Investigating the Use of 3D Printers as an Alternative for Automotive Spare Parts under Disruptions

3D Printing (3DP) has been considered a disruptive technology with applications across various industries. Recent studies suggest it may impact supply chain and logistics practices by enabling improved inventory management, and by shortening and reconfiguring supply chains, among other benefits. Simultaneously, recent global disruptions-such as COVID-19, the Suez Canal blockage, and ongoing geopolitical tensions resulted in heightened interest in resilience studies. These disruptions have affected most industries, including the automotive industries. While 3DP is theorized to be able to help mitigate different disruptions, there is a lack of studies that focus on investigating how disruptions affect the decision to use 3DP. This investigation thus explores and assesses the economic, logistical, and operational viability of using 3DP under disruptions. Therefore, this thesis employs qualitative and quantitative methods, providing a comprehensive approach. Three quantitative models were formulated: the first model (M1) is a cost calculation comparing 3DP and a selected traditional manufacturing method, simulating disruptions as variation in production costs. The aim of this model is to assess the influence of disruptions on production decision-making and to determine when to shift from one method to the other. The second model (M2) capitalizing on an available 3DP network, compares the utilization of this network and that of two production hubs. The objective is to evaluate where to print and when to choose from the available options. The model simulates disruptions, such as changes in customer demand size and location examined in four scenarios. These scenarios range from many customers with low individual demand to small number of customers with high individual demand. Monte Carlo simulation is applied to generate multiple iterations for statistical analysis. Finally, (M3) investigates the effect of deploying a 3DP in manufacturing operations, the aim is to assess the effect of 3DP on supply chain key performance indicators, including inventory and shortage costs, as well as demand fulfilment rates. Disruptions in this model are reflected in changes to demand and supply quantities. It is observed that the 3D printed part weight plays a decisive role in 3DP decision-making in the context of disruptions. Additionally, the dynamics and implications of change in part weight are highlighted. The lower the part weight is, the less sensitive the costs and production quantities are to economic changes. For instance, a door handle, which typically has lower weight compared to a side mirror, consistently remains cheaper to print compared to its selling price. In such cases, break-even quantities decreased by up to 15%. In contrast, the side mirror, being a heavier part, becomes less economically viable to print than to purchase, with the break-even point dropping by up to 46% compared to traditional manufacturing. Further analysis including hypothetical scenarios where 3DP costs are reduced, revealed that the largest cost reduction percentages were achieved for heavier parts. Therefore, it is recommended to prioritize cost reductions across all 3DP-related expenses, with a particular focus on heavier parts to enhance the overall cost efficiency. Regarding the decentralized 3DP vs. centralized hubs allocation decisions (M2), the analysis shows that in most cases of the four scenarios studied, a decentralized network always reduces distance travelled and associated CO2 emissions incurred across different scenarios. Nevertheless, centralized hubs may perform better in specific cases where demand is consolidated among a small number of high demand customers. Finally, in the last model, it is highlighted⎯in line with the part weight and hence print time observations⎯that 3DP can serve as a proactive resilience method for larger parts, and as a reactive method for smaller parts. This observation is extended to the implications of part weight on holding costs, shortage costs, and fulfilment rates. Smaller parts will yield higher holding costs, lower shortage costs and higher fulfilment rates.