Supplier selection for sustainable operations: A triple-bottom-line approach using a Bayesian framework

Joseph Sarkis*, Dileep G. Dhavale

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

222 Citations (Scopus)


In evaluating and selecting sustainable suppliers, we take a triple-bottom-line (profit, people and planet) approach and consider business operations as well as environmental impacts and social responsibilities of the suppliers. Different metrics are introduced to measure performance in these three areas. To examine the influences of different organizational and supply chain operating philosophies, the objectives in selection of suppliers are designed so that some of them favor profit or the business operations, others the planet or the environment and the remaining focusing on people or social responsibility. A novel methodological approach based on a Bayesian framework and Monte Carlo Markov Chain (MCMC) simulation is developed to rank and select suppliers using specific selection objectives. This technique is also effective when smaller or missing data sets exist, which is an especially prevalent characteristic for newer and complex measures such as in a sustainability decision environment. Results obtained from the MCMC simulation provide a wealth of information about supplier performance, which form the basis for additional statistical analyses. The model allows the decision maker to execute various scenarios by changing importance weights attached to the triple-bottom-line areas. We present results for some of those scenarios with managerial and research implications and future research directions identified.

Original languageEnglish
Peer-reviewed scientific journalInternational Journal of Production Economics
Issue numberAugust
Pages (from-to)177-191
Number of pages15
Publication statusPublished - 20.11.2014
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • Supplier selection
  • Multiple objectives
  • Triple bottom line
  • Bayesian framework
  • Markov chain Monte Carlo simulation
  • Gibbs sampler


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