Identification of aftermarket and legacy parts suitable for additive manufacturing: A knowledge management-based approach

A research stream identifying aftermarket and legacy parts suitable for additive manufacturing (AM) has emerged in recent years. However, existing research reveals no golden standard for identifying suitable part candidates for AM and mainly combines preexisting methods that lack conceptual underpinnings. As a result, the identification approaches are not adjusted to organizations and are not completely operationalizable. Our first contribution is to investigate and map the existing literature from the perspective of knowledge management (KM). The second contribution is to develop and empirically investigate a combined part-identification approach in a defense sector case study. The part identification entailed an analytical hierarchy process (AHP), semi-structured interviews, and workshops. In the first run, we screened 35,000 existing aftermarket and legacy parts. Similar to previous research, the approach was not in sync with the organization. However, in contrast to previous research, we infuse part identification with KM theory by developing and testing a “Phase 0” assessment that ensures an operational fit between the approach and the organization. We tested Phase 0 and the knowledge management-based approach in a second run, which is the main contribution of this study. This paper contributes empirical research that moves beyond previous research by demonstrating how to overcome the present challenges of part identification and outlines how knowledge management-based part identification integrates with current operations and supply chains. The paper suggests avenues for future research related to AM; however, it also concerns Industry 4.0, lean improvement, and beyond, particularly from the perspective of KM.