To read this content please select one of the options below:

Decision support for additive manufacturing deployment in remote or austere environments

Nicholas A. Meisel (School of Engineering Design, Technology, and Professional Programs, The Pennsylvania State University, University Park, Pennsylvania, USA)
Christopher B. Williams (Department of Mechanical Engineering, Virginia Tech, Blacksburg, Virginia, USA)
Kimberly P. Ellis (Department of ISE, Virginia Tech, Blacksburg, Virginia, USA)
Don Taylor (Department of ISE, Virginia Tech, Blacksburg, Virginia, USA)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 5 September 2016




Additive manufacturing (AM) can reduce the process supply chain and encourage manufacturing innovation in remote or austere environments by producing an array of replacement/spare parts from a single raw material source. The wide variety of AM technologies, materials, and potential use cases necessitates decision support that addresses the diverse considerations of deployable manufacturing. The paper aims to discuss these issues.


Semi-structured interviews with potential users are conducted in order to establish a general deployable AM framework. This framework then forms the basis for a decision support tool to help users determine appropriate machines and materials for their desired deployable context.


User constraints are separated into process, machine, part, material, environmental, and logistical categories to form a deployable AM framework. These inform a “tiered funnel” selection tool, where each stage requires increased user knowledge of AM and the deployable context. The tool can help users narrow a database of candidate machines and materials to those appropriate for their deployable context.

Research limitations/implications

Future work will focus on expanding the environments covered by the decision support tool and expanding the user needs pool to incorporate private sector users and users less familiar with AM processes.

Practical implications

The framework in this paper can influence the growth of existing deployable manufacturing endeavors (e.g. Rapid Equipping Force Expeditionary Lab – Mobile, Army’s Mobile Parts Hospital, etc.) and considerations for future deployable AM systems.


This work represents novel research to develop both a framework for deployable AM and a user-driven decision support tool to select a process and material for the deployable context.



The authors would like to acknowledge the generous support of the LMI Research Institute’s Academic Partnership Program. The authors would also like to thank the members of LMI’s Logistics Analysis group for their guidance and expertise throughout the duration of this project. They would also like to thank the representatives who volunteered to be interviewed to assist in the development of the deployable AM framework and selection tool. Finally, the authors would like to acknowledge Katelynn Fedele and Alex Lyddane for their efforts in the coding of the final selection tool.


Meisel, N.A., Williams, C.B., Ellis, K.P. and Taylor, D. (2016), "Decision support for additive manufacturing deployment in remote or austere environments", Journal of Manufacturing Technology Management, Vol. 27 No. 7, pp. 898-914.



Emerald Group Publishing Limited

Copyright © 2016, Emerald Group Publishing Limited

Related articles