Reducing the cost of untimely supply chain delivery performance for asymmetric Laplace distributed delivery

Models for evaluating supply chain delivery performance commonly assume a Gaussian delivery time distribution when the true underlying delivery time probability density function (PDF) cannot be analytically determined. Recent research has identified limitations to using the Gaussian PDF and has demonstrated the advantages of using the asymmetric Laplace PDF to model a supply chain delivery time distribution. Current models which use the asymmetric Laplace to model a supply chain delivery time distribution are limited in their scope of application for modeling continuous improvement of delivery performance. In this paper we present a more robust asymmetrical Laplace delivery performance model which overcomes limitations found in the current set of models. A set of propositions for modeling delivery improvement in terms of both the mean and variance of the delivery time distribution are presented. The results of a set of 48 numerical examples conducted across a range of key input parameters are used to illustrate the delivery improvement capabilities of the model.