The impact of adopting Additive Manufacturing on responsive supply chain performance

Additive Manufacturing (AM), or 3D printing as it is frequently known, is an umbrella term for a collection of manufacturing technologies that enables products to be manufactured layer-by-layer from three-dimensional digital data. While the costs associated with AM represents a barrier to its wider adoption, its benefits outweigh its costs when considered in some contexts. Few studies have investigated the costs and benefits of this technology from a supply chain perspective, particularly in market environments characterized by demand uncertainty. In this type of scenario, it becomes necessary to adopt higher levels of internal competencies, find the optimal way to manage inventories and flexibly respond to sudden market requirements. This thesis therefore aims to address this gap by examining three key aspects: the learning effects offered by AM, the impact of AM on inventory-related costs and the impact of AM on the critical capability of flexibility. To assess learning in AM, this thesis focuses on the experimental measurement of AM operator time and improvement in operator effectiveness as a result of learning. Learning is thus assessed by measuring the reduction of labour time through operator learning within a series of build repetitions and estimates a progress ratio which captures the learning effect within this series. To assess the impact of AM on inventory-related costs, this thesis develops a conceptual model that matches possible AM scenarios with demand volume level and severity of stockout penalty. It also conducts a case study to obtain insights into the resulting model which has been developed. In this case study, an interprocess comparison is undertaken by simulating a supply chain based on data collected from a plastic products manufacturing company that produces pipe fittings using Injection Moulding (IM) technology. The simulation model produced has been built using the Arena software package for three distinct scenarios: the current configuration with IM only, iii a proposed configuration with AM only, and a proposed configuration that combines AM with IM. To evaluate the impact of AM on flexibility, a conceptual model has also been constructed that maps certain AM characteristics relevant to flexibility to key market disruption scenarios faced by managers. This aspect is also highlighted through the case study which assesses the impact of AM on four distinct supply chain flexibility types: volume, delivery, mix and new product using metrics obtained from the literature. The results obtained on learning in AM suggest that AM exhibits a learning effect for both the novice and the expert operator with progress ratios of 67.73% and 80.42% respectively. Further, results on the impact of AM on inventory-related costs revealed that utilizing IM alone showed the lowest supply chain unit cost (€0.90) compared to utilizing AM as a stand-alone (€2.72) or in a combined approach (€0.94). With regards to AM’s impact on flexibility, the supply chain employing IM showed greater volume and delivery flexibility levels (i.e. 65.68% and 92.8% for IM compared to 58.70% and 75.35% for AM, respectively). However, AM showed higher mix and new product introduction flexibility level, indicated by the lower changeover time and cost of new product introduction to the system (i.e. 0.33 hrs and €0 for AM compared to 4.91 hrs and €30,000 for IM, respectively). It is anticipated that these results can be used to inform practitioners and scholars on various contexts where AM can create value and the appropriate and timely investments needed to unlock that value