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.
|Date of Award||14 Oct 2020|
- University Of Strathclyde
|Supervisor||Winifred Ijomah (Supervisor) & Andy TC Wong (Supervisor)|