Decision support for additive manufacturing deployment in remote or austere environments

Nicholas A. Meisel, Christopher B. Williams, Kimberly P. Ellis, Don Taylor

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Purpose - 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. Design/methodology/approach - 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. Findings - 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. Originality/value - 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.

Original languageEnglish (US)
Pages (from-to)898-914
Number of pages17
JournalJournal of Manufacturing Technology Management
Issue number7
StatePublished - 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Strategy and Management
  • Industrial and Manufacturing Engineering


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