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Target setting under uncertainty

Target setting under uncertainty The main goal of this study is the definition of a key performance indicator, able to measure the impact of the demand uncertainty in a multi-stage supply chain inventory level. Despite the traditional bullwhip effect being normally analysed considering the particular dimension of the demand volatility in the amplification of upstream orders, an endogenous effect can be found and measured using a new indicator, able to help managers understanding the demand uncertainty penetration in the different stages of a supply chain. With this metric, managers are able to establish a more reliable target for their stock level, in each node, according to the risk exposure, promoting the optimisation of the invested capital in an aggregated supply chain analysis. The mathematical model is supported on the real options methodology. Overall, our investigation increases the knowledge related with demand’s uncertainty treatment in supply chain performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Business Performance and Supply Chain Modelling Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1758-9401
eISSN
1758-941X
DOI
10.1504/IJBPSCM.2011.043816
Publisher site
See Article on Publisher Site

Abstract

The main goal of this study is the definition of a key performance indicator, able to measure the impact of the demand uncertainty in a multi-stage supply chain inventory level. Despite the traditional bullwhip effect being normally analysed considering the particular dimension of the demand volatility in the amplification of upstream orders, an endogenous effect can be found and measured using a new indicator, able to help managers understanding the demand uncertainty penetration in the different stages of a supply chain. With this metric, managers are able to establish a more reliable target for their stock level, in each node, according to the risk exposure, promoting the optimisation of the invested capital in an aggregated supply chain analysis. The mathematical model is supported on the real options methodology. Overall, our investigation increases the knowledge related with demand’s uncertainty treatment in supply chain performance.

Journal

International Journal of Business Performance and Supply Chain ModellingInderscience Publishers

Published: Jan 1, 2011

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