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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31630
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| Title: | Possible Sharing Arrangements in ARMA Supply Chains |
| Authors: | Kovtun, Vladimir
Giloni, Avi
Hurvich, Clifford |
| Keywords: | Supply Chain Management, Information Sharing, Time Series, ARMA,
Invertibility, QUARMA, Demand Sharing, Full Information Shocks, Partial
Information Shocks, Order-up-to policy |
| Issue Date: | 5-Oct-2012 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | ;SOR-2012-04 |
| Abstract: | We introduce a class of new sharing arrangements 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 create a sequence of partial
information shocks (PIS) from its full information shocks FIS and share
these with an adjacent upstream player. We go on to show how such a
sharing arrangement may be benecial to the upstream player by
characterizing the player's FIS in such a case. Hence, we study how a
player can determine its available information under PIS sharing, and
use this information to forecast leadtime demand. We characterize the
value of FIS sharing for a typical supply chain player. Furthermore, we
show conditions under which a player is able to form and share valuable
PIS without (i) revealing its historic demand sequence or (ii) revealing
its FIS sequence. We also provide a way of comparing various PIS sharing
arrangements with each other and with conventional sharing arrangements
involving demand sharing or FIS sharing. We show that demand propagates
through a supply chain where any player may share nothing or a sequence
of PIS shocks with an adjacent upstream player as quasi-ARMA in -
quasi-ARMA out. |
| URI: | http://hdl.handle.net/2451/31630 |
| Appears in Collections: | IOMS: Statistics Working Papers
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