Estimating the on-time probability for vendor selection problem

• Published in: 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) pp. 850 - 855
• Main Authors: Kumar, B. Ashish, Ramachandran, Parthasarthy, Modgil, Girish
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2016
• Subjects: Mathematical model; Ports (Computers); probabilistic models; Random variables; supply chain; Supply chains; Turbines; vendor selection
• ISSN: 2157-362X
• DOI: 10.1109/IEEM.2016.7797997

Customers expect fast delivery of products and services. Businesses understand this requirement and focus on efficient supply chains. The vendor selection process, which is complicated due a host of internal and external factors affecting the decision making, is fundamental to an efficient and responsive supply chain. As a selection criterion, the on-time probability for a vendor to supply a part can be used. In this paper, we have applied three quantitative methods, namely logistics regression, discrete time survival analysis and naïve Bayes classifier to evaluate a vendor. The mathematical models to estimate the on-time probability, were built and tested on a data set provided by a case company and evaluated with the help of key metrics.