An investigation of the value recovery process in the automotive remanufacturing industry : an empirical approach

Remanufacturers have been experiencing challenges when optimising the value recovery process mostly due to the uncertainties of cores regarding quality, quantity, arrival time and demand. Hence, the aim of this study is to gather relevant information from the literature and current industrial practice and then define research gaps to improve the decision-making practice for managing value recovery processes in the automotive remanufacturing industry. The case studies used in this paper are an original equipment remanufacturer and a contract remanufacturer. Both companies in the case studies use credit-based systems to take back old cores which can reduce the severity of cores’ unavailability. The ability to access the parts and specifications of the original equipment was the primary factor considered by the contract remanufacturer before deciding to remanufacture the product. In daily operations, the condition of cores was the main factors the OER and the contract remanufacturer considered to make a decision. Finally, the results of this study indicate further research areas from the intersection of industry’s needs and research gaps

Decision makings in key remanufacturing activities to optimise remanufacturing outcomes : a review

The importance of remanufacturing has been increasing since stricter regulations on protecting the environment were enforced. Remanufacturing is considered as the main means of retaining value from used products and components in order to drive a circular economy. However, it is more complex than traditional manufacturing due to the uncertainties associated with the quality, quantities and return timing of used products and components. Over the past few years, various methods of optimising remanufacturing outcomes have been developed to make decisions such as identifying the best End-Of-Life (EOL) options, acquiring the right amounts of cores, deciding the most suitable disassembly level, applying suitable cleaning techniques, and considering product commonality across different product families. A decision being made at one remanufacturing activity will greatly affect the decisions at subsequent activities, which will affect remanufacturing outcomes, i.e. productivity, economic performance effectiveness, and the proportion of core that can be salvaged. Therefore, a holistic way of integrating different decisions over multiple remanufacturing activities is needed to improve remanufacturing outcomes, which is a major knowledge gap. This paper reviews current remanufacturing practice in order to highlight both the challenges and opportunities, and more importantly, offers useful insights on how such a knowledge gap can be bridged

A holistic two-step decision-making process to optimise multiple objectives over various remanufacturing activities for automotive products

Remanufacturing is the process which used products are reworked to at least to as new condition and are given at least the same guaranty as equivalent new products. Remanufacturing is the most effective process among other recovery options because it can bring economic benefits and positive environmental impacts. Decision-making in the remanufacturing industry is more complicated than conventional manufacturing due to uncertainties of quality, quantities and return time of used components. Previous studies have developed numerous strategies for optimising remanufacturing outcomes. However, there is a lack of research to study integrated decision-making over multiple remanufacturing activities with consideration of under-studied factors. A decision made at one remanufacturing activity would significantly impact the decisions made in subsequent activities, which will affect remanufacturing outcomes. Also, tacit knowledge is not enough for making decisions since companies always have new threats or opportunities.;Therefore, this study developed a systematic and holistic way to integrate different decisions over multiple remanufacturing activities to make better decision-making and improvere manufacturing outcomes. This research studied the two-step decision-making to select the best recovery options and to find the optimal number of components/products in each remanufacturing activity. This study used case studies and mathematical modelling to enhance the ability to research various perspectives. This can lead to a higher quality of the decision model which is the research output. This first step of the decision model revealed whether additive manufacturing is a suitable recovery option in several scenarios by considering four objectives: maximising profit, minimising time, maximising recovered mass and maximising the reliability of components. This enhanced effectiveness of decision making because of the ability to assess a greater number of options properly.;This research finding will help remanufacturers to find new business opportunities by increasing the ability to recover automotive components such as crankshafts. The second step of the decision model can provide remanufacturing companies with material planning. The optimisation objectives of the model are maximising profit, minimising time or both. The findings from the sensitivity analysis contribute to the literature and real practice by quantifying and controlling the impact of component commonality on the objectives under various reworking scenarios defined by the percentage of reworked components, reworking time, and reworking cost.Remanufacturing is the process which used products are reworked to at least to as new condition and are given at least the same guaranty as equivalent new products. Remanufacturing is the most effective process among other recovery options because it can bring economic benefits and positive environmental impacts. Decision-making in the remanufacturing industry is more complicated than conventional manufacturing due to uncertainties of quality, quantities and return time of used components. Previous studies have developed numerous strategies for optimising remanufacturing outcomes. However, there is a lack of research to study integrated decision-making over multiple remanufacturing activities with consideration of under-studied factors. A decision made at one remanufacturing activity would significantly impact the decisions made in subsequent activities, which will affect remanufacturing outcomes. Also, tacit knowledge is not enough for making decisions since companies always have new threats or opportunities.;Therefore, this study developed a systematic and holistic way to integrate different decisions over multiple remanufacturing activities to make better decision-making and improvere manufacturing outcomes. This research studied the two-step decision-making to select the best recovery options and to find the optimal number of components/products in each remanufacturing activity. This study used case studies and mathematical modelling to enhance the ability to research various perspectives. This can lead to a higher quality of the decision model which is the research output. This first step of the decision model revealed whether additive manufacturing is a suitable recovery option in several scenarios by considering four objectives: maximising profit, minimising time, maximising recovered mass and maximising the reliability of components. This enhanced effectiveness of decision making because of the ability to assess a greater number of options properly.;This research finding will help remanufacturers to find new business opportunities by increasing the ability to recover automotive components such as crankshafts. The second step of the decision model can provide remanufacturing companies with material planning. The optimisation objectives of the model are maximising profit, minimising time or both. The findings from the sensitivity analysis contribute to the literature and real practice by quantifying and controlling the impact of component commonality on the objectives under various reworking scenarios defined by the percentage of reworked components, reworking time, and reworking cost

Bi-objective optimisation of remanufacturing decisions with component commonality under different reworking scenarios

Decision-making for remanufacturing is more complicated than that of traditional manufacturing because used or new components can be selected to remanufacture products. It becomes difficult to determine the various options of the components and their subsequent effect on the options at the same time. In fact, all decisions made in one remanufacturing activity will have a direct impact on the decisions taken in subsequent activities and this will affect profitability. Therefore, a decision-making model is proposed to support businesses in each remanufacturing activity by using mixed-integer linear programming to maximise profit and minimise operational time. It remains unknown whether component commonality always benefits remanufacturing. Therefore, this research contributes to the literature by quantifying and controlling the impact of component commonality on the objectives under different reworking scenarios characterised by the percentage of reworked components, reworking time, and reworking cost. When reworking costs and the percentage of reworked components are higher, the effect of component commonality on profit fluctuation increases. The remanufacturer can control these situations by reducing reworking costs, selecting specific patterns of component commonality that generate high profits or choosing the appropriate percentage of reworked components depending on various scenarios