Optimising supplier selection for complex defence projects

Elias Saadeh et al.

International Journal of Procurement Management2026https://doi.org/10.1504/ijpm.2026.152256article
AJG 1
Weight
0.50

What the paper says

In the context of complex procurement processes, particularly in high-value industries such as military vessel acquisition, relying on solid decision-making strategies is critical to selecting the most suitable suppliers. Addressing the problems of linguistic variables and uncertainty requires improved multi-criteria decision-making (MCDM) approaches. This paper provides a comparative analysis of the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) and fuzzy indifference target-based attribute ratio analysis (FITARA) for group decision-making. Applying the fuzzy analytical hierarchy process (FAHP) for criteria weight aggregation, the study uncovers essential insights into the appropriate method for each military maritime vessel procurement scenario. Thus, it showcases significant differences in ranking outcomes, with FITARA displaying higher adaptability and the modified FTOPSIS maintaining stability under different settings and optimising defence acquisitions.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijpm.2026.152256

Or copy a formatted citation

@article{elias2026,
  title        = {{Optimising supplier selection for complex defence projects}},
  author       = {Elias Saadeh et al.},
  journal      = {International Journal of Procurement Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijpm.2026.152256},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Optimising supplier selection for complex defence projects

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.