Electronic Broker Impacts on the Value of Postponement in a Global Supply Chain

Electronic Broker Impacts on the Value of Postponement in a Global Supply Chain

William N. Robinson, Greg Elofson
Copyright: © 2001 |Pages: 15
DOI: 10.4018/jgim.2001100102
OnDemand:
(Individual Articles)
Available
$29.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $29.50

Abstract

Global and electronic markets are increasingly forcing manufacturing enterprises to become more competitive. As a result, many manufacturing enterprises are seeking to manage their supply chains more effectively. Product differentiation timing is one important factor in supply chain management. Under an early product differentiation process, finished products are manufactured and stored in a distribution center until delivery. Under a delayed product differentiation process, partially completed product components are manufactured and stored in a distribution center; later, based on demand information, finished products are completed from the product components. The difference in value between early product differentiation and delayed product differentiation is the value of postponement. Prior research has analytically shown that the value of postponement is affected by information precision in demand forecasts. In this article, we investigate whether adding a market-making electronic broker to a supply chain increases the value of postponement. We hypothesize that it may do so by providing greater accuracy in demand forecasting. We test this relationship by comparing the results of agent-based simulations that vary between early and late differentiation strategies and the use of an electronic broker. In each simulation, the effects of demand correlation, demand variability, and demand pooling are considered. The simulations show that an electronic broker increases inventory cost savings (compared to a non-broker) and increases the value of postponement in the face of increasing demand variability, increasing demand pooling, and decreasing demand correlation. Moreover, an electronic broker may, through its own actions, increase demand variability and demand pooling, while decreasing demand correlation, thereby creating the environment in which is best operates.

Complete Article List

Search this Journal:
Reset
Volume 31: 7 Issues (2023)
Volume 30: 12 Issues (2022)
Volume 29: 6 Issues (2021)
Volume 28: 4 Issues (2020)
Volume 27: 4 Issues (2019)
Volume 26: 4 Issues (2018)
Volume 25: 4 Issues (2017)
Volume 24: 4 Issues (2016)
Volume 23: 4 Issues (2015)
Volume 22: 4 Issues (2014)
Volume 21: 4 Issues (2013)
Volume 20: 4 Issues (2012)
Volume 19: 4 Issues (2011)
Volume 18: 4 Issues (2010)
Volume 17: 4 Issues (2009)
Volume 16: 4 Issues (2008)
Volume 15: 4 Issues (2007)
Volume 14: 4 Issues (2006)
Volume 13: 4 Issues (2005)
Volume 12: 4 Issues (2004)
Volume 11: 4 Issues (2003)
Volume 10: 4 Issues (2002)
Volume 9: 4 Issues (2001)
Volume 8: 4 Issues (2000)
Volume 7: 4 Issues (1999)
Volume 6: 4 Issues (1998)
Volume 5: 4 Issues (1997)
Volume 4: 4 Issues (1996)
Volume 3: 4 Issues (1995)
Volume 2: 4 Issues (1994)
Volume 1: 4 Issues (1993)
View Complete Journal Contents Listing