Stochastic Models for Production-Inventory Planning
Author | : Ali Cheaitou |
Publisher | : |
Total Pages | : 198 |
Release | : 2008 |
ISBN-10 | : OCLC:494760438 |
ISBN-13 | : |
Rating | : 4/5 ( Downloads) |
Download or read book Stochastic Models for Production-Inventory Planning written by Ali Cheaitou and published by . This book was released on 2008 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the Supply Chain Management domain, the main source of randomness is the future demand. The influence of this demand variabilityon the performance of the Supply Chain is very important: for example, in 2007 the global inventory shortage rate in the retail industrywere around 8.3%. On the other hand, in 2003 the global Unsaleable products cost around 1% in the grocery industry. These two types ofcosts, which are mainly caused by the uncertainty of the future demand, represent important lost for the whole Supply Chain actors.This Ph.D. dissertation aims at developing mathematical production planning and inventory management models, which take intoconsideration the randomness of the future demand in order to reduce its economic negative impact, essentially for short life cycleproducts. We provide many planning models that consider the main issues of the planning problems, such as the production capacities,the information updating processes, the supply contracts and the advanced capacity reservation in a total costs minimization context. Weconsider in these models some aspects that are not considered in the literature, such as the "Payback" or the return options. Weemphasize also on an important issue that characterize the globalization of the industry, which may be resumed in the difference betweenthe procurement costs of the different suppliers. This issue is considered in the most chapters presenting models for short life cycleproducts and in the last chapter it is generalized to a long life cycle products setting.All the presented models are solved eitheranalytically or numerically using the dynamic stochastic programming.