policies would be responsible for most of the delays in remanufacturing operations. In this paper, a simulation-based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line. Moreover, an estimated relationship between preventive maintenance frequency and Mean Time Between Failure (MTBF), is presented to determine the best preventive maintenance frequency. The solution approach is applied to computer remanufacturing industry. Analysis of variance (ANOVA), and regression analysis are performed to denote the most influential factors to remanufacturing cycle time (performance measures). A case study is used to show the applicability of the modelling approach in assessing and improving the cycle time, and the profit of a remanufacturing line . Managerial insights are highlighted to support managers and decision-makers in their quest for more efficient and smooth operation of the remanufacturing system

Performance Evaluation of Remanufacturing Systems

Implementation of new environmental legislation and public awareness has increased the responsibility on manufacturers. These responsibilities have forced manufacturers to begin remanufacturing and recycling of their goods after they are disposed or returned by customers. Ever since the introduction of remanufacturing, it has been applied in many industries and sectors. The remanufacturing process involves many uncertainties like time, quantity, and quality of returned products. Returned products are time sensitive products and their value drops with time. Thus, the returned products need to be remanufactured quickly to generate the maximum revenue. Every year millions of electronic products return to the manufacturer. However, only 10% to 20% of the returned products pass through the remanufacturing process, and the remaining products are disposed in the landfills. Uncertainties like failure rate of the servers, buffer capacity and inappropriate preventive maintenance policy would be highly responsible the delays in remanufacturing. In this thesis, a simulation based experimental methodology is used to determine the optimal preventive maintenance frequency and buffer allocation in a remanufacturing line, which will help to reduce the cycle time and increase the profit of the firm. Moreover, an estimated relationship between preventive maintenance frequency and MTBF (Mean Time Between Failure) is presented to determine the best preventive maintenance frequency for any industry. The solution approach is applied to a computer remanufacturing and a cell phone remanufacturing industry. Analysis of variance and regression analysis are performed to denote the influential factors in the remanufacturing line, and optimization is done by using the regression techniques and ANOVA results

A hybrid meta-heuristic approach for buffer allocation in remanufacturing environment

Remanufacturing system is complicated due to its stochastic nature. Random customer demand, return product rate and system unreliability contribute to this complexity. Remanufacturing systems with unreliable machines usually contain intermediate buffers which are used to decouple the machines, thereby, reducing mutual interference due to machine breakdowns. Intermediate buffers should be optimized to eliminate waste of resources and avoid loss of throughput. The Buffer Allocation Problem (BAP) deals with allocating optimally fixed amount of available buffers to workstations located in manufacturing or remanufacturing systems to achieve specific objectives. Optimal buffer allocation in manufacturing and remanufacturing systems not only minimizes holding cost and stock space, but also makes facilities planning and remanufacturing decisions to be effectively coordinated. BAP in a non-deterministic environment is certainly one of the most difficult optimization problems. Therefore, a mathematical framework is provided to model the dependence of throughput on buffer capacities. Obviously, based on the survey undertaken, not only there exists no algebraic relation between the objective function and buffer size but the current literature does not offer analytical results for buffer capacity design in remanufacturing environment. Decomposition principle, expansion method for evaluating system performance and an efficient hybrid Meta-heuristic search algorithm are implemented to find an optimal buffer allocation for remanufacturing system. The proposed hybrid Simulated Annealing (SA) with Genetic Algorithm (GA) is compared to pure SA and GA. The computational experiments show better quality, more accurate, efficient and reliable solutions obtained by the proposed hybrid algorithm. The improvement obtained is more than 4.18 %. Finally, the proposed method is applied on toner cartridge remanufacturing company as a case study, and the numerical results from hybrid algorithm are presented and compared with results from SA and GA

The impact of unequal processing time variability on reliable and unreliable merging line performance

Research on merging lines is expanding as their use grows significantly in the contexts of remanufacturing, reverse logistics and developing economies. This article is the first to study the behavior of unpaced, reliable, and unreliable merging assembly lines that are deliberately unbalanced with respect to their coefficients of variation (CV). Conducting a series of simulation runs with varying line lengths, buffer storage capacities and unbalanced CV patterns delivers intriguing results. For both reliable and unreliable lines, the best pattern for generating higher throughput is found to be a balanced configuration (equal CVs along both parallel lines), except for unreliable lines with a station buffer capacity of six. In that case, the highest throughput results from the descending configuration, i.e. concentrating the variable stations close to the beginning of both parallel lines and the steady stations towards the end of the line. Ordering from the least to most steady station also provides the best average buffer level. By exploring the experimental Pareto Frontier, this study shows the combined performance of unbalanced CV patterns for throughput and average buffer level. Study results suggest that caution should be exercised when assuming equivalent behavior from reliable and unreliable lines, or single serial lines and merging lines, since the relative throughput performance of some CV patterns changed between the different configurations

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

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

Inventory strategies for systems with fast remanufacturing

We describe hybrid manufacturing/remanufacturing systems with a long lead time for manufacturing and a short lead time for remanufacturing. We review the classes of inventory strategies for hybrid systems in the literature. These are all based on equal lead times. For systems with slow manufacturing and fast remanufacturing, we propose a new class. An extensive numerical experiment shows that the optimal strategy in the new class almost always performs better and often much better than the optimal strategies in all other classes

Modeling, Control and Optimisation of Hybrid Systems in a Manufacturing Setting

This study comprises a body of work that investigates the performance of hybrid manufacturing systems. And we have provided a valuable insight into the development of the optimisation techniques for hybrid manufacturing system. With the primary objective of developing prac-tical mathematical algorithms that balance trade-o? cost between product quality and completion time. For sta-bility criterion, a sliding mode control was deployed

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