The Role of Additive Manufacturing in Reducing Demand Volatility in Aerospace: A Conceptual Framework

Abstract

The aerospace industry faces challenges in managing inventory effectively due to long product life cycles and unpredictable demand. Additive Manufacturing (AM) is a promising technology that enables the on-demand production of spare parts, potentially reducing inventory costs and improving supply chain efficiency. This paper proposes a novel conceptual framework for employing AM in the aerospace spare parts industry to isolate demand volatility. A conceptual approach is employed in this study, which involves a comprehensive literature review to identify the factors to consider when employing AM for spare parts and the methods for demand volatility isolation, followed by a structured framework development that outlines the decision-making steps for AM utilization based on the identified factors. The framework outlines a structured approach for using AM to produce spare parts and isolate demand volatility, which can help mitigate the impact of demand uncertainty on inventory management. The proposed approach provides a basis for future research and has the potential to transform how spare parts are produced and managed in the aerospace industry. Overall, this paper contributes to the emerging literature on AM in the aerospace industry by presenting a novel approach to improving inventory management and addressing demand uncertainty.

Enhancing antioxidant delivery through 3D printing: a pathway to advanced therapeutic strategies

“…In recent years, 3D Printing has emerged as a transformative technology with vast applications in various industries including aerospace (A.A. Alogla, Alzahrani, and Alghamdi, 2023 ), medicine and pharmaceuticals ( Lin et al, 2019 ) and spare parts in general (A.A. Alogla et al, 2021 ; A; Alogla, Baumers, and Tuck, 2019 ). It enables the precise fabrication of complex three-dimensional structures with high accuracy, allowing for the customization of physical and chemical properties ( Goyanes, Wang, et al, 2015 ; A; Alogla, 2021 ).…”