Name
Abstract | ||
|---|---|---|
The effective management of a supply chain requires performance measures that accurately represent the underlying structure of the supply chain. Measures such as delivery performance to the final customer often require summing a set of random variables that capture the stochastic nature of activities across the various stages of the supply chain. The convolution calculus required to evaluate these measures is complex and often leads to intractable results. In this paper we present a discrete convolution algorithm that simplifies this evaluation. The algorithm is demonstrated for a delivery performance measure in a three stage serial supply chain. Numerical results and a supporting error analysis are presented for a set of experiments utilizing reproductive and nonreproductive probability density functions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/HICSS.2007.12 | Waikoloa, HI |
Keywords | Field | DocType |
delivery performance,effective management,supply chain delivery performance,stage serial supply chain,supply chain,intractable result,performance measure,final customer,nonreproductive probability density function,convolution algorithm,discrete convolution algorithm,delivery performance measure,random variable,random processes,convolution,supply chain management,calculus,probability density function,probability | Convolution of probability distributions,Mathematical optimization,Delivery Performance,Convolution,Convolution random number generator,Computer science,Algorithm,Stochastic process,Illustration of the central limit theorem,Supply chain management,Supply chain | Conference |
ISSN | ISBN | Citations |
1530-1605 E-ISBN : 0-7695-2755-8 | 0-7695-2755-8 | 1 |
PageRank | References | Authors |
0.40 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alfred L. Guiffrida | 1 | 88 | 10.83 |
| Robert A. Rzepka | 2 | 1 | 0.40 |
| Mohamad Y. Jaber | 3 | 236 | 26.90 |