Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology

Cengiz Kahraman, Ahmet Beskese*, Ihsan Kaya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

92 Citations (Scopus)


Outsourcing can be seen as a strategic way to align technology initiatives and business goals, as a strategy for managing technology operations in today's difficult business environment, and as a way to reduce operating costs. Often, companies begin the process by outsourcing non-core business operations, which may include applications, assets, people and other resources. The outsourcing decision is important since the correct selection can dramatically increase a firm's performance. When companies outsource a significant part of their business and become more dependent on outsourcers, the consequences of poor decision making becomes more severe. Since enterprise resource planning (ERP) is one of the vital systems, if not the one, that integrates all functions including planning, manufacturing, distribution, and accounting into a single system, its outsourcing is a very important multi-attribute decision problem for the firms. In the literature, outsourcing decisions are often based on multi-criteria approaches. In this paper, a fuzzy multi-criteria decision making methodology is suggested for the selection among ERP outsourcing alternatives. The methodology is based on the analytic hierarchy process (AHP) under fuzziness. It allows decision-makers to express their evaluations in linguistic expressions, crisp or fuzzy numbers. In the application of the proposed methodology, an automotive firm selects the best alternative among three ERP outsourcing firms.

Original languageEnglish
Pages (from-to)547-566
Number of pages20
JournalInternational Journal of Production Research
Issue number2
Publication statusPublished - Jan 2010


  • AHP
  • Decision making
  • ERP
  • Fuzzy
  • Outsourcing


Dive into the research topics of 'Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology'. Together they form a unique fingerprint.

Cite this