Additive Manufacturing Supply Chain Research Articles

Additive manufacturing: A framework for implementation

As mass production has migrated to developing countries, European and US companies are forced to rapidly switch towards low volume production of more innovative, customised and sustainable products with high added value. To compete in this turbulent environment, manufacturers have sought new fabrication techniques to provide the necessary tools to support the need for increased flexibility and enable economic low volume production. One such emerging technique is Additive Manufacturing (AM). AM is a method of manufacture which involves the joining of materials, usually layer-upon-layer, to create objects from 3D model data. The benefits of this methodology include new design freedom, removal of tooling requirements, and economic low volumes. AM consists of various technologies to process versatile materials, and for many years its dominant application has been the manufacture of prototypes, or Rapid Prototyping. However, the recent growth in applications for direct part manufacture, or Rapid Manufacturing, has resulted in much research effort focusing on development of new processes and materials. This study focuses on the implementation process of AM and is motivated by the lack of socio-technical studies in this area. It addresses the need for existing and potential future AM project managers to have an implementation framework to guide their efforts in adopting this new and potentially disruptive technology class to produce high value products and generate new business opportunities. Based on a review of prior works and through qualitative case study analysis, we construct and test a normative structural model of implementation factors related to AM technology, supply chain, organisation, operations and strategy.

The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (scor) model based analysis

Additive manufacturing (AM) technology has the potential to significantly improve supply chain dynamics, reduce shipping costs and shorten delivery lead times. Using AM technology, manufacturers can produce parts on demand and thus reduce the need of maintaining safety inventory. This is especially useful in the aircraft spare parts industry where currently there is a need to maintain a high level of safety inventory for high-cost long-lead time metallic parts. Therefore, more and more companies in the aerospace industry are interested in using AM technology. There are different approaches to configure the aircraft spare parts supply chain using AM technology. This paper evaluates the impact of AM in the aircraft spare parts supply chain based on the well-known supply chain operation reference model. Three supply chain scenarios are investigated; namely, conventional (as-is) supply chain, centralized AM supply chain and distributed AM supply chain. A case study is conducted based on data obtained in the literature. The result shows that the use of AM will bring various opportunities for reducing the required safety inventory of aircraft spare parts in the supply chain. A sensitivity analysis is performed and some key factors affecting the choice of AM scenarios are studied.

Performance analysis of a multi-plant specialty chemical manufacturing enterprise using an agent-based model

Modern day manufacturing enterprises consist of networks of worldwide production sites, each of which has its own supply chain. There are complex interactions between the decisions at various levels of such enterprises that lead to intricate dynamics. To make holistic decisions, it is necessary to measure and analyze performance of the enterprise and its constituents under various conditions. Such performance analysis calls for appropriate modeling and simulation tools. Agent-based modeling has been demonstrated as a promising approach for modeling such complex networks of distributed actors. In this paper, we demonstrate how an agent-based model can be developed to explicitly capture the interactions among the various constituents including the plants, functional departments, and external entities. As an illustrative case, an agent-based model of a lube additive manufacturing supply chain is introduced and the performance of the system studied under a significant range of behaviors, business policies, and environmental events.