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Vendor performance with supply risk: A chance-constrained DEA approach [An article from: International Journal of Production Economics]
Book Details
Author(s)S. Talluri, R. Narasimhan, A. Nair
PublisherElsevier
ISBN / ASINB000RR9S4U
ISBN-13978B000RR9S43
AvailabilityAvailable for download now
Sales Rank14,383,141
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from International Journal of Production Economics, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The strategic importance of vendor evaluation is well established in the purchasing literature. Several evaluation methodologies that consider multiple performance attributes have been proposed for vendor evaluation purposes. While these techniques range from scoring models that utilize prior articulation of weights to derive composite scores for vendors to advanced mathematical models, methods that incorporate the inherent variability in vendor's performance attributes have been limited. The primary reason for the lack of development of such models is due to the complexities associated with stochastic approaches. In order to more accurately evaluate the performance of vendors, it is critical to consider variability in vendor attributes. This paper is an attempt to fill this void in vendor evaluation models by presenting a chance-constrained data envelopment analysis (CCDEA) approach in the presence of multiple performance measures that are uncertain. Our paper effectively demonstrates the first application of CCDEA in the area of purchasing, in general, and vendor evaluation, in particular. The model is demonstrated by applying it to a previously reported dataset from a pharmaceutical company.
Description:
The strategic importance of vendor evaluation is well established in the purchasing literature. Several evaluation methodologies that consider multiple performance attributes have been proposed for vendor evaluation purposes. While these techniques range from scoring models that utilize prior articulation of weights to derive composite scores for vendors to advanced mathematical models, methods that incorporate the inherent variability in vendor's performance attributes have been limited. The primary reason for the lack of development of such models is due to the complexities associated with stochastic approaches. In order to more accurately evaluate the performance of vendors, it is critical to consider variability in vendor attributes. This paper is an attempt to fill this void in vendor evaluation models by presenting a chance-constrained data envelopment analysis (CCDEA) approach in the presence of multiple performance measures that are uncertain. Our paper effectively demonstrates the first application of CCDEA in the area of purchasing, in general, and vendor evaluation, in particular. The model is demonstrated by applying it to a previously reported dataset from a pharmaceutical company.
