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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31629
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| Title: | Assessing the Difference Between Shock Sharing and Demand Sharing in
Supply Chains |
| Authors: | Kovtun, Vladimir
Giloni, Avi
Hurvich, Clifford |
| Keywords: | Supply Chain Management, Information Sharing, Time Series, ARMA,
Invertibility, QUARMA, Demand Sharing, Shock Sharing, Full Information
Shocks, Order-up-to policy. |
| Issue Date: | 3-Oct-2012 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | ;SOR-2012-03 |
| Abstract: | We consider the problem of assessing value of demand sharing in a
multi-stage supply chain in which the retailer observes stationary
autoregressive moving average demand with Gaussian white noise (shocks).
Similar to previous research, we assume each supply chain player
constructs its best linear forecast of the leadtime demand and uses it
to determine the order quantity via a periodic review myopic order-up-to
policy. We demonstrate how a typical supply chain player can determine
the extent of its available information under demand sharing by studying
the properties of the moving average polynomials of adjacent supply
chain players. Hence, we study how a player can determine its available
information under demand sharing, and use this information to forecast
leadtime demand. We characterize the value of demand sharing for a
typical supply chain player. Furthermore, we show conditions under which
(i) it is equivalent to no sharing, (ii) it is equivalent to full
information shock sharing, and (iii) it is intermediate in value to the
two previously described arrangements. We then show that demand
propagates through a supply chain where any player may share nothing,
its demand, or its full-information shocks with an adjacent upstream
player as quasi-ARMA in - quasi-ARMA out. We also provide a convenient
form for the propagation of demand in a supply chain that will lend
itself to future research applications. |
| URI: | http://hdl.handle.net/2451/31629 |
| Appears in Collections: | IOMS: Statistics Working Papers
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